<?xml version="1.0" encoding="UTF-8"?>
<otherdata xmlns="http://linux.duke.edu/metadata/other" packages="65">
<package pkgid="4d4d8a718710cb963e34f140ba39851d7c691a1af6f0899972c3a003d779c9841d9ee26c0dc9eae324defcf640aeed34ed4baa53749ddd36ec0abafd31e224fc" name="libMIOpen1" arch="src">
  <version epoch="0" ver="7.2.1" rel="5.7"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1746100800">- Fix dir ownerships</changelog>
  <changelog author="Tim Flink &lt;tflink@fedoraproject.org&gt;" date="1746792000">- use ninja-build for epel builds</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1747051200">- Cleanup module build</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749211200">- Improving testing on suse</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753358400">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753876800">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1754654400">- Uses hip and rocrand devel at runtime.
- Cleanup dupes</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1754827200">- Fix build for older SUSE enterprise versions.
- Add calls to ldconfig for SUSE.
- Replace an unknown license string at SUSE.
- Fix buildinfo and compiler flags handling for SUSE.</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756382400">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758456000">- Update to 7.0.1
  Add: 0001-add-link-and-compile-pools-for-miopen.patch
  Remove: 0001-miopen-add-link-and-compile-pools.patch</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761998400">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1763812800">- Remove dir tags</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766491200">- Add --with compat</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1768219200">- Fix --with check</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1768564800">- Rebuild for boost 1.90</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769774400">- requires gcc-c++</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770724800">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771329600">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1774353600">- Update to 7.2.1</changelog>
</package>
<package pkgid="13424aa2696f381732f0cd66bdf13572f33c5ba1e29c86fa1233a452bdd4bad417735dc20893f09fb7e7c408b0808dccc1141e67987fe1bde5c57dd89d615f24" name="libMIOpen1" arch="x86_64">
  <version epoch="0" ver="7.2.1" rel="5.7"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1746100800">- Fix dir ownerships</changelog>
  <changelog author="Tim Flink &lt;tflink@fedoraproject.org&gt;" date="1746792000">- use ninja-build for epel builds</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1747051200">- Cleanup module build</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749211200">- Improving testing on suse</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753358400">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753876800">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1754654400">- Uses hip and rocrand devel at runtime.
- Cleanup dupes</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1754827200">- Fix build for older SUSE enterprise versions.
- Add calls to ldconfig for SUSE.
- Replace an unknown license string at SUSE.
- Fix buildinfo and compiler flags handling for SUSE.</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756382400">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758456000">- Update to 7.0.1
  Add: 0001-add-link-and-compile-pools-for-miopen.patch
  Remove: 0001-miopen-add-link-and-compile-pools.patch</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761998400">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1763812800">- Remove dir tags</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766491200">- Add --with compat</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1768219200">- Fix --with check</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1768564800">- Rebuild for boost 1.90</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769774400">- requires gcc-c++</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770724800">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771329600">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1774353600">- Update to 7.2.1</changelog>
</package>
<package pkgid="19212951449687a851d6c78fd662ef899c0583afc3d0d355a4db0589958b470b782af7e034bcf25e2520192db5e5964e55c157a53e7286c991a8446a4aa86761" name="libMIOpen1-devel" arch="x86_64">
  <version epoch="0" ver="7.2.1" rel="5.7"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1746100800">- Fix dir ownerships</changelog>
  <changelog author="Tim Flink &lt;tflink@fedoraproject.org&gt;" date="1746792000">- use ninja-build for epel builds</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1747051200">- Cleanup module build</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749211200">- Improving testing on suse</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753358400">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753876800">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1754654400">- Uses hip and rocrand devel at runtime.
- Cleanup dupes</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1754827200">- Fix build for older SUSE enterprise versions.
- Add calls to ldconfig for SUSE.
- Replace an unknown license string at SUSE.
- Fix buildinfo and compiler flags handling for SUSE.</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756382400">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758456000">- Update to 7.0.1
  Add: 0001-add-link-and-compile-pools-for-miopen.patch
  Remove: 0001-miopen-add-link-and-compile-pools.patch</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761998400">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1763812800">- Remove dir tags</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766491200">- Add --with compat</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1768219200">- Fix --with check</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1768564800">- Rebuild for boost 1.90</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769774400">- requires gcc-c++</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770724800">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771329600">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1774353600">- Update to 7.2.1</changelog>
</package>
<package pkgid="dbb24922a901cce935952c0c78dbcb2dd490433899afc80d2b7aed137c70e7e423227ac78311ee7aed482b0892deae17c68fbc2e311fa566028e1bd5281e39b0" name="librccl1" arch="src">
  <version epoch="0" ver="7.2.0" rel="7.1"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1745409600">- Fix suse</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749988800">- Remove suse check on ldconfig
  Thu May 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1750161600">- Fix builds test subpackage</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753444800">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild
  Tue Jul 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753790400">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755172800">- build --with test on SUSE
- Remove multibuild file generation</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755518400">- Fine tune parallel jobs</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756123200">- Simplify file removal</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756296000">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758369600">- Update to 7.0.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1760184000">- Update to 7.0.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761912000">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1764158400">- Update to 7.1.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766404800">- Add --with compat</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1768651200">- Rebuilt for https://fedoraproject.org/wiki/Fedora_44_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769688000">- Reduce gpu set</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770552000">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771588800">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1773316800">- Use bfd/ld from linking</changelog>
</package>
<package pkgid="49ef351754b65540dc05e09fbaefb90fc03fc0ff0b82951600906c4b64d5ab703c55aae2937260c20378bb288329dc9b3aaae9c0b9aceb00d212a1d711362996" name="librccl1" arch="x86_64">
  <version epoch="0" ver="7.2.0" rel="7.1"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1745409600">- Fix suse</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749988800">- Remove suse check on ldconfig
  Thu May 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1750161600">- Fix builds test subpackage</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753444800">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild
  Tue Jul 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753790400">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755172800">- build --with test on SUSE
- Remove multibuild file generation</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755518400">- Fine tune parallel jobs</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756123200">- Simplify file removal</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756296000">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758369600">- Update to 7.0.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1760184000">- Update to 7.0.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761912000">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1764158400">- Update to 7.1.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766404800">- Add --with compat</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1768651200">- Rebuilt for https://fedoraproject.org/wiki/Fedora_44_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769688000">- Reduce gpu set</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770552000">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771588800">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1773316800">- Use bfd/ld from linking</changelog>
</package>
<package pkgid="a47f240e0b18bb389d48e06af7c0c39fefb9168fbf61e7b8620b6f636af92147b82dc7c36da6d644613dfcf8d4d16d81888d16fd566f93b8976492bcb4c22fe8" name="librccl1-data" arch="noarch">
  <version epoch="0" ver="7.2.0" rel="7.1"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1745409600">- Fix suse</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749988800">- Remove suse check on ldconfig
  Thu May 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1750161600">- Fix builds test subpackage</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753444800">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild
  Tue Jul 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753790400">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755172800">- build --with test on SUSE
- Remove multibuild file generation</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755518400">- Fine tune parallel jobs</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756123200">- Simplify file removal</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756296000">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758369600">- Update to 7.0.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1760184000">- Update to 7.0.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761912000">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1764158400">- Update to 7.1.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766404800">- Add --with compat</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1768651200">- Rebuilt for https://fedoraproject.org/wiki/Fedora_44_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769688000">- Reduce gpu set</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770552000">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771588800">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1773316800">- Use bfd/ld from linking</changelog>
</package>
<package pkgid="26bac0c70cce08a151d1852e034c74b6de66fdb8fa8d5535eebd74887e0d6be762bbfedb319cccce4e28662181a63c7857200b199966b4041ea177334f962fbd" name="librccl1-debuginfo" arch="x86_64">
  <version epoch="0" ver="7.2.0" rel="7.1"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1745409600">- Fix suse</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749988800">- Remove suse check on ldconfig
  Thu May 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1750161600">- Fix builds test subpackage</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753444800">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild
  Tue Jul 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753790400">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755172800">- build --with test on SUSE
- Remove multibuild file generation</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755518400">- Fine tune parallel jobs</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756123200">- Simplify file removal</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756296000">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758369600">- Update to 7.0.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1760184000">- Update to 7.0.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761912000">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1764158400">- Update to 7.1.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766404800">- Add --with compat</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1768651200">- Rebuilt for https://fedoraproject.org/wiki/Fedora_44_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769688000">- Reduce gpu set</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770552000">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771588800">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1773316800">- Use bfd/ld from linking</changelog>
</package>
<package pkgid="3ebe2a4e235ac6b9b217817f6ba3014632739fdfada37d0424040b062f7e0532f8afb4f3099f2a8b74dcf8573419aab6d4ab217401d8921c07a51e833798ecbe" name="librccl1-debugsource" arch="x86_64">
  <version epoch="0" ver="7.2.0" rel="7.1"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1745409600">- Fix suse</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749988800">- Remove suse check on ldconfig
  Thu May 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1750161600">- Fix builds test subpackage</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753444800">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild
  Tue Jul 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753790400">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755172800">- build --with test on SUSE
- Remove multibuild file generation</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755518400">- Fine tune parallel jobs</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756123200">- Simplify file removal</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756296000">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758369600">- Update to 7.0.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1760184000">- Update to 7.0.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761912000">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1764158400">- Update to 7.1.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766404800">- Add --with compat</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1768651200">- Rebuilt for https://fedoraproject.org/wiki/Fedora_44_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769688000">- Reduce gpu set</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770552000">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771588800">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1773316800">- Use bfd/ld from linking</changelog>
</package>
<package pkgid="1d06f3573c827f377b3dcae570035b567427b35ab4bfac056d587a74ae2fea20a055f0fc49a8e93bba231958b4a7703dbb115f2804bdeb950aa1bb7531786672" name="librccl1-devel" arch="x86_64">
  <version epoch="0" ver="7.2.0" rel="7.1"/>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1744977600">- Update to 6.4.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1745409600">- Fix suse</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1749988800">- Remove suse check on ldconfig
  Thu May 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1750161600">- Fix builds test subpackage</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1753444800">- Rebuilt for https://fedoraproject.org/wiki/Fedora_43_Mass_Rebuild
  Tue Jul 22 00:00:00 UTC 2025 - Jeremy Newton &lt;alexjnewt at hotmail dot com&gt;
- Update to 6.4.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1753790400">- Remove -mtls-dialect cflag</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755172800">- build --with test on SUSE
- Remove multibuild file generation</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1755518400">- Fine tune parallel jobs</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756123200">- Simplify file removal</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1756296000">- Add Fedora copyright</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1758369600">- Update to 7.0.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1760184000">- Update to 7.0.2</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1761912000">- Update to 7.1.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1764158400">- Update to 7.1.1</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1766404800">- Add --with compat</changelog>
  <changelog author="Fedora Release Engineering &lt;releng@fedoraproject.org&gt;" date="1768651200">- Rebuilt for https://fedoraproject.org/wiki/Fedora_44_Mass_Rebuild</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1769688000">- Reduce gpu set</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1770552000">- Update to 7.2.0</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1771588800">- Cleanup specfile</changelog>
  <changelog author="Tom Rix &lt;Tom.Rix@amd.com&gt;" date="1773316800">- Use bfd/ld from linking</changelog>
</package>
<package pkgid="667b58820567f139e946776d1b38d30780e69e36ae51b6ca1512fef0e088ddf113a2f266e0f844fd37a188bd7cdf23ec2739c6ebe459de0d6409c9216394bd12" name="libtorch" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="e364cad39b2433fbf375acd82f51412eeeaedf66206f7337ddc91415e371678a2acc4209a52d910a34c2113386885066df141bb216d7c0f962411c96d57c7d5c" name="libtorch-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="ccecd1dd42bc8f53de4eeb1cdc09a7e1637ffe497e0fb4fa6ff58f982c7086fad8b52611782e50017f24814a34606cd0d5ddc2990c267b6e371eb64de4179580" name="libtorch-openmpi4" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="291a6faa68bd5fab4d7861d263a8f2ec9c28b06cff8eb873558fec5db6a53ed400d4117297a5bf7e4cb91b64c67071c35b9be4535ed76c55ea44efefce2105fa" name="libtorch-openmpi4-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="a92c76cc6e71a06e3ad0798e1c365d6ab69ac65e74a1b3665784cba14f5ead970581fd67817f669046a27e90ba9ced13d7016f811cb8ccc385dabf5930565f3a" name="libtorch-rocm" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="3968eef3a967b855c41daf96ec02cd6a845b37c465154da0a8f24a609a8158f6024231cf9b052a62c5a3e780262a82714f91ac496ecbbe6ddc65802a5f6026a2" name="libtorch-rocm-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="c9471de927977b6d6bd3cf8a91138f8ba13b787f2a71bfbfd76a8affc1e38f36d52f69007a21523cbe170d560a04a00e774896b7fb1d92ef52dc9221a705638f" name="ollama" arch="aarch64">
  <version epoch="0" ver="0.15.2" rel="167.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754395200">- Update to version 0.10.1:
  * No notable changes.
- Update to version 0.10.0:
  * ollama ps will now show the context length of loaded models
  * Improved performance in gemma3n models by 2-3x
  * Parallel request processing now defaults to 1
  * Fixed issue where tool calling would not work correctly with
    granite3.3 and mistral-nemo models
  * Fixed issue where Ollama's tool calling would not work
    correctly if a tool's name was part of of another one, such as
    add and get_address
  * Improved performance when using multiple GPUs by 10-30%
  * Ollama's OpenAI-compatible API will now support WebP images
  * Fixed issue where ollama show would report an error
  * ollama run will more gracefully display errors</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754481600">- Update to version 0.11.0:
  * New model: OpenAI gpt-oss 20B and 120B
  * Quantization - MXFP4 format</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754568000">- Update to version 0.11.4:
  * openai: allow for content and tool calls in the same message
  * openai: when converting role=tool messages, propagate the tool
    name
  * openai: always provide reasoning
  * Bug fixes</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1756123200">- Update to version 0.11.7:
  * DeepSeek-V3.1 is now available to run via Ollama.
  * Fixed issue where multiple models would not be loaded on
    CPU-only systems
  * Ollama will now work with models who skip outputting the
    initial &lt;think&gt; tag (e.g. DeepSeek-V3.1)
  * Fixed issue where text would be emitted when there is no
    opening &lt;think&gt; tag from a model
  * Fixed issue where tool calls containing { or } would not be
    parsed correctly
- Update to version 0.11.6:
  * Improved performance when using flash attention
  * Fixed boundary case when encoding text using BPE
- Update to version 0.11.5:
  * Performance improvements for the gpt-oss models
  * Improved memory management for scheduling models on GPUs,
    leading to better VRAM utilization, model performance and less
    out of memory errors. These new memory estimations can be
    enabled with OLLAMA_NEW_ESTIMATES=1 ollama serve and will soon
    be enabled by default.
  * Improved multi-GPU scheduling and reduced VRAM allocation when
    using more than 2 GPUs
  * Fix error when parsing bad harmony tool calls
  * OLLAMA_FLASH_ATTENTION=1 will also enable flash attention for
    pure-CPU models
  * Fixed OpenAI-compatible API not supporting reasoning_effort</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1757419200">- Update to version 0.11.10:
  * Added support for EmbeddingGemma, a new open embedding model
- Update to version 0.11.9:
  * Improved performance via overlapping GPU and CPU computations
- Update to version 0.11.8:
  * gpt-oss now has flash attention enabled by default for systems
    that support it
  * Improved load times for gpt-oss</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1759579200">- Update to version 0.12.3:
  * New models: DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905
  * Fixed issue where tool calls provided as stringified JSON
    would not be parsed correctly
  * ollama push will now provide a URL to follow to sign in
  * Fixed issues where qwen3-coder would output unicode characters
    incorrectly
  * Fix issue where loading a model with /load would crash
- Update to version 0.12.2:
  * A new web search API is now available in Ollama
  * Models with Qwen3's architecture including MoE now run in
    Ollama's new engine
  * Fixed issue where built-in tools for gpt-oss were not being
    rendered correctly
  * Support multi-regex pretokenizers in Ollama's new engine
  * Ollama's new engine can now load tensors by matching a prefix
    or suffix
- Update to version 0.12.1:
  * New model: Qwen3 Embedding: state of the art open embedding
    model by the Qwen team
  * Qwen3-Coder now supports tool calling
  * Fixed issue where Gemma3 QAT models would not output correct
    tokens
  * Fix issue where &amp; characters in Qwen3-Coder would not be parsed
    correctly when function calling
  * Fixed issues where ollama signin would not work properly
- Update to version 0.12.0:
  * Cloud models are now available in preview
  * Models with the Bert architecture now run on Ollama's engine
  * Models with the Qwen 3 architecture now run on Ollama's engine
  * Fixed issue where models would not be imported correctly with
    ollama create
  * Ollama will skip parsing the initial &lt;think&gt; if provided in
    the prompt for /api/generate
- Update to version 0.11.11:
  * Improved memory usage when using gpt-oss
  * Fixed error that would occur when attempting to import
    safetensor files
  * Improved memory estimates for hybrid and recurrent models
  * Fixed error that would occur when when batch size was greater
    than context length
  * Flash attention &amp; KV cache quantization validation fixes
  * Add dimensions field to embed requests
  * Enable new memory estimates in Ollama's new engine by default
  * Ollama will no longer load split vision models in the Ollama engine</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1760184000">- Update vendored golang.org/x/net/html to v0.45.0
  [boo#1251413] [CVE-2025-47911] [boo#1241757] [CVE-2025-22872]
- Update to version 0.12.5:
  * Fixed issue where "think": false would show an error instead of
    being silently ignored
  * Fixed deepseek-r1 output issues
- Update to version 0.12.4:
  * Flash attention is now enabled by default for Qwen 3 and Qwen 3
    Coder
  * Fixed an issue where keep_alive in the API would accept
    different values for the /api/chat and /api/generate endpoints
  * Fixed tool calling rendering with qwen3-coder
  * More reliable and accurate VRAM detection
  * OLLAMA_FLASH_ATTENTION can now be overridden to 0 for models
    that have flash attention enabled by default
  * Fixed crash where templates were not correctly defined</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1760788800">- Update vendored golang.org/x/net/html to v0.46.0
- Update to version 0.12.6
  * Experimental Vulkan support
  * Ollama's app now supports searching when running DeepSeek-V3.1,
    Qwen3 and other models that support tool calling.
  * Flash attention is now enabled by default for Gemma 3,
    improving performance and memory utilization
  * Fixed issue where Ollama would hang while generating responses
  * Fixed issue where qwen3-coder would act in raw mode when using
    /api/generate or ollama run qwen3-coder &lt;prompt&gt;
  * Fixed qwen3-embedding providing invalid results
  * Ollama will now evict models correctly when num_gpu is set
  * Fixed issue where tool_index with a value of 0 would not be
    sent to the model
- Add ollama user to render group</changelog>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1769601601">- Update to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot using
    Ollama models
- Update to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on macOS and arm64 Linux
  * Fixed issue where ollama launch would not detect claude and
    would incorrectly update opencode configurations
- Update to 0.15.0:
  * New ollama launch command for Claude Code, Codex, OpenCode,
    and Droid
  * Fixed issue where creating multi-line strings with """ would
    not work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama
    run
  * Reduced memory usage for GLM-4.7-Flash models</changelog>
</package>
<package pkgid="a161abf248880cdc9a44f7d713c1d9711f0d2d01203abadc30b278391f7702e811025ed5bc85489e506f4e88e4d8b9bcb32e4871d264f4ba77db5ed788791b64" name="ollama" arch="src">
  <version epoch="0" ver="0.15.2" rel="167.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754395200">- Update to version 0.10.1:
  * No notable changes.
- Update to version 0.10.0:
  * ollama ps will now show the context length of loaded models
  * Improved performance in gemma3n models by 2-3x
  * Parallel request processing now defaults to 1
  * Fixed issue where tool calling would not work correctly with
    granite3.3 and mistral-nemo models
  * Fixed issue where Ollama's tool calling would not work
    correctly if a tool's name was part of of another one, such as
    add and get_address
  * Improved performance when using multiple GPUs by 10-30%
  * Ollama's OpenAI-compatible API will now support WebP images
  * Fixed issue where ollama show would report an error
  * ollama run will more gracefully display errors</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754481600">- Update to version 0.11.0:
  * New model: OpenAI gpt-oss 20B and 120B
  * Quantization - MXFP4 format</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754568000">- Update to version 0.11.4:
  * openai: allow for content and tool calls in the same message
  * openai: when converting role=tool messages, propagate the tool
    name
  * openai: always provide reasoning
  * Bug fixes</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1756123200">- Update to version 0.11.7:
  * DeepSeek-V3.1 is now available to run via Ollama.
  * Fixed issue where multiple models would not be loaded on
    CPU-only systems
  * Ollama will now work with models who skip outputting the
    initial &lt;think&gt; tag (e.g. DeepSeek-V3.1)
  * Fixed issue where text would be emitted when there is no
    opening &lt;think&gt; tag from a model
  * Fixed issue where tool calls containing { or } would not be
    parsed correctly
- Update to version 0.11.6:
  * Improved performance when using flash attention
  * Fixed boundary case when encoding text using BPE
- Update to version 0.11.5:
  * Performance improvements for the gpt-oss models
  * Improved memory management for scheduling models on GPUs,
    leading to better VRAM utilization, model performance and less
    out of memory errors. These new memory estimations can be
    enabled with OLLAMA_NEW_ESTIMATES=1 ollama serve and will soon
    be enabled by default.
  * Improved multi-GPU scheduling and reduced VRAM allocation when
    using more than 2 GPUs
  * Fix error when parsing bad harmony tool calls
  * OLLAMA_FLASH_ATTENTION=1 will also enable flash attention for
    pure-CPU models
  * Fixed OpenAI-compatible API not supporting reasoning_effort</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1757419200">- Update to version 0.11.10:
  * Added support for EmbeddingGemma, a new open embedding model
- Update to version 0.11.9:
  * Improved performance via overlapping GPU and CPU computations
- Update to version 0.11.8:
  * gpt-oss now has flash attention enabled by default for systems
    that support it
  * Improved load times for gpt-oss</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1759579200">- Update to version 0.12.3:
  * New models: DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905
  * Fixed issue where tool calls provided as stringified JSON
    would not be parsed correctly
  * ollama push will now provide a URL to follow to sign in
  * Fixed issues where qwen3-coder would output unicode characters
    incorrectly
  * Fix issue where loading a model with /load would crash
- Update to version 0.12.2:
  * A new web search API is now available in Ollama
  * Models with Qwen3's architecture including MoE now run in
    Ollama's new engine
  * Fixed issue where built-in tools for gpt-oss were not being
    rendered correctly
  * Support multi-regex pretokenizers in Ollama's new engine
  * Ollama's new engine can now load tensors by matching a prefix
    or suffix
- Update to version 0.12.1:
  * New model: Qwen3 Embedding: state of the art open embedding
    model by the Qwen team
  * Qwen3-Coder now supports tool calling
  * Fixed issue where Gemma3 QAT models would not output correct
    tokens
  * Fix issue where &amp; characters in Qwen3-Coder would not be parsed
    correctly when function calling
  * Fixed issues where ollama signin would not work properly
- Update to version 0.12.0:
  * Cloud models are now available in preview
  * Models with the Bert architecture now run on Ollama's engine
  * Models with the Qwen 3 architecture now run on Ollama's engine
  * Fixed issue where models would not be imported correctly with
    ollama create
  * Ollama will skip parsing the initial &lt;think&gt; if provided in
    the prompt for /api/generate
- Update to version 0.11.11:
  * Improved memory usage when using gpt-oss
  * Fixed error that would occur when attempting to import
    safetensor files
  * Improved memory estimates for hybrid and recurrent models
  * Fixed error that would occur when when batch size was greater
    than context length
  * Flash attention &amp; KV cache quantization validation fixes
  * Add dimensions field to embed requests
  * Enable new memory estimates in Ollama's new engine by default
  * Ollama will no longer load split vision models in the Ollama engine</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1760184000">- Update vendored golang.org/x/net/html to v0.45.0
  [boo#1251413] [CVE-2025-47911] [boo#1241757] [CVE-2025-22872]
- Update to version 0.12.5:
  * Fixed issue where "think": false would show an error instead of
    being silently ignored
  * Fixed deepseek-r1 output issues
- Update to version 0.12.4:
  * Flash attention is now enabled by default for Qwen 3 and Qwen 3
    Coder
  * Fixed an issue where keep_alive in the API would accept
    different values for the /api/chat and /api/generate endpoints
  * Fixed tool calling rendering with qwen3-coder
  * More reliable and accurate VRAM detection
  * OLLAMA_FLASH_ATTENTION can now be overridden to 0 for models
    that have flash attention enabled by default
  * Fixed crash where templates were not correctly defined</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1760788800">- Update vendored golang.org/x/net/html to v0.46.0
- Update to version 0.12.6
  * Experimental Vulkan support
  * Ollama's app now supports searching when running DeepSeek-V3.1,
    Qwen3 and other models that support tool calling.
  * Flash attention is now enabled by default for Gemma 3,
    improving performance and memory utilization
  * Fixed issue where Ollama would hang while generating responses
  * Fixed issue where qwen3-coder would act in raw mode when using
    /api/generate or ollama run qwen3-coder &lt;prompt&gt;
  * Fixed qwen3-embedding providing invalid results
  * Ollama will now evict models correctly when num_gpu is set
  * Fixed issue where tool_index with a value of 0 would not be
    sent to the model
- Add ollama user to render group</changelog>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1769601601">- Update to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot using
    Ollama models
- Update to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on macOS and arm64 Linux
  * Fixed issue where ollama launch would not detect claude and
    would incorrectly update opencode configurations
- Update to 0.15.0:
  * New ollama launch command for Claude Code, Codex, OpenCode,
    and Droid
  * Fixed issue where creating multi-line strings with """ would
    not work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama
    run
  * Reduced memory usage for GLM-4.7-Flash models</changelog>
</package>
<package pkgid="bb8cb758b343cc309d5af139c4ee87d5e3178f9d8dec1516085f6f12273af4ce82264ce79ddf7c5393368238d05e8ffd9744066d0528a1ce11d7b092c1cbad29" name="ollama" arch="src">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="f002394d68b4efa30bb5398c6b753ad44929eaa04a011919354a888f34c5d6bf55b22bb68270ce4f83c0ba0d89a9ae9abd3730135f720356eef9e5941d15f51c" name="ollama" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="54dab522eec76828646168331ee585bbcd24af1efd15c35a9422a485cb9016e157240993b00d24ded2b31700c602d75a7ca4da1f673289b58ebc41caae5af526" name="ollama-debuginfo" arch="aarch64">
  <version epoch="0" ver="0.15.2" rel="167.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754395200">- Update to version 0.10.1:
  * No notable changes.
- Update to version 0.10.0:
  * ollama ps will now show the context length of loaded models
  * Improved performance in gemma3n models by 2-3x
  * Parallel request processing now defaults to 1
  * Fixed issue where tool calling would not work correctly with
    granite3.3 and mistral-nemo models
  * Fixed issue where Ollama's tool calling would not work
    correctly if a tool's name was part of of another one, such as
    add and get_address
  * Improved performance when using multiple GPUs by 10-30%
  * Ollama's OpenAI-compatible API will now support WebP images
  * Fixed issue where ollama show would report an error
  * ollama run will more gracefully display errors</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754481600">- Update to version 0.11.0:
  * New model: OpenAI gpt-oss 20B and 120B
  * Quantization - MXFP4 format</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754568000">- Update to version 0.11.4:
  * openai: allow for content and tool calls in the same message
  * openai: when converting role=tool messages, propagate the tool
    name
  * openai: always provide reasoning
  * Bug fixes</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1756123200">- Update to version 0.11.7:
  * DeepSeek-V3.1 is now available to run via Ollama.
  * Fixed issue where multiple models would not be loaded on
    CPU-only systems
  * Ollama will now work with models who skip outputting the
    initial &lt;think&gt; tag (e.g. DeepSeek-V3.1)
  * Fixed issue where text would be emitted when there is no
    opening &lt;think&gt; tag from a model
  * Fixed issue where tool calls containing { or } would not be
    parsed correctly
- Update to version 0.11.6:
  * Improved performance when using flash attention
  * Fixed boundary case when encoding text using BPE
- Update to version 0.11.5:
  * Performance improvements for the gpt-oss models
  * Improved memory management for scheduling models on GPUs,
    leading to better VRAM utilization, model performance and less
    out of memory errors. These new memory estimations can be
    enabled with OLLAMA_NEW_ESTIMATES=1 ollama serve and will soon
    be enabled by default.
  * Improved multi-GPU scheduling and reduced VRAM allocation when
    using more than 2 GPUs
  * Fix error when parsing bad harmony tool calls
  * OLLAMA_FLASH_ATTENTION=1 will also enable flash attention for
    pure-CPU models
  * Fixed OpenAI-compatible API not supporting reasoning_effort</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1757419200">- Update to version 0.11.10:
  * Added support for EmbeddingGemma, a new open embedding model
- Update to version 0.11.9:
  * Improved performance via overlapping GPU and CPU computations
- Update to version 0.11.8:
  * gpt-oss now has flash attention enabled by default for systems
    that support it
  * Improved load times for gpt-oss</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1759579200">- Update to version 0.12.3:
  * New models: DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905
  * Fixed issue where tool calls provided as stringified JSON
    would not be parsed correctly
  * ollama push will now provide a URL to follow to sign in
  * Fixed issues where qwen3-coder would output unicode characters
    incorrectly
  * Fix issue where loading a model with /load would crash
- Update to version 0.12.2:
  * A new web search API is now available in Ollama
  * Models with Qwen3's architecture including MoE now run in
    Ollama's new engine
  * Fixed issue where built-in tools for gpt-oss were not being
    rendered correctly
  * Support multi-regex pretokenizers in Ollama's new engine
  * Ollama's new engine can now load tensors by matching a prefix
    or suffix
- Update to version 0.12.1:
  * New model: Qwen3 Embedding: state of the art open embedding
    model by the Qwen team
  * Qwen3-Coder now supports tool calling
  * Fixed issue where Gemma3 QAT models would not output correct
    tokens
  * Fix issue where &amp; characters in Qwen3-Coder would not be parsed
    correctly when function calling
  * Fixed issues where ollama signin would not work properly
- Update to version 0.12.0:
  * Cloud models are now available in preview
  * Models with the Bert architecture now run on Ollama's engine
  * Models with the Qwen 3 architecture now run on Ollama's engine
  * Fixed issue where models would not be imported correctly with
    ollama create
  * Ollama will skip parsing the initial &lt;think&gt; if provided in
    the prompt for /api/generate
- Update to version 0.11.11:
  * Improved memory usage when using gpt-oss
  * Fixed error that would occur when attempting to import
    safetensor files
  * Improved memory estimates for hybrid and recurrent models
  * Fixed error that would occur when when batch size was greater
    than context length
  * Flash attention &amp; KV cache quantization validation fixes
  * Add dimensions field to embed requests
  * Enable new memory estimates in Ollama's new engine by default
  * Ollama will no longer load split vision models in the Ollama engine</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1760184000">- Update vendored golang.org/x/net/html to v0.45.0
  [boo#1251413] [CVE-2025-47911] [boo#1241757] [CVE-2025-22872]
- Update to version 0.12.5:
  * Fixed issue where "think": false would show an error instead of
    being silently ignored
  * Fixed deepseek-r1 output issues
- Update to version 0.12.4:
  * Flash attention is now enabled by default for Qwen 3 and Qwen 3
    Coder
  * Fixed an issue where keep_alive in the API would accept
    different values for the /api/chat and /api/generate endpoints
  * Fixed tool calling rendering with qwen3-coder
  * More reliable and accurate VRAM detection
  * OLLAMA_FLASH_ATTENTION can now be overridden to 0 for models
    that have flash attention enabled by default
  * Fixed crash where templates were not correctly defined</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1760788800">- Update vendored golang.org/x/net/html to v0.46.0
- Update to version 0.12.6
  * Experimental Vulkan support
  * Ollama's app now supports searching when running DeepSeek-V3.1,
    Qwen3 and other models that support tool calling.
  * Flash attention is now enabled by default for Gemma 3,
    improving performance and memory utilization
  * Fixed issue where Ollama would hang while generating responses
  * Fixed issue where qwen3-coder would act in raw mode when using
    /api/generate or ollama run qwen3-coder &lt;prompt&gt;
  * Fixed qwen3-embedding providing invalid results
  * Ollama will now evict models correctly when num_gpu is set
  * Fixed issue where tool_index with a value of 0 would not be
    sent to the model
- Add ollama user to render group</changelog>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1769601601">- Update to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot using
    Ollama models
- Update to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on macOS and arm64 Linux
  * Fixed issue where ollama launch would not detect claude and
    would incorrectly update opencode configurations
- Update to 0.15.0:
  * New ollama launch command for Claude Code, Codex, OpenCode,
    and Droid
  * Fixed issue where creating multi-line strings with """ would
    not work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama
    run
  * Reduced memory usage for GLM-4.7-Flash models</changelog>
</package>
<package pkgid="648d8332220db3a2e338a52fcb632af4f28304cd8f51a8b664a95eed6d05915b8f7d736441cfcac23cbe276fc1559d66a999333034b7b4cdb9dd931d9fa7a74f" name="ollama-debuginfo" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="a04e224049802ca9a39d3792b8786e342579ad1c07048825e9ec5d061b3b22e252a47658d563e4492fe3171183d330a4974e9c1454dfaf992b2094b3ec5e63d4" name="ollama-debugsource" arch="aarch64">
  <version epoch="0" ver="0.15.2" rel="167.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754395200">- Update to version 0.10.1:
  * No notable changes.
- Update to version 0.10.0:
  * ollama ps will now show the context length of loaded models
  * Improved performance in gemma3n models by 2-3x
  * Parallel request processing now defaults to 1
  * Fixed issue where tool calling would not work correctly with
    granite3.3 and mistral-nemo models
  * Fixed issue where Ollama's tool calling would not work
    correctly if a tool's name was part of of another one, such as
    add and get_address
  * Improved performance when using multiple GPUs by 10-30%
  * Ollama's OpenAI-compatible API will now support WebP images
  * Fixed issue where ollama show would report an error
  * ollama run will more gracefully display errors</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754481600">- Update to version 0.11.0:
  * New model: OpenAI gpt-oss 20B and 120B
  * Quantization - MXFP4 format</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754568000">- Update to version 0.11.4:
  * openai: allow for content and tool calls in the same message
  * openai: when converting role=tool messages, propagate the tool
    name
  * openai: always provide reasoning
  * Bug fixes</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1756123200">- Update to version 0.11.7:
  * DeepSeek-V3.1 is now available to run via Ollama.
  * Fixed issue where multiple models would not be loaded on
    CPU-only systems
  * Ollama will now work with models who skip outputting the
    initial &lt;think&gt; tag (e.g. DeepSeek-V3.1)
  * Fixed issue where text would be emitted when there is no
    opening &lt;think&gt; tag from a model
  * Fixed issue where tool calls containing { or } would not be
    parsed correctly
- Update to version 0.11.6:
  * Improved performance when using flash attention
  * Fixed boundary case when encoding text using BPE
- Update to version 0.11.5:
  * Performance improvements for the gpt-oss models
  * Improved memory management for scheduling models on GPUs,
    leading to better VRAM utilization, model performance and less
    out of memory errors. These new memory estimations can be
    enabled with OLLAMA_NEW_ESTIMATES=1 ollama serve and will soon
    be enabled by default.
  * Improved multi-GPU scheduling and reduced VRAM allocation when
    using more than 2 GPUs
  * Fix error when parsing bad harmony tool calls
  * OLLAMA_FLASH_ATTENTION=1 will also enable flash attention for
    pure-CPU models
  * Fixed OpenAI-compatible API not supporting reasoning_effort</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1757419200">- Update to version 0.11.10:
  * Added support for EmbeddingGemma, a new open embedding model
- Update to version 0.11.9:
  * Improved performance via overlapping GPU and CPU computations
- Update to version 0.11.8:
  * gpt-oss now has flash attention enabled by default for systems
    that support it
  * Improved load times for gpt-oss</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1759579200">- Update to version 0.12.3:
  * New models: DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905
  * Fixed issue where tool calls provided as stringified JSON
    would not be parsed correctly
  * ollama push will now provide a URL to follow to sign in
  * Fixed issues where qwen3-coder would output unicode characters
    incorrectly
  * Fix issue where loading a model with /load would crash
- Update to version 0.12.2:
  * A new web search API is now available in Ollama
  * Models with Qwen3's architecture including MoE now run in
    Ollama's new engine
  * Fixed issue where built-in tools for gpt-oss were not being
    rendered correctly
  * Support multi-regex pretokenizers in Ollama's new engine
  * Ollama's new engine can now load tensors by matching a prefix
    or suffix
- Update to version 0.12.1:
  * New model: Qwen3 Embedding: state of the art open embedding
    model by the Qwen team
  * Qwen3-Coder now supports tool calling
  * Fixed issue where Gemma3 QAT models would not output correct
    tokens
  * Fix issue where &amp; characters in Qwen3-Coder would not be parsed
    correctly when function calling
  * Fixed issues where ollama signin would not work properly
- Update to version 0.12.0:
  * Cloud models are now available in preview
  * Models with the Bert architecture now run on Ollama's engine
  * Models with the Qwen 3 architecture now run on Ollama's engine
  * Fixed issue where models would not be imported correctly with
    ollama create
  * Ollama will skip parsing the initial &lt;think&gt; if provided in
    the prompt for /api/generate
- Update to version 0.11.11:
  * Improved memory usage when using gpt-oss
  * Fixed error that would occur when attempting to import
    safetensor files
  * Improved memory estimates for hybrid and recurrent models
  * Fixed error that would occur when when batch size was greater
    than context length
  * Flash attention &amp; KV cache quantization validation fixes
  * Add dimensions field to embed requests
  * Enable new memory estimates in Ollama's new engine by default
  * Ollama will no longer load split vision models in the Ollama engine</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1760184000">- Update vendored golang.org/x/net/html to v0.45.0
  [boo#1251413] [CVE-2025-47911] [boo#1241757] [CVE-2025-22872]
- Update to version 0.12.5:
  * Fixed issue where "think": false would show an error instead of
    being silently ignored
  * Fixed deepseek-r1 output issues
- Update to version 0.12.4:
  * Flash attention is now enabled by default for Qwen 3 and Qwen 3
    Coder
  * Fixed an issue where keep_alive in the API would accept
    different values for the /api/chat and /api/generate endpoints
  * Fixed tool calling rendering with qwen3-coder
  * More reliable and accurate VRAM detection
  * OLLAMA_FLASH_ATTENTION can now be overridden to 0 for models
    that have flash attention enabled by default
  * Fixed crash where templates were not correctly defined</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1760788800">- Update vendored golang.org/x/net/html to v0.46.0
- Update to version 0.12.6
  * Experimental Vulkan support
  * Ollama's app now supports searching when running DeepSeek-V3.1,
    Qwen3 and other models that support tool calling.
  * Flash attention is now enabled by default for Gemma 3,
    improving performance and memory utilization
  * Fixed issue where Ollama would hang while generating responses
  * Fixed issue where qwen3-coder would act in raw mode when using
    /api/generate or ollama run qwen3-coder &lt;prompt&gt;
  * Fixed qwen3-embedding providing invalid results
  * Ollama will now evict models correctly when num_gpu is set
  * Fixed issue where tool_index with a value of 0 would not be
    sent to the model
- Add ollama user to render group</changelog>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1769601601">- Update to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot using
    Ollama models
- Update to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on macOS and arm64 Linux
  * Fixed issue where ollama launch would not detect claude and
    would incorrectly update opencode configurations
- Update to 0.15.0:
  * New ollama launch command for Claude Code, Codex, OpenCode,
    and Droid
  * Fixed issue where creating multi-line strings with """ would
    not work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama
    run
  * Reduced memory usage for GLM-4.7-Flash models</changelog>
</package>
<package pkgid="7ff698126243bf831313fbf732530dbcdb8bfda55a6d0e52a3360cb65a288533ea40d6003d49eb940b4941e0e19253b3ce12fab358fdfc4eeba1863c1ec366dd" name="ollama-debugsource" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="f6b63654b4d51850ce6a4ab9ebd0b6d647d46c19abee2ed1fc1c3d853bbb05a44dcef5c62835e8da805bd5acbab43e0e7c61cb3d04396abc0e5f7a18ca619f92" name="ollama-rocm" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="12280945319897993a490eaf521e15b5b748d0bf03f94ac59665bc2325e21463ed9e4e93c30b46b7afa83231ef8ed3ff853c409a9f18a68167607d0b83d7001d" name="ollama-rocm-debuginfo" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="aaba1b6fe16ce52eec7d6cc121cf1168734759a51444f3a07af06aa0a5df1f9f9eaa16c6aebaaa3ef84f24ab8c26e3d112ed978cd8c565b7ae11b2fac6f70c0d" name="ollama-vulkan" arch="aarch64">
  <version epoch="0" ver="0.15.2" rel="167.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754395200">- Update to version 0.10.1:
  * No notable changes.
- Update to version 0.10.0:
  * ollama ps will now show the context length of loaded models
  * Improved performance in gemma3n models by 2-3x
  * Parallel request processing now defaults to 1
  * Fixed issue where tool calling would not work correctly with
    granite3.3 and mistral-nemo models
  * Fixed issue where Ollama's tool calling would not work
    correctly if a tool's name was part of of another one, such as
    add and get_address
  * Improved performance when using multiple GPUs by 10-30%
  * Ollama's OpenAI-compatible API will now support WebP images
  * Fixed issue where ollama show would report an error
  * ollama run will more gracefully display errors</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754481600">- Update to version 0.11.0:
  * New model: OpenAI gpt-oss 20B and 120B
  * Quantization - MXFP4 format</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754568000">- Update to version 0.11.4:
  * openai: allow for content and tool calls in the same message
  * openai: when converting role=tool messages, propagate the tool
    name
  * openai: always provide reasoning
  * Bug fixes</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1756123200">- Update to version 0.11.7:
  * DeepSeek-V3.1 is now available to run via Ollama.
  * Fixed issue where multiple models would not be loaded on
    CPU-only systems
  * Ollama will now work with models who skip outputting the
    initial &lt;think&gt; tag (e.g. DeepSeek-V3.1)
  * Fixed issue where text would be emitted when there is no
    opening &lt;think&gt; tag from a model
  * Fixed issue where tool calls containing { or } would not be
    parsed correctly
- Update to version 0.11.6:
  * Improved performance when using flash attention
  * Fixed boundary case when encoding text using BPE
- Update to version 0.11.5:
  * Performance improvements for the gpt-oss models
  * Improved memory management for scheduling models on GPUs,
    leading to better VRAM utilization, model performance and less
    out of memory errors. These new memory estimations can be
    enabled with OLLAMA_NEW_ESTIMATES=1 ollama serve and will soon
    be enabled by default.
  * Improved multi-GPU scheduling and reduced VRAM allocation when
    using more than 2 GPUs
  * Fix error when parsing bad harmony tool calls
  * OLLAMA_FLASH_ATTENTION=1 will also enable flash attention for
    pure-CPU models
  * Fixed OpenAI-compatible API not supporting reasoning_effort</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1757419200">- Update to version 0.11.10:
  * Added support for EmbeddingGemma, a new open embedding model
- Update to version 0.11.9:
  * Improved performance via overlapping GPU and CPU computations
- Update to version 0.11.8:
  * gpt-oss now has flash attention enabled by default for systems
    that support it
  * Improved load times for gpt-oss</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1759579200">- Update to version 0.12.3:
  * New models: DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905
  * Fixed issue where tool calls provided as stringified JSON
    would not be parsed correctly
  * ollama push will now provide a URL to follow to sign in
  * Fixed issues where qwen3-coder would output unicode characters
    incorrectly
  * Fix issue where loading a model with /load would crash
- Update to version 0.12.2:
  * A new web search API is now available in Ollama
  * Models with Qwen3's architecture including MoE now run in
    Ollama's new engine
  * Fixed issue where built-in tools for gpt-oss were not being
    rendered correctly
  * Support multi-regex pretokenizers in Ollama's new engine
  * Ollama's new engine can now load tensors by matching a prefix
    or suffix
- Update to version 0.12.1:
  * New model: Qwen3 Embedding: state of the art open embedding
    model by the Qwen team
  * Qwen3-Coder now supports tool calling
  * Fixed issue where Gemma3 QAT models would not output correct
    tokens
  * Fix issue where &amp; characters in Qwen3-Coder would not be parsed
    correctly when function calling
  * Fixed issues where ollama signin would not work properly
- Update to version 0.12.0:
  * Cloud models are now available in preview
  * Models with the Bert architecture now run on Ollama's engine
  * Models with the Qwen 3 architecture now run on Ollama's engine
  * Fixed issue where models would not be imported correctly with
    ollama create
  * Ollama will skip parsing the initial &lt;think&gt; if provided in
    the prompt for /api/generate
- Update to version 0.11.11:
  * Improved memory usage when using gpt-oss
  * Fixed error that would occur when attempting to import
    safetensor files
  * Improved memory estimates for hybrid and recurrent models
  * Fixed error that would occur when when batch size was greater
    than context length
  * Flash attention &amp; KV cache quantization validation fixes
  * Add dimensions field to embed requests
  * Enable new memory estimates in Ollama's new engine by default
  * Ollama will no longer load split vision models in the Ollama engine</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1760184000">- Update vendored golang.org/x/net/html to v0.45.0
  [boo#1251413] [CVE-2025-47911] [boo#1241757] [CVE-2025-22872]
- Update to version 0.12.5:
  * Fixed issue where "think": false would show an error instead of
    being silently ignored
  * Fixed deepseek-r1 output issues
- Update to version 0.12.4:
  * Flash attention is now enabled by default for Qwen 3 and Qwen 3
    Coder
  * Fixed an issue where keep_alive in the API would accept
    different values for the /api/chat and /api/generate endpoints
  * Fixed tool calling rendering with qwen3-coder
  * More reliable and accurate VRAM detection
  * OLLAMA_FLASH_ATTENTION can now be overridden to 0 for models
    that have flash attention enabled by default
  * Fixed crash where templates were not correctly defined</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1760788800">- Update vendored golang.org/x/net/html to v0.46.0
- Update to version 0.12.6
  * Experimental Vulkan support
  * Ollama's app now supports searching when running DeepSeek-V3.1,
    Qwen3 and other models that support tool calling.
  * Flash attention is now enabled by default for Gemma 3,
    improving performance and memory utilization
  * Fixed issue where Ollama would hang while generating responses
  * Fixed issue where qwen3-coder would act in raw mode when using
    /api/generate or ollama run qwen3-coder &lt;prompt&gt;
  * Fixed qwen3-embedding providing invalid results
  * Ollama will now evict models correctly when num_gpu is set
  * Fixed issue where tool_index with a value of 0 would not be
    sent to the model
- Add ollama user to render group</changelog>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1769601601">- Update to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot using
    Ollama models
- Update to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on macOS and arm64 Linux
  * Fixed issue where ollama launch would not detect claude and
    would incorrectly update opencode configurations
- Update to 0.15.0:
  * New ollama launch command for Claude Code, Codex, OpenCode,
    and Droid
  * Fixed issue where creating multi-line strings with """ would
    not work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama
    run
  * Reduced memory usage for GLM-4.7-Flash models</changelog>
</package>
<package pkgid="18fb413de682a44e5bf5abbd6b641c7807cf9bb97bc36da75948b049e6a83d527617595edc5027693151e996814e682d28088cc12c2522623eb51c0a024cc50a" name="ollama-vulkan" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="dbbf02cb8798cc50b4c8b5d9463c63f7ac24b5d69bade587da10be03419e8fc87dfabb5712147793d5f607de02f7c7fdf0688eadbc49b432d569e05d81b684df" name="ollama-vulkan-debuginfo" arch="aarch64">
  <version epoch="0" ver="0.15.2" rel="167.1"/>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754395200">- Update to version 0.10.1:
  * No notable changes.
- Update to version 0.10.0:
  * ollama ps will now show the context length of loaded models
  * Improved performance in gemma3n models by 2-3x
  * Parallel request processing now defaults to 1
  * Fixed issue where tool calling would not work correctly with
    granite3.3 and mistral-nemo models
  * Fixed issue where Ollama's tool calling would not work
    correctly if a tool's name was part of of another one, such as
    add and get_address
  * Improved performance when using multiple GPUs by 10-30%
  * Ollama's OpenAI-compatible API will now support WebP images
  * Fixed issue where ollama show would report an error
  * ollama run will more gracefully display errors</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754481600">- Update to version 0.11.0:
  * New model: OpenAI gpt-oss 20B and 120B
  * Quantization - MXFP4 format</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1754568000">- Update to version 0.11.4:
  * openai: allow for content and tool calls in the same message
  * openai: when converting role=tool messages, propagate the tool
    name
  * openai: always provide reasoning
  * Bug fixes</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1756123200">- Update to version 0.11.7:
  * DeepSeek-V3.1 is now available to run via Ollama.
  * Fixed issue where multiple models would not be loaded on
    CPU-only systems
  * Ollama will now work with models who skip outputting the
    initial &lt;think&gt; tag (e.g. DeepSeek-V3.1)
  * Fixed issue where text would be emitted when there is no
    opening &lt;think&gt; tag from a model
  * Fixed issue where tool calls containing { or } would not be
    parsed correctly
- Update to version 0.11.6:
  * Improved performance when using flash attention
  * Fixed boundary case when encoding text using BPE
- Update to version 0.11.5:
  * Performance improvements for the gpt-oss models
  * Improved memory management for scheduling models on GPUs,
    leading to better VRAM utilization, model performance and less
    out of memory errors. These new memory estimations can be
    enabled with OLLAMA_NEW_ESTIMATES=1 ollama serve and will soon
    be enabled by default.
  * Improved multi-GPU scheduling and reduced VRAM allocation when
    using more than 2 GPUs
  * Fix error when parsing bad harmony tool calls
  * OLLAMA_FLASH_ATTENTION=1 will also enable flash attention for
    pure-CPU models
  * Fixed OpenAI-compatible API not supporting reasoning_effort</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1757419200">- Update to version 0.11.10:
  * Added support for EmbeddingGemma, a new open embedding model
- Update to version 0.11.9:
  * Improved performance via overlapping GPU and CPU computations
- Update to version 0.11.8:
  * gpt-oss now has flash attention enabled by default for systems
    that support it
  * Improved load times for gpt-oss</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1759579200">- Update to version 0.12.3:
  * New models: DeepSeek-V3.1-Terminus, Kimi K2-Instruct-0905
  * Fixed issue where tool calls provided as stringified JSON
    would not be parsed correctly
  * ollama push will now provide a URL to follow to sign in
  * Fixed issues where qwen3-coder would output unicode characters
    incorrectly
  * Fix issue where loading a model with /load would crash
- Update to version 0.12.2:
  * A new web search API is now available in Ollama
  * Models with Qwen3's architecture including MoE now run in
    Ollama's new engine
  * Fixed issue where built-in tools for gpt-oss were not being
    rendered correctly
  * Support multi-regex pretokenizers in Ollama's new engine
  * Ollama's new engine can now load tensors by matching a prefix
    or suffix
- Update to version 0.12.1:
  * New model: Qwen3 Embedding: state of the art open embedding
    model by the Qwen team
  * Qwen3-Coder now supports tool calling
  * Fixed issue where Gemma3 QAT models would not output correct
    tokens
  * Fix issue where &amp; characters in Qwen3-Coder would not be parsed
    correctly when function calling
  * Fixed issues where ollama signin would not work properly
- Update to version 0.12.0:
  * Cloud models are now available in preview
  * Models with the Bert architecture now run on Ollama's engine
  * Models with the Qwen 3 architecture now run on Ollama's engine
  * Fixed issue where models would not be imported correctly with
    ollama create
  * Ollama will skip parsing the initial &lt;think&gt; if provided in
    the prompt for /api/generate
- Update to version 0.11.11:
  * Improved memory usage when using gpt-oss
  * Fixed error that would occur when attempting to import
    safetensor files
  * Improved memory estimates for hybrid and recurrent models
  * Fixed error that would occur when when batch size was greater
    than context length
  * Flash attention &amp; KV cache quantization validation fixes
  * Add dimensions field to embed requests
  * Enable new memory estimates in Ollama's new engine by default
  * Ollama will no longer load split vision models in the Ollama engine</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1760184000">- Update vendored golang.org/x/net/html to v0.45.0
  [boo#1251413] [CVE-2025-47911] [boo#1241757] [CVE-2025-22872]
- Update to version 0.12.5:
  * Fixed issue where "think": false would show an error instead of
    being silently ignored
  * Fixed deepseek-r1 output issues
- Update to version 0.12.4:
  * Flash attention is now enabled by default for Qwen 3 and Qwen 3
    Coder
  * Fixed an issue where keep_alive in the API would accept
    different values for the /api/chat and /api/generate endpoints
  * Fixed tool calling rendering with qwen3-coder
  * More reliable and accurate VRAM detection
  * OLLAMA_FLASH_ATTENTION can now be overridden to 0 for models
    that have flash attention enabled by default
  * Fixed crash where templates were not correctly defined</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1760788800">- Update vendored golang.org/x/net/html to v0.46.0
- Update to version 0.12.6
  * Experimental Vulkan support
  * Ollama's app now supports searching when running DeepSeek-V3.1,
    Qwen3 and other models that support tool calling.
  * Flash attention is now enabled by default for Gemma 3,
    improving performance and memory utilization
  * Fixed issue where Ollama would hang while generating responses
  * Fixed issue where qwen3-coder would act in raw mode when using
    /api/generate or ollama run qwen3-coder &lt;prompt&gt;
  * Fixed qwen3-embedding providing invalid results
  * Ollama will now evict models correctly when num_gpu is set
  * Fixed issue where tool_index with a value of 0 would not be
    sent to the model
- Add ollama user to render group</changelog>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1769601601">- Update to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot using
    Ollama models
- Update to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on macOS and arm64 Linux
  * Fixed issue where ollama launch would not detect claude and
    would incorrectly update opencode configurations
- Update to 0.15.0:
  * New ollama launch command for Claude Code, Codex, OpenCode,
    and Droid
  * Fixed issue where creating multi-line strings with """ would
    not work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama
    run
  * Reduced memory usage for GLM-4.7-Flash models</changelog>
</package>
<package pkgid="5ad2bc11b7a8b62501035551decdd77a130b163b717a67b23b4a28af8a1582968ce5b5662c5ecc9e2336bfee94be6f3d04b74563059616d08a5beab73b4486d3" name="ollama-vulkan-debuginfo" arch="x86_64">
  <version epoch="0" ver="0.21.0" rel="183.1"/>
  <changelog author="Yoshio Sato &lt;vasua.ukraine@gmail.com&gt;" date="1761480000">- Require groups video and render instead of providing them while
  competing with the system-group-hardware package</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762084800">- Fixed issue with duplicated libraries (/usr/lib, /usr/lib64)
- Update to version 0.12.9
  * Fix performance regression on CPU-only systems
- Update to version 0.12.8
  * qwen3-vl performance improvements, including flash attention
    support by default
  * qwen3-vl will now output less leading whitespace in the
    response when thinking
  * Fixed issue where deepseek-v3.1 thinking could not be disabled
    in Ollama's new app
  * Fixed issue where qwen3-vl would fail to interpret images with
    transparent backgrounds
  * Ollama will now stop running a model before removing it via
    ollama rm
  * Fixed issue where prompt processing would be slower on
    Ollama's engine
- Update to version 0.12.7
  * New model: Qwen3-VL: Qwen3-VL is now available in all parameter
    sizes ranging from 2B to 235B
  * New model: MiniMax-M2: a 230 Billion parameter model built for
    coding &amp; agentic workflows available on Ollama's cloud
  * Model load failures now include more information on Windows
  * Fixed embedding results being incorrect when running
    embeddinggemma
  * Fixed gemma3n on Vulkan backend
  * Increased time allocated for ROCm to discover devices
  * Fixed truncation error when generating embeddings
  * Fixed request status code when running cloud models
  * The OpenAI-compatible /v1/embeddings endpoint now supports
    encoding_format parameter
  * Ollama will now parse tool calls that don't conform to
    {"name": name, "arguments": args} (thanks @rick-github!)
  * Fixed prompt processing reporting in the llama runner
  * Increase speed when scheduling models
  * Fixed issue where FROM &lt;model&gt; would not inherit RENDERER or
    PARSER commands</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1762516800">- Update to version 0.12.10
  * Fixed errors when running qwen3-vl:235b and
    qwen3-vl:235b-instruct
  * Enable flash attention for Vulkan (currently needs to be built
    from source)
  * Add Vulkan memory detection for Intel GPU using DXGI+PDH
  * Ollama will now return tool call IDs from the /api/chat API
  * Fixed hanging due to CPU discovery
  * Ollama will now show login instructions when switching to a
    cloud model in interactive mode
  * Fix reading stale VRAM data
  * 'ollama run' now works with embedding models</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1762776000">- Consolidate spec file to build for CPU or GPUs from NVIDIA (CUDA)
  and AMD (ROCm). Both are presently disabled on openSUSE, ROCm
  will be available on Tumbleweed soon.
- Splitting Vulkan, CUDA and ROCm into separate packages. The
  Vulkan, CUDA and ROCm modules are recommended.</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1763812800">- Update to version 0.13.0
  * New models: DeepSeek-OCR, Cogito-V2.1
  * DeepSeek-V3.1 architecture is now supported in Ollama's engine
  * Fixed performance issues that arose in Ollama 0.12.11 on CUDA
  * Fixed issue where Linux install packages were missing required
    Vulkan libraries
  * Improved CPU and memory detection while in containers/cgroups
  * Improved VRAM information detection for AMD GPUs
  * Improved KV cache performance to no longer require
    defragmentation
- Update to version 0.12.11
  * Ollama's API and the OpenAI-compatible API now supports
    Logprobs, see:
    https://cookbook.openai.com/examples/using_logprobs) and
    https://github.com/ollama/ollama/releases/tag/v0.12.11
  * Ollama's new app now supports WebP images
  * Improved rendering performance in Ollama's new app, especially
    when rendering code
  * The "required" field in tool definitions will now be omitted if
    not specified
  * Fixed issue where "tool_call_id" would be omitted when using
    the OpenAI-compatible API.
  * Fixed issue where ollama create would import data from both
    consolidated.safetensors and other safetensor files.
  * Ollama will now prefer dedicated GPUs over iGPUs when
    scheduling models
  * Vulkan can now be enabled by setting OLLAMA_VULKAN=1.
    For example: OLLAMA_VULKAN=1 ollama serve</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1764849600">- Update to version 0.13.1:
  * New models: Ministral-3, Mistral-Large-3
  * nomic-embed-text will now use Ollama's engine by default
  * Tool calling support for cogito-v2.1
  * Ollama will now better render errors instead of showing
    Unmarshal: errors</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1765972800">- Update vendored golang.org/x/net/html to v0.48.0
- Update to version 0.13.4:
  * New models: Nemotron 3 Nano, Olmo 3, Olmo 3.1
  * Enable Flash Attention automatically for models by default
  * Fixed handling of long contexts with Gemma 3 models
  * Fixed issue that would occur with Gemma 3 QAT models or
    other models imported with the Gemma 3 architecture
- Update to version 0.13.3:
  * New models: Devstral-Small-2, rnj-1, nomic-embed-text-v2
  * Improved truncation logic when using /api/embed and
    /v1/embeddings
  * Extend Gemma 3 architecture to support rnj-1 model
  * Fix error that would occur when running qwen2.5vl with image
    input
- Update to version 0.13.2:
  * New models: Qwen3-Next
  * Flash attention is now enabled by default for vision models
    such as mistral-3, gemma3, qwen3-vl and more. This improves
    memory utilization and performance when providing images as
    input.
  * Fixed GPU detection on multi-GPU CUDA machines
  * Fixed issue where deepseek-v3.1 would always think even with
    thinking is disabled in Ollama's app</changelog>
  <changelog author="Glen Masgai &lt;glen.masgai@gmail.com&gt;" date="1766145600">- Added 'Requires:' tag for subpackages to spec file
- Update to version 0.13.5:
  * New models: FunctionGemma
  * 'bert' architecture models now run on Ollama's engine
  * Added built-in renderer &amp; tool parsing capabilities for
    DeepSeek-V3.1
  * Fixed issue where nested properties in tools may not have been
    rendered properly</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768392000">- Update to version 0.14.0:
  * ollama run --experimental CLI will now open a new Ollama CLI
    that includes an agent loop and the bash tool
  * Anthropic API compatibility: support for the /v1/messages API
  * A new REQUIRES command for the Modelfile allows declaring which
    version of Ollama is required for the model
  * For older models, Ollama will avoid an integer underflow on low
    VRAM systems during memory estimation
  * More accurate VRAM measurements for AMD iGPUs
  * An error will now return when embeddings return NaN or -Inf</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1768996800">- Update to version 0.14.2:
  * New models: TranslateGemma
  * Shift + Enter will now enter a newline in Ollama's CLI
  * Improve /v1/responses API to better confirm to OpenResponses
    specification
- Update to version 0.14.1:
  * Experimental image generation models are available Linux (CUDA)
    `ollama run x/z-image-turbo`</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1769601600">- Updated to version 0.15.2:
  * New ollama launch clawdbot command for launching Clawdbot
    using Ollama models
- Updated to version 0.15.1:
  * GLM-4.7-Flash performance and correctness improvements, fixing
    repetitive answers and tool calling quality
  * Fixed performance issues on arm64
  * Fixed issue where ollama launch would not detect claude and would
    incorrectly update opencode configurations
- Updated to version 0.15.0:
  * New command: ollama launch
    A new ollama launch command to use Ollama's models with Claude
    Code, Codex, OpenCode, and Droid without separate configuration.
    New ollama launch command for Claude Code, Codex, OpenCode, and Droid
  * Fixed issue where creating multi-line strings with """ would not
    work when using ollama run
  * Ctrl+J and Shift+Enter now work for inserting newlines in ollama run
  * Reduced memory usage for GLM-4.7-Flash models
- Updated to version 0.14.3:
  Image generation:
  * Z-Image Turbo: 6 billion parameter text-to-image model from
    Alibaba’s Tongyi Lab. It generates high-quality photorealistic
    images.
  * Flux.2 Klein: Black Forest Labs’ fastest image-generation models
    to date.
  New models:
  * GLM-4.7-Flash: As the strongest model in the 30B
    class, GLM-4.7-Flash offers a new option for lightweight
    deployment that balances performance and efficiency.
  * LFM2.5-1.2B-Thinking: LFM2.5 is a new family of hybrid models
    designed for on-device deployment.
  * Fixed issue where Ollama's macOS app would interrupt system shutdown
  * Fixed ollama create and ollama show commands for experimental models
  * The /api/generate API can now be used for image generation
  * Fixed minor issues in Nemotron-3-Nano tool parsing
  * Fixed issue where removing an image generation model would cause it to first load
  * Fixed issue where ollama rm would only stop the first model in
    the list if it were running</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772280000">- Add fix-mlxrunner-tests.diff: disable compiling MLX tests
- Update to version 0.17.4:
  * New Models: Qwen 3.5, LFM 2
  * Tool call indices will now be included in parallel tool calls
- Update to version 0.17.3:
  * Fixed issue where tool calls in the Qwen 3 and Qwen 3.5 model
    families would not be parsed correctly if emitted during
    thinking
- Update to version 0.17.2:
  * No notable changes
- Update to version 0.17.1:
  * Nemotron architecture support in Ollama's engine
  * Improved LFM2 and LFM2.5 models in Ollama's engine
- Update to version 0.17.0:
  * OpenClaw can now be installed and configured automatically via
    Ollama: ollama launch openclaw
  * Improved tokenizer performance
- Update to version 0.16.3:
  * New ollama launch cline added for the Cline CLI
  * ollama launch &lt;integration&gt; will now always show the model
    picker
- Update to version 0.16.2:
  * ollama launch claude now supports searching the web when using
    :cloud models
  * New setting in Ollama's app makes it easier to disable cloud
    models for sensitive and private tasks where data cannot leave
    your computer. For Linux or when running ollama serve manually,
    set OLLAMA_NO_CLOUD=1.
  * Fixed issue where experimental image generation models would
    not run in 0.16.0 and 0.16.1
- Update to version 0.16.1:
  * Image generation models will now respect the
    OLLAMA_LOAD_TIMEOUT variable
- Update to version 0.16.0:
  * New Models: GLM-5; MiniMax-M2.5
  * New `ollama` command
  * Launch Pi with ollama launch pi
  * Ctrl+G will now allow for editing text prompts in a text
    editor when running a model
- Update to version 0.15.6:
  * Fixed context limits when running ollama launch droid
  * ollama launch will now download missing models instead of
    erroring
  * Fixed bug where ollama launch claude would cause context
    compaction when providing images
- Update to version 0.15.5:
  * New models: Qwen3-Coder-Next, GLM-OCR
  * ollama launch can now be provided arguments, for example
    ollama launch claude -- --resume
  * ollama launch will now work run subagents when using ollama
    launch claude
  * Ollama will now set context limits for a set of models when
    using ollama launch opencode
  * Sub-agent support for ollama launch for planning, deep
    research, and similar tasks
  * ollama signin will now open a browser window to make signing
    in easier
  * Ollama will now default to the following context lengths based
    on VRAM: &lt; 24 GiB VRAM: 4,096 context; 24-48 GiB VRAM: 32,768
    context; &gt;= 48 GiB VRAM: 262,144 context
  * Fixed off by one error when using num_predict in the API
  * Fixed issue where tokens from a previous sequence would be
    returned when hitting num_predict
- Update to version 0.15.4:
  * ollama launch openclaw will now enter the standard OpenClaw
    onboarding flow if this has not yet been completed.
- Update to version 0.15.3:
  * Renamed ollama launch clawdbot to ollama launch openclaw to
    reflect the project's new name
  * Improved tool calling for Ministral models
  * ollama launch will now use the value of OLLAMA_HOST when
    running it</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1772539200">- Update to version 0.17.5:
  New models
  - Qwen3.5: the small Qwen 3.5 model series is now available in
    0.8B, 2B, 4B and 9B parameter sizes.
  What's Changed
  - Fixed crash in Qwen 3.5 models when split over GPU &amp; CPU
  - Fixed issue where Qwen 3.5 models would repeat themselves due
    to no presence penalty (note: you may have to redownload the
    qwen3.5 models: ollama pull qwen3.5:35b for example)
  - ollama run --verbose will now show peak memory usage when using
    Ollama's MLX engine
  - Fixed memory issues and crashes in MLX runner
  - Fixed issue where Ollama would not be able to run models
    imported from Qwen3.5 GGUF files</changelog>
  <changelog author="Eyad Issa &lt;eyadlorenzo@gmail.com&gt;" date="1772625600">- Update to version 0.17.6:
  * Fixed issue where GLM-OCR would not work due to incorrect
    prompt rendering
  * Fixed tool calling parsing and rendering for Qwen 3.5 models</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1773316800">- Update to version 0.17.7
  * Allow thinking levels such as "medium" to correctly interpreted
    in Ollama's API for all thinking models
  * Add context length to support compaction when using ollama launch</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1774958400">- Update to version 0.19.0
  * Ollama's app will now no longer incorrectly show "model is out of date"
  * ollama launch pi now includes web search plugin that uses Ollama's web search
  * Improved KV cache hit rate when using the Anthropic-compatible API
  * Fixed tool call parsing issue with Qwen3.5 where tool calls would be output in thinking
  * MLX runner will now create periodic snapshots during prompt processing
  * Fixed KV cache snapshot memory leak in MLX runner
  * Fixed issue where flash attention would be incorrectly enabled for grok models
  * Fixed qwen3-next:80b not loading in Ollama
- Important 0.18.x updates
  * Improved OpenClaw performance with Kimi-K2.5
  * Nemotron-3-Super support
  * openclaw support
  * ROCm 7 support and requirement</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1775908800">- Update to versoin 0.20.5
  * Gemma 4 support
  * Fixes for ROCm
  * openClaw extensions</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776254400">- Update to version 0.20.7
  * Fix quality of gemma:e2b and gemma:e4b when thinking is disabled
  * ROCm: Update to ROCm 7.2.1 on Linux by @saman-amd
  * Gemma 4 tool calling ability is improved and updated to use Google's latest post-launch fixes
  * Parallel tool calling improved for streaming responses
  * Hermes agent Ollama integration guide is now available
  * Ollama app is updated to fix image attachment errors</changelog>
  <changelog author="Egbert Eich &lt;eich@suse.com&gt;" date="1776340800">- Add HIP compiler flag `--offload-compress` for builds with ROCm
  support. This will allow the package to build against ROCm 7.2
  from science:GPU:ROCm.</changelog>
  <changelog author="Adrian Schr&#xF6;ter &lt;adrian@suse.de&gt;" date="1776427200">- Update to version 0.21.0
  * launch: skip unchanged integration rewrite configration
  * launch/openclaw: fix --yes flag behaviour to skip channels configuration
  * launch: OpenCode inline config
  * launch: add hermes by
  * launch: always list cloud recommendations first
  * cmd/launch: add Copilot CLI integration
- fixing vulkan dependencies, should also fix runtime
  (so far ollama claimed to use vulkan, but actually didn't)
- check for missing symbols in cuda and vulkan backend if available</changelog>
</package>
<package pkgid="681aca5425c89782c88c9729455dac7944ee6c6d2bbce03bc10e4e08faa665876ea0e6f7f4cf5355e681b938e4902e15cd842573f8021a38fb64d860955a315e" name="python-torch" arch="src">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643200">- Make sure to pull py2/py3 package from the devel pkg</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913600">- updated to bugfix release 1.4.1 and added _multibuild file so
  that cuda versions can be build on commandline</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913601">- updated to new stable release 1.5.1 which has following changes:
  This release includes several major new API additions and improvements. These
  include new APIs for autograd allowing for easy computation of hessians and
  jacobians, a significant update to the C++ frontend, ‘channels last’ memory
  format for more performant computer vision models, a stable release of the
  distributed RPC framework used for model parallel training, and a new API
  that allows for the creation of Custom C++ Classes that was inspired by
  PyBind. Additionally torch_xla 1.5 is now available and tested with the
  PyTorch 1.5 release providing a mature Cloud TPU experience.
  * see release.html for detailed information
- added patches:
  * fix-call-of-onnxInitGraph.patch for API mismatch in onnx
  * fix-mov-operand-for-gcc.patch for aarch64 operands
- removed sources:
  * cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
  * gloo-7c541247a6fa49e5938e304ab93b6da661823d0f.tar.gz
  * onnx-fea8568cac61a482ed208748fdc0e1a8e47f62f5.tar.gz
  * psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
  * pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
- added sources:
  * cpuinfo-0e6bde92b343c5fbcfe34ecd41abf9515d54b4a7.tar.gz
  * gloo-113bde13035594cafdca247be953610b53026553.tar.gz
  * onnx-9fdae4c68960a2d44cd1cc871c74a6a9d469fa1f.tar.gz
  * psimd-10b4ffc6ea9e2e11668f86969586f88bc82aaefa.tar.gz
  * pthreadpool-d465747660ecf9ebbaddf8c3db37e4a13d0c9103.tar.gz</changelog>
  <changelog author="Martin Li&#x161;ka &lt;mliska@suse.cz&gt;" date="1597924800">- Use memoryperjob constraint instead of %limit_build macro.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602072000">- Use GCC9 to build on aarch64 Tumbleweed to workaround SVE
  problem with GCC10 with sleef, see:
  https://github.com/pytorch/pytorch/issues/45971</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Matej Cepl &lt;mcepl@suse.com&gt;" date="1619179200">- Don't build python36-* package (missing pandas)</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update sleef to fix build on aarch64</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Add _service file to ease future update of deps</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955202">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955203">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="ecab5fb3ae7d8d93e23081384b5bfde770bc71c77b071635493c6dea896da856c34afc73fc92066b3523dc927304f99021b2e12d2a6c4dc1df08d225ca6da7f3" name="python-torch-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="8dcc38371b9b6e75219e71ffbc1c9089c958574a62f40da2327ab73774e5c3a031a657387e2b5fa803b5e550c827e17ded0168a5c7add58f921850f98ec6de60" name="python-torch-debugsource" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="104e0bb5146825cef95cfb0bb8ca0ad4a831b0afc1c6b999408a52f176d4ceef9116b9c4f7ce5b57289f639ca2c820b0cce29bc661df58ca32ffe366b13f768c" name="python-torch-openmpi4" arch="src">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643200">- Make sure to pull py2/py3 package from the devel pkg</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913600">- updated to bugfix release 1.4.1 and added _multibuild file so
  that cuda versions can be build on commandline</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913601">- updated to new stable release 1.5.1 which has following changes:
  This release includes several major new API additions and improvements. These
  include new APIs for autograd allowing for easy computation of hessians and
  jacobians, a significant update to the C++ frontend, ‘channels last’ memory
  format for more performant computer vision models, a stable release of the
  distributed RPC framework used for model parallel training, and a new API
  that allows for the creation of Custom C++ Classes that was inspired by
  PyBind. Additionally torch_xla 1.5 is now available and tested with the
  PyTorch 1.5 release providing a mature Cloud TPU experience.
  * see release.html for detailed information
- added patches:
  * fix-call-of-onnxInitGraph.patch for API mismatch in onnx
  * fix-mov-operand-for-gcc.patch for aarch64 operands
- removed sources:
  * cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
  * gloo-7c541247a6fa49e5938e304ab93b6da661823d0f.tar.gz
  * onnx-fea8568cac61a482ed208748fdc0e1a8e47f62f5.tar.gz
  * psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
  * pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
- added sources:
  * cpuinfo-0e6bde92b343c5fbcfe34ecd41abf9515d54b4a7.tar.gz
  * gloo-113bde13035594cafdca247be953610b53026553.tar.gz
  * onnx-9fdae4c68960a2d44cd1cc871c74a6a9d469fa1f.tar.gz
  * psimd-10b4ffc6ea9e2e11668f86969586f88bc82aaefa.tar.gz
  * pthreadpool-d465747660ecf9ebbaddf8c3db37e4a13d0c9103.tar.gz</changelog>
  <changelog author="Martin Li&#x161;ka &lt;mliska@suse.cz&gt;" date="1597924800">- Use memoryperjob constraint instead of %limit_build macro.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602072000">- Use GCC9 to build on aarch64 Tumbleweed to workaround SVE
  problem with GCC10 with sleef, see:
  https://github.com/pytorch/pytorch/issues/45971</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Matej Cepl &lt;mcepl@suse.com&gt;" date="1619179200">- Don't build python36-* package (missing pandas)</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update sleef to fix build on aarch64</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Add _service file to ease future update of deps</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955202">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955203">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="6ae713a9c8da5900c1124d64ad4103dbfaf59d3fa41b2fc2e6f6c4c31409259f4bd3907a44a34600a910fa7d7cd6ff2db66f2c51bc85738e4516dff8b5c3d25d" name="python-torch-openmpi4-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="c9fb838030e6e83df59c71b3d2d847c4e6147a58119643913346123f4e0b55624f6c77cd529294f1b67c3b34ad0cf702105e90f213d008b42d135f5ca416f9f0" name="python-torch-openmpi4-debugsource" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="6d9ee8cec58a631f0796cff4201f5d7961ccf57f92aa6f1f252e3b4382a5b1d0138e2a08b7769bd0e2849517bd919d3998b0140049e03512b88861c47b5c2260" name="python-torch-rocm" arch="src">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Tom&#xE1;&#x161; Chv&#xE1;tal &lt;tchvatal@suse.com&gt;" date="1587643200">- Make sure to pull py2/py3 package from the devel pkg</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913600">- updated to bugfix release 1.4.1 and added _multibuild file so
  that cuda versions can be build on commandline</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1592913601">- updated to new stable release 1.5.1 which has following changes:
  This release includes several major new API additions and improvements. These
  include new APIs for autograd allowing for easy computation of hessians and
  jacobians, a significant update to the C++ frontend, ‘channels last’ memory
  format for more performant computer vision models, a stable release of the
  distributed RPC framework used for model parallel training, and a new API
  that allows for the creation of Custom C++ Classes that was inspired by
  PyBind. Additionally torch_xla 1.5 is now available and tested with the
  PyTorch 1.5 release providing a mature Cloud TPU experience.
  * see release.html for detailed information
- added patches:
  * fix-call-of-onnxInitGraph.patch for API mismatch in onnx
  * fix-mov-operand-for-gcc.patch for aarch64 operands
- removed sources:
  * cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
  * gloo-7c541247a6fa49e5938e304ab93b6da661823d0f.tar.gz
  * onnx-fea8568cac61a482ed208748fdc0e1a8e47f62f5.tar.gz
  * psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
  * pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
- added sources:
  * cpuinfo-0e6bde92b343c5fbcfe34ecd41abf9515d54b4a7.tar.gz
  * gloo-113bde13035594cafdca247be953610b53026553.tar.gz
  * onnx-9fdae4c68960a2d44cd1cc871c74a6a9d469fa1f.tar.gz
  * psimd-10b4ffc6ea9e2e11668f86969586f88bc82aaefa.tar.gz
  * pthreadpool-d465747660ecf9ebbaddf8c3db37e4a13d0c9103.tar.gz</changelog>
  <changelog author="Martin Li&#x161;ka &lt;mliska@suse.cz&gt;" date="1597924800">- Use memoryperjob constraint instead of %limit_build macro.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1602072000">- Use GCC9 to build on aarch64 Tumbleweed to workaround SVE
  problem with GCC10 with sleef, see:
  https://github.com/pytorch/pytorch/issues/45971</changelog>
  <changelog author="Benjamin Greiner &lt;code@bnavigator.de&gt;" date="1611230400">- Fix python-rpm-macros usage</changelog>
  <changelog author="Matej Cepl &lt;mcepl@suse.com&gt;" date="1619179200">- Don't build python36-* package (missing pandas)</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Update sleef to fix build on aarch64</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955201">- Add _service file to ease future update of deps</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955202">- Update to 1.9.0
- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
- Drop upstreamed patch:
  * fix-mov-operand-for-gcc.patch
- Drop unneeded patches:
  * removed-peachpy-depedency.patch
- Refresh patches:
  * skip-third-party-check.patch
  * fix-call-of-onnxInitGraph.patch
- Add new patch:
  * pytorch-rm-some-gitmodules.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955203">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="001d2b65348471bf4836a6e20136bd38532aa85072e525e749adc275321a87c86a061843ebcb35bf3f8be0b5d99ca9ac363b618f58e6af292be2bcec103e232e" name="python-torch-rocm-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="0878db630d7e3a37966288c228a3b9dc5c6c5e315e0628616a0312012c6e2435ac8e5f3c70cc36d5c68b4ddf9e2fa6a7495c57dc56353612b749f2958e946ecc" name="python-torch-rocm-debugsource" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="899f460a71de9083cc8641302b12a7cffb2306e00e1fc85c3518df860a1a68b55fe8aeea0aab202e13c6055a7fd3c59fdf8edf3d4a9c4ced365cc87822dc60e5" name="python311-torch" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="a05f2b5230bfb296d77cbaa238e9afcaa80fe41ce84852e2efbcd7d2d5e225c66a722b62c2c90cd448c3221d2fabb4b5e5131a5f337a5d2f869951dede73efbb" name="python311-torch-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="22bd141b32fb201425dc9bd161786b7c2159d84aa4ebbf0e8c4074256c06ef524a5ba12a0b01e5b48e8cf5d7d5ae40c08d7f1ff1c44b6cee278874a338553947" name="python311-torch-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="4990ea504e62d3b3ee69dd694f873497d34165a1d9e2f84dca11ad04ff49230068fb926cf989b02cb4320471c094639e72773ca2b40d03fef64518aef039980b" name="python311-torch-openmpi4" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="16e1de43196b4a5c0f87bf877242288bbcd45c8fbd6fecff7e5a4e9bd069b4411a5525c6689c87a787f2f21eaa2ced85f22c6beb583f343f6a467415dd5fbd6f" name="python311-torch-openmpi4-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="6ea957459c1aae798746d43bbc46565f13998ffc5ccea789abbea0a04c8dff47d5dd3d34a91d6f6d1fce5cc5da985921cd0c0132066c40513e2b77678f883493" name="python311-torch-openmpi4-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="f6dea73f912e2b2e714e93c123ff51131d872dc3b441e75bc7604a7fc1a377408ebbae9d843f59ffd0f08854fc75c54e6b2a283bf3abf182d890a32f5e7c1055" name="python311-torch-rocm" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="75e2d44f960311cda6a2f42abed01dcbbbd3888757c6edaf1cc4f4aef46a3964690983dc58d5c96411ea2dee35545d1c2bde4e4cacecf6327bd4aadc6eecb9c9" name="python311-torch-rocm-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="aba6d077c83f47094e5c1bd7ad5d0360fc409698db21e3339d26d832e672ea4cfa8aced40f179847374950ae50a0eeb7375687b371b08d2ce0471af1323576c7" name="python311-torch-rocm-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="71106c4f21b945febce361a5c1da6047f65388481148adb8b02e871ad776a244fcb50195e12dcf787054f34d382e988a952d7c21acaa1a5e8936a777daf9a5cb" name="python313-torch" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="00c0dbb37cf4a3e2dd75eecb32a6190bbb7dfa16c33f034177e6a4741d85be6b38a4b5cb3d89a206e78322aeab5f3ebda91b2a3ba694958f064e158846b8e06d" name="python313-torch-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="5865fbd7f1217b30cf078b18074a6cf959f0f2a9ac39e7fcbaee9b08175bbb06fb39b534c9c259650f5f784d95c38ecd8e84510bedcf820b99134b5dc006704e" name="python313-torch-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="4169b60370ae1f73e79448f1766baada75e91279fde5567252403f8eb07027bfdbd782a5f00c5ff0a51ac3255caf06250290c82f243a7ff4e2a23c327eaa5e1d" name="python313-torch-openmpi4" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="d4acdd1c96668f3ca8df29136f0d2ac188a6bf039a191704c4fcf4c3305346cad7ea4d34c10a3842e7c80ae9e59927fb857f8ec2aa1f37f4d5465a6b20e14161" name="python313-torch-openmpi4-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="e8b4ee652c2e9627ec54e150312faa211dbde757d603231fe87c6a735e2c2adfe65bd6559424ee955e51ab941ae7646aac178f8f3dfeda7e0ac30d2f2fb589a8" name="python313-torch-openmpi4-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="d566090aa6cd5a1596ee6dca868328146284b787e13930fa272d47e2a66740ec22736eeb83e28b0958e060e6ff74117600ae72191f5828f65cde4cb194235fae" name="python313-torch-rocm" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="1a12d2e199104f8317446b0904a42600e7cb8019de4f13a7393fb40150fe152ef66ba3311bcc67a5db59444ff772aee1731b979b2ef648f21072394fb207dff6" name="python313-torch-rocm-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="3e3901804ffd718d1876861fece769b76de24660e1390af3d9e8ed6c79310ab3c22a289d251d88746feaec365494de948cc0e0085a882e8d0889ccca47c72b06" name="python313-torch-rocm-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="3cacbaebef71fb00cac89b1f016c6de00b6de6dedc5bcc71be892f232c5e1556f1de68788f0a797d2b9f604bf7553f0805af7824cd1df9602b93cdf160e38a36" name="python314-torch" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="d9b3af9e5e00e4e2659a8f7ff7af58cb90508ffd62372828f80ece8bfea739db01b978fba5d5646efcb0667c6e8465f3278c2cba21ed5550663a0e25d1ffb376" name="python314-torch-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="7098f6724efa41af0bd0f7a10c1ea7d5917cee8cdae04c95060b62d63ffdbf50f7ea6e9bdea340feb098d59c1d821912595583e25a2755d1994ff1f09d72f76c" name="python314-torch-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="d94c658a6877b177c381817a609ddee42f71d472bdac89d57c3c5e935577c2681d570e1b825c661069eb988e88ed3572f7fcd10c7ddd521df8ff9337b23fc541" name="python314-torch-openmpi4" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="8996e8b8109c1d8aacc4f6bd266d743f39002efaad83f41c813b7d7f5e990442adadb0dce48c3b223bfc681ac2c919eed6bc1254b06f026c9a30ddcceb5953d9" name="python314-torch-openmpi4-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="18cc1ce5914d8a6f112c1b79c44c8cb778b0bdc7f98cd895ddf3d586d90eb2afb4317e94a7f7632dd2ff3d63294d4a25c0e25f13c8718a859df936ce0bdb95e5" name="python314-torch-openmpi4-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="99df804c579cf52bf831ee8f549674e46d4e031f509e5fb66215cf11880f968b117cd8494b03222d139b31e93af7afccd43b1b8a049ef80ba88be98538bd4053" name="python314-torch-rocm" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="7ea57963b68204ca6984031fdda54d1749122b6b5e1a89e18ad854d04d25662414b2b11f2eb9512e609bfc93b4cf6399beb8c2e4db125caefc406950c2ef3c72" name="python314-torch-rocm-debuginfo" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
<package pkgid="535fe2a42f5446a50c9c3adab0c189aa5f2f2b924f639de3be5d86d804139e007e27a9791f771b9dc246e013cdbaafcbe4720a362aba1c15d970fbe3e43f183a" name="python314-torch-rocm-devel" arch="x86_64">
  <version epoch="0" ver="2.11.0" rel="48.3"/>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1626955200">- Fix build on x86_64 by using GCC10 instead of GCC11
  https://github.com/google/XNNPACK/issues/1550</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1720699200">- update to 2.3.1 with following summarized highlights:
  * from 2.0.x:
  - torch.compile is the main API for PyTorch 2.0, which wraps your model and
    returns a compiled model. It is a fully additive (and optional) feature
    and hence 2.0 is 100% backward compatible by definition
  - Accelerated Transformers introduce high-performance support for training
    and inference using a custom kernel architecture for scaled dot product
    attention (SPDA). The API is integrated with torch.compile() and model
    developers may also use the scaled dot product attention kernels directly
    by calling the new scaled_dot_product_attention() operato
  * from 2.1.x:
  - automatic dynamic shape support in torch.compile,
    torch.distributed.checkpoint for saving/loading distributed training jobs
    on multiple ranks in parallel, and torch.compile support for the NumPy
    API.
  - In addition, this release offers numerous performance improvements (e.g.
    CPU inductor improvements, AVX512 support, scaled-dot-product-attention
    support) as well as a prototype release of torch.export, a sound
    full-graph capture mechanism, and torch.export-based quantization.
  * from 2.2.x:
  - 2x performance improvements to scaled_dot_product_attention via
    FlashAttention-v2 integration, as well as AOTInductor, a new
    ahead-of-time compilation and deployment tool built for non-python
    server-side deployments.
  * from 2.3.x:
  - support for user-defined Triton kernels in torch.compile, allowing for
    users to migrate their own Triton kernels from eager without
    experiencing performance complications or graph breaks. As well, Tensor
    Parallelism improves the experience for training Large Language Models
    using native PyTorch functions, which has been validated on training
    runs for 100B parameter models.
- added seperate openmpi4 build
- added sepetate vulcan build, although this functions isn't exposed to python
  abi
- For the obs build all the vendored sources follow the pattern
  NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
- added following patches:
  * skip-third-party-check.patch
  * fix-setup.patch
- removed patches:
  * pytorch-rm-some-gitmodules.patch
  * fix-call-of-onnxInitGraph.patch</changelog>
  <changelog author="Guang Yee &lt;gyee@suse.com&gt;" date="1724932800">- Enable sle15_python_module_pythons.
- GCC 9.3 or newer is required, regardless if CUDA is enabled.
  See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
  Therefore, for SLE15 we went with GCC 11 as it seems to be the most
  common one.
- Use %gcc_version macro for Tumbleweed.</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1727784000">- Update to 2.4.1:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.1
- Skip update to 2.4.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.4.0
- Remove _service since 'osc mr download_files' is easier to use
  and maintain
- Drop config vars not used anymore: BUILD_CAFFE2, USE_LEVELDB, USE_LMDB,
  USE_OPENCV, USE_TBB
- Remove examples package since code has been removed upstream
- Refresh pacth:
  * skip-third-party-check.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1728043200">- Add patch to fix build with oneDNN:
  * pytorch-patch-onednn.patch</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1729252800">-  Update to 2.5.0:
  * https://github.com/pytorch/pytorch/releases/tag/v2.5.0</changelog>
  <changelog author="Andreas Schwab &lt;schwab@suse.de&gt;" date="1734436800">- Use oneDNN only on x86_64 aarch64 ppc64le</changelog>
  <changelog author="Christian Goll &lt;cgoll@suse.com&gt;" date="1751630400">- Updated to 2.7.1 with following fiex:
  * Fix assertion error due to inductor permuting inputs to flex attention (#151959)
  * Fix performance regression on nanogpt speedrun (#152641)
  * Fix improve PyTorch Wheel size due to introduction of addition of 128 bit vectorization (#148320) (#152396)
  * Fix fmsub function definition (#152075)
  * Fix Floating point exception in torch.mkldnn_max_pool2d (#151848)
  * Fix ONNX decomposition does not preserve custom CompositeImplicitAutograd ops (#151826)
  * Fix error with dynamic linking of libgomp library (#150084)
  * Fix segfault in profiler with Python 3.13 (#153848)
- Changes from 2.7.0:
  * torch.onnx.dynamo_export now uses the ExportedProgram logic path (#137296)
    Users using the torch.onnx.dynamo_export API may see some ExportOptions
    become unsupported due to an internal switch to use torch.onnx.export(...,
    dynamo=True): diagnostic_options, fake_context and onnx_registry are
    removed/ignored by ExportOptions. Only dynamic_shapes is retained.
  * Finish deprecation of LRScheduler.print_lr() along with the verbose kwarg
    to the LRScheduler constructor. (#147301)
  * libtorch_python.so symbols are now invisible by default on all platforms (#142214)
  * Please use torch.export.export instead of capture_pre_autograd_graph to
    export the model for pytorch 2 export quantization (#139505)
  * New interface for
    torch.fx.passes.graph_transform_observer.GraphTransformObserver to enable
    Node Level provenance tracking (#144277)
  * torch.ao.quantization.pt2e.graph_utils.get_control_flow_submodules is no
    longer public (#141612)
  * torch.onnx.dynamo_export is deprecated (#146425, #146639, #146923)
  * XNNPACKQuantizer is deprecated in PyTorch and moved to ExecuTorch, please
    use it from executorch.backends.xnnpack.quantizer.xnnpack_quantizer instead
    of torch.ao.quantization.quantizer.xnnpack_quantizer. (#144940)
- Changes from 2.6.0
  * [Beta] torch.compiler.set_stance
  * [Beta] torch.library.triton_op
  * [Beta] torch.compile support for Python 3.13
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] New packaging APIs for AOTInductor
  * [Beta] AOTInductor: minifier
  * [Beta] AOTInductor: ABI-compatible mode code generation
  * [Beta] FP16 support for X86 CPUs (both eager and Inductor modes)
  * FlexAttention support on X86 CPU for LLMs
- Added patches for gcc15 compatibilty:
  * add-cstdint.patch
  * gloo-gcc15-fix.patch
- Updated vendored sources:
  * kineto-d975313.tar.gz -&gt; kineto-a054a4b.tar.gz
  * onnx-3bf92c0.tar.gz -&gt; onnx-b8baa84.tar.gz
  * pybind11-7c33cdc.tar.gz -&gt; pybind11-a2e59f0.tar.gz
  * sleef-60e76d2.tar.gz -&gt; sleef-56e1f79.tar.gz
  * XNNPACK-fcbf55a.tar.gz -&gt; XNNPACK-51a0103.tar.gz
  * cpuinfo-fa1c679.tar.gz -&gt; cpuinfo-1e83a2f.tar.gz
  * fmt-e69e5f9.tar.gz -&gt; fmt-1239137.tar.gz</changelog>
  <changelog author="Guillaume GARDET &lt;guillaume.gardet@opensuse.org&gt;" date="1752148800">- Add kleidiai dep (vendored)
- Workaround build error on aarch64 with -Wno-incompatible-pointer-types</changelog>
  <changelog author="James Oakley &lt;jfunk@funktronics.ca&gt;" date="1777896000">- Enable ROCm
- Update to 2.11.0:
  * Differentiable Collectives for Distributed Training
  * FlexAttention now has a FlashAttention-4 backend on Hopper and Blackwell
    GPUs.
  * MPS (Apple Silicon) Comprehensive Operator Expansion
  * RNN/LSTM GPU Export Support
  * XPU Graph</changelog>
</package>
</otherdata>