Guillaume GARDET
62aba2ab6b
- 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. OBS-URL: https://build.opensuse.org/package/show/science:machinelearning/python-torch?expand=0&rev=36
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-------------------------------------------------------------------
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Thu Aug 29 04:28:03 UTC 2024 - Guang Yee <gyee@suse.com>
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- Enable sle15_python_module_pythons.
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- GCC 9.3 or newer is required, regardless if CUDA is enabled.
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See https://github.com/pytorch/pytorch/blob/v2.3.1/CMakeLists.txt#L48
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Therefore, for SLE15 we went with GCC 11 as it seems to be the most
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common one.
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- Use %gcc_version macro for Tumbleweed.
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-------------------------------------------------------------------
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Thu Jul 11 09:37:17 UTC 2024 - Christian Goll <cgoll@suse.com>
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- update to 2.3.1 with following summarized highlights:
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* from 2.0.x:
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- torch.compile is the main API for PyTorch 2.0, which wraps your model and
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returns a compiled model. It is a fully additive (and optional) feature
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and hence 2.0 is 100% backward compatible by definition
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- Accelerated Transformers introduce high-performance support for training
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and inference using a custom kernel architecture for scaled dot product
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attention (SPDA). The API is integrated with torch.compile() and model
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developers may also use the scaled dot product attention kernels directly
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by calling the new scaled_dot_product_attention() operato
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* from 2.1.x:
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- automatic dynamic shape support in torch.compile,
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torch.distributed.checkpoint for saving/loading distributed training jobs
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on multiple ranks in parallel, and torch.compile support for the NumPy
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API.
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- In addition, this release offers numerous performance improvements (e.g.
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CPU inductor improvements, AVX512 support, scaled-dot-product-attention
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support) as well as a prototype release of torch.export, a sound
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full-graph capture mechanism, and torch.export-based quantization.
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* from 2.2.x:
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- 2x performance improvements to scaled_dot_product_attention via
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FlashAttention-v2 integration, as well as AOTInductor, a new
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ahead-of-time compilation and deployment tool built for non-python
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server-side deployments.
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* from 2.3.x:
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- support for user-defined Triton kernels in torch.compile, allowing for
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users to migrate their own Triton kernels from eager without
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experiencing performance complications or graph breaks. As well, Tensor
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Parallelism improves the experience for training Large Language Models
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using native PyTorch functions, which has been validated on training
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runs for 100B parameter models.
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- added seperate openmpi4 build
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- added sepetate vulcan build, although this functions isn't exposed to python
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abi
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- For the obs build all the vendored sources follow the pattern
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NAME-7digitcommit.tar.gz and not the NAME-COMMIT.tar.gz
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- added following patches:
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* skip-third-party-check.patch
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* fix-setup.patch
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- removed patches:
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* pytorch-rm-some-gitmodules.patch
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* fix-call-of-onnxInitGraph.patch
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-------------------------------------------------------------------
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Thu Jul 22 14:40:45 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Fix build on x86_64 by using GCC10 instead of GCC11
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https://github.com/google/XNNPACK/issues/1550
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-------------------------------------------------------------------
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Thu Jul 22 10:11:03 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Update to 1.9.0
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- Release notes: https://github.com/pytorch/pytorch/releases/tag/v1.9.0
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- Drop upstreamed patch:
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* fix-mov-operand-for-gcc.patch
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- Drop unneeded patches:
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* removed-peachpy-depedency.patch
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- Refresh patches:
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* skip-third-party-check.patch
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* fix-call-of-onnxInitGraph.patch
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- Add new patch:
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* pytorch-rm-some-gitmodules.patch
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-------------------------------------------------------------------
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Thu Jul 22 09:07:31 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Add _service file to ease future update of deps
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-------------------------------------------------------------------
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Thu Jul 22 08:26:17 UTC 2021 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Update sleef to fix build on aarch64
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-------------------------------------------------------------------
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Fri Apr 23 21:51:36 UTC 2021 - Matej Cepl <mcepl@suse.com>
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- Don't build python36-* package (missing pandas)
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-------------------------------------------------------------------
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Thu Jan 21 23:28:20 UTC 2021 - Benjamin Greiner <code@bnavigator.de>
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- Fix python-rpm-macros usage
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-------------------------------------------------------------------
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Wed Oct 7 15:15:28 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Use GCC9 to build on aarch64 Tumbleweed to workaround SVE
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problem with GCC10 with sleef, see:
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https://github.com/pytorch/pytorch/issues/45971
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-------------------------------------------------------------------
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Thu Aug 20 09:04:08 UTC 2020 - Martin Liška <mliska@suse.cz>
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- Use memoryperjob constraint instead of %limit_build macro.
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-------------------------------------------------------------------
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Tue Jun 23 15:28:57 UTC 2020 - Christian Goll <cgoll@suse.com>
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- updated to new stable release 1.5.1 which has following changes:
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This release includes several major new API additions and improvements. These
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include new APIs for autograd allowing for easy computation of hessians and
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jacobians, a significant update to the C++ frontend, ‘channels last’ memory
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format for more performant computer vision models, a stable release of the
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distributed RPC framework used for model parallel training, and a new API
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that allows for the creation of Custom C++ Classes that was inspired by
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PyBind. Additionally torch_xla 1.5 is now available and tested with the
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PyTorch 1.5 release providing a mature Cloud TPU experience.
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* see release.html for detailed information
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- added patches:
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* fix-call-of-onnxInitGraph.patch for API mismatch in onnx
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* fix-mov-operand-for-gcc.patch for aarch64 operands
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- removed sources:
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* cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
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* gloo-7c541247a6fa49e5938e304ab93b6da661823d0f.tar.gz
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* onnx-fea8568cac61a482ed208748fdc0e1a8e47f62f5.tar.gz
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* psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
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* pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
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- added sources:
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* cpuinfo-0e6bde92b343c5fbcfe34ecd41abf9515d54b4a7.tar.gz
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* gloo-113bde13035594cafdca247be953610b53026553.tar.gz
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* onnx-9fdae4c68960a2d44cd1cc871c74a6a9d469fa1f.tar.gz
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* psimd-10b4ffc6ea9e2e11668f86969586f88bc82aaefa.tar.gz
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* pthreadpool-d465747660ecf9ebbaddf8c3db37e4a13d0c9103.tar.gz
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-------------------------------------------------------------------
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Tue Jun 23 09:25:06 UTC 2020 - Christian Goll <cgoll@suse.com>
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- updated to bugfix release 1.4.1 and added _multibuild file so
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that cuda versions can be build on commandline
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-------------------------------------------------------------------
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Thu Apr 23 14:30:22 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
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- Make sure to pull py2/py3 package from the devel pkg
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-------------------------------------------------------------------
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Thu Apr 23 09:54:25 UTC 2020 - Tomáš Chvátal <tchvatal@suse.com>
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- Do not pull in python2 only dependencies
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-------------------------------------------------------------------
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Wed Feb 26 13:07:14 UTC 2020 - Simon Lees <sflees@suse.de>
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- Exclude i586 builds for now, they fail with a cryptic return
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code of 1 from cmake from python.
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-------------------------------------------------------------------
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Fri Feb 21 14:15:00 UTC 2020 - Christian Goll <cgoll@suse.com>
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- updated to stable release 1.4.0, which has as Highlights:
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* Distributed Model Parallel Training
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* Pruning functionalities have been added to PyTorch
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- New Features:
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* torch.optim.lr_scheduler now support “chaining.”
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* torch.distributed.rpc is a newly introduced package
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- full Changelog listed in relases file or under
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https://github.com/pytorch/pytorch/releases
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and in the releases.hml file
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- added files:
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* skip-third-party-check.patch which is a patch to skip
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the check of disabled dependencies
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* QNNPACK-7d2a4e9931a82adc3814275b6219a03e24e36b4c.tar.gz
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which is part of pytorch but developed in different repo
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* releases.html which is the downloaded releases file
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- removed patch files:
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* fix-build-options.patch
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* honor-PSIMD-env.patch
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* removed-some-tests.patch
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-------------------------------------------------------------------
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Tue Jan 14 13:01:33 UTC 2020 - Guillaume GARDET <guillaume.gardet@opensuse.org>
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- Requires python-PeachPy on x86_64 only, as it is optional
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and available on x86_64 only
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-------------------------------------------------------------------
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Wed Jan 8 10:47:18 UTC 2020 - Christian Goll <cgoll@suse.com>
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- updated the requirement for examples and converters
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-------------------------------------------------------------------
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Wed Jun 12 11:17:34 UTC 2019 - Christian Goll <cgoll@suse.com>
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- Updated to stable version 1.1.0, which needed also updates of
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following dependend sources:
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* onnx-1.4.1.tar.gz ->
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onnx-22662bfd4dcc6baebf29e3b823a051676f991001.tar.gz
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- Removed following sources:
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* FBGEMM-f65f0ebe54f0512d8f42ee10025b596e3f42e0b8.tar.gz
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- Added following sources:
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* foxi-8f74bc4df3a4cfc69b1a3eadf62aa29d9961c72d.tar.gz
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- Changed patch
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* fix-build-options.patch to work with new buid system and
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exclude FBGEMM
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- Added patch:
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* honor-PSIMD-env.patch, which makes depend sources of pytorch
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to use the source of psimd
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-------------------------------------------------------------------
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Tue Mar 26 09:33:11 UTC 2019 - Christian Goll <cgoll@suse.com>
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- Inital commit of pytorch/caffe2 which is an opensource
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machineleraning platform. This is the stable release 1.0.1
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including like other tools a lot of third party sources,
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which could not be used from the base system due to messy
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build system. Additional sources are
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* gloo, a communitcation library for GPUs as
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gloo-670b4d4aa46886cc66874e2a4dc846f5cfc2a285.tar.gz
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* fbgemm, a low precission, high peformance matrix lib
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FBGEMM-f65f0ebe54f0512d8f42ee10025b596e3f42e0b8.tar.gz
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* cpuinfo, a cross platform cpu information tool
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cpuinfo-89fe1695edf9ee14c22f815f24bac45577a4f135.tar.gz
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* sleef, a function for elementary functions
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sleef-191f655caa25526ae226cf88dd2529265176014a.tar.gz
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* pytbind11, which exposes C/C++ headers to pythob, but
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the source code of this library is deeply integrated into
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pytorch, so we need
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pybind11-25abf7efba0b2990f5a6dfb0a31bc65c0f2f4d17.tar.gz
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* onnx, which is an format for exchaning neural networks as
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onnx-1.4.1.tar.gz
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* pthreadpool, a pthread based thread tool implementation, which
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can be used when omp is not available
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pthreadpool-13da0b4c21d17f94150713366420baaf1b5a46f4.tar.gz
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* FXdiv, a Header-only library for division via fixed-point
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multiplication by inverse, which has no stable API atm, so
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FXdiv-b742d1143724d646cd0f914646f1240eacf5bd73.tar.gz
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* psimd, portable 128-bit SIMD intrinsics
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psimd-90a938f30ba414ada2f4b00674ee9631d7d85e19.tar.gz
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* fp16, a numeric conversion library
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FP16-febbb1c163726b5db24bed55cc9dc42529068997.tar.gz
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* gemmlowp, self-contained low-precision GEMM library as
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gemmlowp-8416bab644641a5c0a81ecf91a5cda804af0aee1.tar.gz
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* fix-build-options.patch, which points pytorch to system libs
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* removed-peachpy-depedency.patch, which forces to use system
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peachpy
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|