chore(deps): bump the python-packages group across 1 directory with 3 updates

[dependabot]

Bumps the python-packages group with 3 updates in the / directory: numpy, torch and pytest.

Updates numpy from 1.26.4 to 2.0.1

Release notes

Sourced from numpy's releases.

v2.0.1

NumPy 2.0.1 Release Notes

NumPy 2.0.1 is a maintenance release that fixes bugs and regressions discovered after the 2.0.0 release. NumPy 2.0.1 is the last planned release in the 2.0.x series, 2.1.0rc1 should be out shortly.

The Python versions supported by this release are 3.9-3.12.

NOTE: Do not use the GitHub generated "Source code" files listed in the "Assets", they are garbage.

Improvements

np.quantile with method closest_observation chooses nearest even order statistic

This changes the definition of nearest for border cases from the nearest odd order statistic to nearest even order statistic. The numpy implementation now matches other reference implementations.

(gh-26656)

Contributors

A total of 15 people contributed to this release. People with a "+" by their names contributed a patch for the first time.

• @​vahidmech +
• Alex Herbert +
• Charles Harris
• Giovanni Del Monte +
• Leo Singer
• Lysandros Nikolaou
• Matti Picus
• Nathan Goldbaum
• Patrick J. Roddy +
• Raghuveer Devulapalli
• Ralf Gommers
• Rostan Tabet +
• Sebastian Berg
• Tyler Reddy
• Yannik Wicke +

Pull requests merged

A total of 24 pull requests were merged for this release.

• #26711: MAINT: prepare 2.0.x for further development
• #26792: TYP: fix incorrect import in ma/extras.pyi stub
• #26793: DOC: Mention '1.25' legacy printing mode in set_printoptions
• #26794: DOC: Remove mention of NaN and NAN aliases from constants

... (truncated)

Commits

• 4c9f431 Merge pull request #27000 from charris/prepare-2.0.1
• 0e70e00 REL: Prepare for the NumPy 2.0.1 release [wheel build]
• 4d10ffc Merge pull request #26995 from charris/backport-26985
• 764b667 BUG: Add object cast to avoid warning with limited API
• 9be6ad6 Merge pull request #26971 from charris/backport-26935
• 6d950e9 BUG: fix f2py tests to work with v2 API
• 89630c0 Merge pull request #26962 from charris/backport-26919
• 88fa840 TST: Apply test suggestion by Nathan for rlstrip fixes
• a9da01e BUG,MAINT: Fix utf-8 character stripping memory access
• 6afbbf8 Merge pull request #26963 from charris/backport-26930
• Additional commits viewable in compare view

Updates torch from 2.3.1+rocm6.0 to 2.4.0

Release notes

Sourced from torch's releases.

PyTorch 2.4: Python 3.12, AOTInductor freezing, libuv backend for TCPStore

PyTorch 2.4 Release Notes

• Highlights
• Tracked Regressions
• Backward incompatible changes
• Deprecations
• New features
• Improvements
• Bug Fixes
• Performance
• Documentation
• Developers
• Security

Highlights

We are excited to announce the release of PyTorch® 2.4! PyTorch 2.4 adds support for the latest version of Python (3.12) for torch.compile. AOTInductor freezing gives developers running AOTInductor more performance based optimizations by allowing the serialization of MKLDNN weights. As well, a new default TCPStore server backend utilizing libuv has been introduced which should significantly reduce initialization times for users running large-scale jobs. Finally, a new Python Custom Operator API makes it easier than before to integrate custom kernels into PyTorch, especially for torch.compile.

This release is composed of 3661 commits and 475 contributors since PyTorch 2.3. We want to sincerely thank our dedicated community for your contributions. As always, we encourage you to try these out and report any issues as we improve 2.4.

Tracked Regressions

Subproc exception with torch.compile and onnxruntime-training

There is a reported issue when using torch.compile if onnxruntime-training lib is installed. The issue will be fixed in v2.4.1. It can be solved locally by setting the environment variable TORCHINDUCTOR_WORKER_START=fork before executing the script.

cu118 wheels will not work with pre-cuda12 drivers

It was also reported that the new version of triton uses cuda features that are not compatible with pre-cuda12 drivers. In this case, the workaround is to set TRITON_PTXAS_PATH manually as follows (adapt the code according to the local installation path):

TRITON_PTXAS_PATH=/usr/local/lib/python3.10/site-packages/torch/bin/ptxas  python script.py

Backwards Incompatible Change

Python frontend

Default TreadPool size to number of physical cores (#125963)

Changed the default number of threads used for intra-op parallelism from the number of logical cores to the number of

... (truncated)

Commits

• See full diff in compare view

Updates pytest from 8.3.1 to 8.3.2

Release notes

Sourced from pytest's releases.

8.3.2

pytest 8.3.2 (2024-07-24)

Bug fixes

• #12652: Resolve regression [conda]{.title-ref} environments where no longer being automatically detected.

  -- by RonnyPfannschmidt{.interpreted-text role="user"}

Commits

• bbcec9c Prepare release version 8.3.2
• 78fe8b6 Merge pull request #12657 from pytest-dev/patchback/backports/8.3.x/6c806b499...
• 238bad2 Merge pull request #12656 from RonnyPfannschmidt/fix-12652-detect-conda-env
• ae6034a Merge pull request #12641 from pytest-dev/patchback/backports/8.3.x/c03989cee...
• 31337ab Merge pull request #12640 from pytest-dev/update-user
• ca3070b Merge pull request #12637 from pytest-dev/release-8.3.1
• See full diff in compare view

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[dependabot]

Looks like these dependencies are updatable in another way, so this is no longer needed.