This release is meant to fix the following issues (regressions / silent correctness):
Breaking Changes:
The pytorch/pytorch docker image now installs the PyTorch package through pip and has switch its conda installation from miniconda to miniforge (#134274)
Windows:
Fix performance regression on Windows related to MKL static linking (#130619) (#130697)
Fix error during loading on Windows: [WinError 126] The specified module could not be found. (#131662) (#130697)
Fix error on Windows with CPU inference (#131958) (#130697)
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Bumps torch from 2.3.1 to 2.4.1.
Release notes
Sourced from torch's releases.
... (truncated)
Commits
ee1b680
[Doc] Fix rendering of the unicode characters (#134695)79c8867
Fix docstring for torch.signal.windows.nuttall (#134704)38b96d3
Do not use\<filesystem>
on Linux (#134494) (#134604)b84e8c6
Move module_tracker to logging for confused hierarchy (#134467) (#134501)6a79d4a
[ROCm] Prevent accidental enablement of efficient attention. (#134531)e0ddbff
[Release Only] Disable flaky failing tests in release. Pin optree. Pin sympy ...314f033
Use ephemeral runners for windows nightly builds (#134463) (#134496)9c1f78e
[CD] Use ephemeral arm64 runners for nightly and docker builds (#134473) (#13...3675fc5
Use ephemeral runners for linux nightly builds (#134367) (#134492)920c023
docker: Use miniforge, install from pip (#134497)Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting
@dependabot rebase
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