[air](deps): Update cupy-cuda12x requirement from >=13.4.0 to >=14.1.0 in /python/requirements/ml

[dependabot]

Updates the requirements on cupy-cuda12x to permit the latest version.

Release notes

Sourced from cupy-cuda12x's releases.

v14.1.0

CuPy v14.1.0 Release Note

This release for the CuPy v14 series introduces new features, enhancements, and bug fixes.

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✨ Highlights

Support for large sparse matrices

CuPy now supports large sparse matrices, allowing 64-bit sized dimensions and number of nonzero elements. Similar to SciPy, creation functions will automatically choose the larger index dtype for the sparsity pattern. The added functionality mostly uses newly wrapped cuSPARSE calls.

Initial support for free-threaded Python

CuPy 14.1 now releases free-threaded Python 3.14t Linux wheels and includes a number of thread-safety fixes. As threading issues can be intermittent, please report any issues you encounter. A known limitation is that some threaded CUDA graph-capture calls may fail when using threads.

Support for structured dtypes with fields

CuPy now supports structured dtypes with fields in kernels. This enables previously missing features such as comparisons and casts/copies. Because CUDA requires a larger alignment in some cases, CuPy now includes the make_aligned_dtype helper to create structured dtypes with larger alignments than guaranteed by NumPy’s align=True.

Caching for CUDA C++ template instantiations

CuPy now caches template kernels instantiated using name_expressions with RawModule. This avoids recompilation in cases where CuPy was previously unable to use the on-disk cache.

Optional faster kernel compilation using PCH

Users can now set the environment variable CUPY_NVRTC_USE_PCH=1 to use NVRTC’s precompiled headers (PCH) with CUDA 12.8+. This can drastically speed up compilation of multiple kernels and should be especially useful when the on-disk cache is cold or not used.

Support for CUDA 13.2

CuPy now supports CUDA 13.2 and NCCL 2.29.

New API coverage

CuPy now supports cupy.byteswap, cupy.isdtype, cupy.matrix_transpose, cupy.linalg.matmul and the cupyx.scipy.linalg.sparse.bicgstab (BIConjugate Gradient STABilized) solver. cupy.repeat was sped up and extended to allow a CuPy array of repeats.

📝 Changes

See here for the complete list of merged PRs.

New Features

• Implement ndarray.byteswap() (#9868)
• Add bicgstab solver for sparse linear systems (#9889)
• Add int64 index support to cupyx.scipy.sparse (#9914)
• ENH: (almost) full structured dtype support (#9927)

Enhancements

... (truncated)

Commits

• 7ed30be Merge pull request #9957 from kmaehashi/copilot/fix-v14-1-0-similar-to-9733
• e77766a Bump Docker package pins to v14.1.0
• 9f14e79 Merge pull request #9955 from chainer-ci/bp-9954-v14-fix-args
• 9cc09f7 Merge pull request #9954 from kmaehashi/fix-args
• 1ceffb8 Merge pull request #9953 from kmaehashi/bump-v14.1.0
• d8ba6c6 bump version to v14.1.0
• c3b42db Merge pull request #9935 from chainer-ci/bp-9757-v14-comparison-weak-promotion
• 4cfaf65 Merge pull request #9934 from chainer-ci/bp-9861-v14-think-ft-unitest
• 2129b24 Merge pull request #9757 from seberg/comparison-weak-promotion
• a89a311 Merge pull request #9933 from chainer-ci/bp-9853-v14-fix/use-pathfinder-for-h...
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