This is a beta release of cuPyNumeric.

Pip wheels are available on PyPI at https://pypi.org/project/nvidia-cupynumeric/, for Linux (x86-64 and ARM64, with CUDA 12 and multi-node support) and macOS (for ARM64). Conda packages are available at https://anaconda.org/legate/cupynumeric, for Linux (x86-64 and ARM64, with CUDA 12/13 and multi-node support). GASNet-based (rather than UCX-based) conda packages are under the gex label. Windows is currently supported through WSL.

Documentation for this release can be found at https://docs.nvidia.com/cupynumeric/26.01/.

Highlights

Added functionality

• Implement cupynumeric.pad.
• Implement cupynumeric.linalg.pinv (single-CPU/GPU only)
• Implement from_dlpack for exporting cuPyNumeric ndarray s through the DLPack interface
• Detect when an object being used to initialize a cuPyNumeric ndarray implements the DLPack interface, and use it if possible.

Bugfixes

• Ensure unimplemented stub functions always return cuPyNumeric ndarrays.

Known issues

• We are aware of hangs when using cuSolverMp-based APIs on 4+ Perlmutter nodes. This appears to be a cluster-specific issue, that we are investigating.
• We are aware of performance regressions with cupynumeric.einsum on Blackwell GPUs, starting to occur with cuBLAS 13.2. These are under investigation.

Full Changelog: https://github.com/nv-legate/cupynumeric/compare/v25.11.00...v26.01.00