nv-legate/cupynumeric

Última actividad de origen registrada: 28 de julio de 2026 a las 13:30

Catálogo de SkillsMP actualizado: 5 de noviembre de 2026 a las 19:57

skills recopiladas: 4

Estrellas en GitHub: 981
Forks en GitHub: 89

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README del repositorio

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# cuPyNumeric > [!IMPORTANT] > cuPyNumeric has reached end of life and is no longer maintained or > supported. The final release is `v26.06.01`. No further releases, > fixes, or support are planned. Existing packages and documentation remain > available for historical reference, but we do not recommend starting new > projects with cuPyNumeric. > > Consider one of the following alternatives for multi-GPU/multi-node > array computing: > * [nvmath-python](https://docs.nvidia.com/cuda/nvmath-python/latest/overview.html), > in particular the > [nvmath.distributed module](https://docs.nvidia.com/cuda/nvmath-python/latest/distributed-apis/distribution.html) > * [CuPy](https://cupy.dev) combined with > [mpi4py](https://mpi4py.readthedocs.io/en/stable/) or > [Dask Array](https://docs.dask.org/en/latest/how-to/selecting-the-collection-backend.html) > & [Dask-CUDA](https://docs.nvidia.com/dask-cuda/latest/) > * [JAX](https://jax.dev), in particular the > [multi-controller module](https://docs.jax.dev/en/latest/501/multiprocess.html) > * [PyTorch](https://pytorch.org), in particular the > [torch.distributed module](https://docs.pytorch.org/docs/stable/distributed) cuPyNumeric is a high-performance array computing library that implements the NumPy API on top of the Legate framework. It enables you to run existing NumPy workflows on GPUs and distributed systems with little to no code changes. Whether your work involves large-scale data analysis, complex simulations, or machine learning, cuPyNumeric allows you to seamlessly scale from a single CPU, to a single GPU, and up to thousands of GPUs across multiple nodes. ## Installation Existing pre-built cuPyNumeric packages remain available from [conda](https://docs.conda.io/projects/conda/en/latest/index.html) on the [legate channel](https://anaconda.org/legate/cupynumeric) and from [PyPI](https://pypi.org/project/nvidia-cupynumeric/). See https://docs.nvidia.com/cupynumeric/26.06/installation.html for details about different install configurations, or building cuPyNumeric from source. 📌 **Note** Linux packages support Python versions 3.11 to 3.14. Windows is only supported through WSL. ## Documentation The cuPyNumeric documentation can be found [here](https://docs.nvidia.com/cupynumeric/26.06/). ## Note The cuPyNumeric project is independent of the CuPy project. CuPy is a trademark of Preferred Networks, Inc, and the name 'cuPyNumeric' is used with their permission.

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Skills en este repositorio

Desarrolladores de software

1 categorías ocupacionales · 25% clasificado

skill

ocupación

descripción

actualizado

cupynumeric-install

sin clasificar

Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.

Idioma del texto original: inglés

2026-07-28

cupynumeric-migration-readiness

sin clasificar

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be…

Idioma del texto original: inglés

2026-07-28

cupynumeric-parallel-data-load

sin clasificar

Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including…

Idioma del texto original: inglés

2026-07-28

cupynumeric-hdf5

Desarrolladores de software

Read and write large cuPyNumeric arrays to HDF5 with Legate's parallel, distributed HDF5 I/O (legate.io.hdf5: to_file, from_file, from_file_batched). Use when a developer needs to save a cuPyNumeric array to an .h5/.hdf5 file, load an HDF5 dataset into a…

Idioma del texto original: inglés

2026-06-02

Mostrando 4 de 4 skills recopiladas.