nv-legate/cupynumeric

記録された最新のソース活動: 2026年7月28日 13:30

SkillsMP カタログ更新: 2026年11月5日 19:57

収集済み skills: 4

GitHub スター: 981
GitHub フォーク: 89

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リポジトリの README

README の元ファイル：README.md

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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 cuPyNumeric has reached end of life and is no longer maintained or 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 available for historical reference, but we do not recommend starting new 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.

リポジトリの README を読み込んでいます…

このリポジトリの skills

ソフトウェア開発者

1 件の職業カテゴリ · 25% 分類済み

skill

職業分類

説明

更新

cupynumeric-install

未分類

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

原文の言語: 英語

2026-07-28

cupynumeric-migration-readiness

未分類

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…

原文の言語: 英語

2026-07-28

cupynumeric-parallel-data-load

未分類

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…

原文の言語: 英語

2026-07-28

cupynumeric-hdf5

ソフトウェア開発者

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…

原文の言語: 英語

2026-06-02

収集済み skill 4 件中 4 件を表示しています。