Inferensi, Deployment & Runtime

ray

ray-project/ray

A distributed computing runtime for machine learning training, tuning and model serving.

★ 43,7KBintang
⑂ 8KFork
3565Isu terbuka
PythonBahasa
Apache-2.0Lisensi
Q85Skor editorial

Ringkasan

A distributed computing runtime for machine learning training, tuning and model serving. The repository is maintained under ray-project on GitHub. Its primary language is Python. See the [project README](https://github.com/ray-project/ray#readme) for the supported workflows.

Fitur utama

  • A distributed computing runtime for machine learning training, tuning and model serving.

Persyaratan, instalasi, dan mulai cepat

Install Ray in a Python environment using the official installation guide. Select additional dependencies for the Ray libraries needed by your workload.

[Read the upstream installation and quickstart instructions](https://github.com/ray-project/ray#readme).

Penggunaan

Start with the documented local example, confirm it runs with your resources, then connect it to your application using the supported interface.

[Usage examples and configuration reference](https://github.com/ray-project/ray#readme).

Kompatibilitas model dan kasus penggunaan

Kompatibilitas model tidak tercantum dalam metadata repositori.

Catatan lisensi dan risiko

GitHub reports Apache-2.0 for this repository. Review the upstream license file; model weights, datasets and dependencies may have separate terms.

Source review 2026-09-05: GitHub search metadata and repository README. No runtime benchmark performed. License metadata: Apache-2.0.

Rilis dan pemeliharaan

[View upstream releases](https://github.com/ray-project/ray/releases).

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