mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
25 lines
1.6 KiB
Markdown
25 lines
1.6 KiB
Markdown
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## About
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This is an internal image, the [`rayproject/ray`](https://hub.docker.com/repository/docker/rayproject/ray) or [`rayproject/ray-ml`](https://hub.docker.com/repository/docker/rayproject/ray-ml) should be used!
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This has the python-level dependencies for `Ray` and the `Ray Autoscaler`. The `ray` image is built on top of this. This image is built periodically or when dependencies are added. [Find the Dockerfile here.](https://github.com/ray-project/ray/blob/master/docker/ray-deps/Dockerfile)
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## Tags
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* [`:latest`](https://hub.docker.com/repository/docker/rayproject/ray-deps/tags?page=1&name=latest) - The most recent Ray release.
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* `:1.x.x` - A specific release build.
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* [`:nightly`](https://hub.docker.com/repository/docker/rayproject/ray-deps/tags?page=1&name=nightly) - The most recent nightly build.
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* `:DATE` - A specific build.
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### Suffixes
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* `-gpu` - These are based off of an `NVIDIA CUDA` image. They require the [Nvidia Docker Runtime](https://github.com/NVIDIA/nvidia-docker) to be installed on the host for the container to access GPUs.
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* `-cpu`- These are based off of an `Ubuntu` image.
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* Tags without a suffix refer to `-cpu` images
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## Other Images
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* [`rayproject/ray`](https://hub.docker.com/repository/docker/rayproject/ray) - Ray and all of its dependencies.
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* [`rayproject/ray-ml`](https://hub.docker.com/repository/docker/rayproject/ray-ml) - This image with common ML libraries to make development & deployment more smooth!
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<br></br><br></br>
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* [`rayproject/base-deps`](https://hub.docker.com/repository/docker/rayproject/base-deps) - Internal image with system-level dependencies.
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