mirror of
https://github.com/vale981/ray
synced 2025-03-05 10:01:43 -05:00
34 lines
2 KiB
Markdown
34 lines
2 KiB
Markdown
## About
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Default docker images for [Ray](https://github.com/ray-project/ray)! This includes
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everything needed to get started with running Ray! They work for both local development and *are ideal* for use with the [Ray Cluster Launcher](https://docs.ray.io/en/master/cluster/cloud.html). [Find the Dockerfile here.](https://github.com/ray-project/ray/blob/master/docker/ray/Dockerfile)
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## Tags
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Images are `tagged` with the format `{Ray version}[-{Python version}][-{Platform}]`. `Ray version` tag can be one of the following:
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| Ray version tag | Description |
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| --------------- | ----------- |
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| `latest` | The most recent Ray release. |
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| `x.y.z` | A specific Ray release, e.g. 1.12.1 |
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| `nightly` | The most recent Ray development build (a recent commit from Github `master`) |
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| `6 character Git SHA prefix` | A specific development build (uses a SHA from the Github `master`, e.g. `8960af`). |
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The optional `Python version` tag specifies the Python version in the image. All Python versions supported by Ray are available, e.g. `py37`, `py38`, `py39` and `py310`. If unspecified, the tag points to an image using `Python 3.7`.
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The optional `Platform` tag specifies the platform where the image is intended for:
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| Platform tag | Description |
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| --------------- | ----------- |
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| `-cpu` | These are based off of an Ubuntu image. |
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| `-cuXX` | These are based off of an NVIDIA CUDA image with the specified CUDA version `xx`. They require the Nvidia Docker Runtime. |
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| `-gpu` | Aliases to a specific `-cuXX` tagged image. |
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| no tag | Aliases to `-cpu` tagged images for `ray`, and aliases to ``-gpu`` tagged images for `ray-ml`. |
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Examples tags:
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- none: equivalent to `latest`
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- `latest`: equivalent to `latest-py37-cpu`, i.e. image for the most recent Ray release
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- `nightly-py38-cpu`
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- `806c18-py38-cu112`
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## Other Images
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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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