ray/docker/ray/README.md
mwtian b2d41fc427
[Doc] update docker readme files to include Python versions (#25099)
Similar to #25053, update the documentations on the docker site.
2022-05-25 19:42:24 -07:00

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## About
Default docker images for [Ray](https://github.com/ray-project/ray)! This includes
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)
## Tags
Images are `tagged` with the format `{Ray version}[-{Python version}][-{Platform}]`. `Ray version` tag can be one of the following:
| Ray version tag | Description |
| --------------- | ----------- |
| `latest` | The most recent Ray release. |
| `x.y.z` | A specific Ray release, e.g. 1.12.1 |
| `nightly` | The most recent Ray development build (a recent commit from Github `master`) |
| `6 character Git SHA prefix` | A specific development build (uses a SHA from the Github `master`, e.g. `8960af`). |
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`.
The optional `Platform` tag specifies the platform where the image is intended for:
| Platform tag | Description |
| --------------- | ----------- |
| `-cpu` | These are based off of an Ubuntu image. |
| `-cuXX` | These are based off of an NVIDIA CUDA image with the specified CUDA version `xx`. They require the Nvidia Docker Runtime. |
| `-gpu` | Aliases to a specific `-cuXX` tagged image. |
| no tag | Aliases to `-cpu` tagged images for `ray`, and aliases to ``-gpu`` tagged images for `ray-ml`. |
Examples tags:
- none: equivalent to `latest`
- `latest`: equivalent to `latest-py37-cpu`, i.e. image for the most recent Ray release
- `nightly-py38-cpu`
- `806c18-py38-cu112`
## Other Images
* [`rayproject/ray-ml`](https://hub.docker.com/repository/docker/rayproject/ray-ml) - This image with common ML libraries to make development & deployment more smooth!