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[docs] Fix Docker links (#12702)

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* switch autoscaler -> ray-ml

* add more tables
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Ian Rodney
2020-12-10 18:08:48 -08:00
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parent 62d6b0a558
commit 9b3ef2f340
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@@ -233,13 +233,45 @@ However, should you need to build from source, follow :ref:`these instructions f
Docker Source Images
--------------------
Most users should pull a Docker image from the Ray Docker Hub.
Most users should pull a Docker image from the `Ray Docker Hub. <https://hub.docker.com/r/rayproject/>`_
- The ``rayproject/ray`` image has ray and all required dependencies. It comes with anaconda and Python 3.7.
- The ``rayproject/autoscaler`` image has the above features as well as many additional libraries.
- The ``rayproject/ray`` `image has ray and all required dependencies. It comes with anaconda and Python 3.7. <https://hub.docker.com/r/rayproject/ray>`_
- The ``rayproject/ray-ml`` `image has the above features as well as many additional libraries. <https://hub.docker.com/r/rayproject/ray-ml>`_
- The ``rayproject/base-deps`` and ``rayproject/ray-deps`` are for the linux and python dependencies respectively.
These images are tagged by their release number (or commit hash for nightlies) as well as a ``"-gpu"`` if they are GPU compatible.
Image releases are `tagged` using the following format:
.. list-table::
:widths: 25 50
:header-rows: 1
* - Tag
- Description
* - latest
- The most recent Ray release.
* - 1.x.x
- A specific Ray release.
* - nightly
- The most recent Ray build (the most recent commit on Github ``master``)
* - Git SHA
- A specific nightly build (uses a SHA from the Github ``master``).
Each tag has `variants` that add or change functionality:
.. list-table::
:widths: 16 40
:header-rows: 1
* - Variant
- Description
* - -gpu
- These are based off of an NVIDIA CUDA image. They require the Nvidia Docker Runtime.
* - -cpu
- These are based off of an Ubuntu image.
* - <no tag>
- Aliases to ``-cpu`` tagged images
If you want to tweak some aspect of these images and build them locally, refer to the following script: