ray/docker/ray

About

Default docker images for 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. Find the Dockerfile here.

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 - This image with common ML libraries to make development & deployment more smooth!