Installation on Docker

You can install Ray from source on any platform that runs Docker. We do not presently publish Docker images for Ray, but you can build them yourself using the Ray distribution.

Using Docker can streamline the build process and provide a reliable way to get up and running quickly.

Install Docker

Mac, Linux, Windows platforms

The Docker Platform release is available for Mac, Windows, and Linux platforms. Please download the appropriate version from the Docker website and follow the corresponding installation instructions. Linux user may find these alternate instructions helpful.

Docker installation on EC2 with Ubuntu


The Ray autoscaler can automatically install Docker on all of the nodes of your cluster.

The instructions below show in detail how to prepare an Amazon EC2 instance running Ubuntu 16.04 for use with Docker.

Apply initialize the package repository and apply system updates:

sudo apt-get update
sudo apt-get -y dist-upgrade

Install Docker and start the service:

sudo apt-get install -y
sudo service docker start

Add the ubuntu user to the docker group to allow running Docker commands without sudo:

sudo usermod -a -G docker ubuntu

Initiate a new login to gain group permissions (alternatively, log out and log back in again):

exec sudo su -l ubuntu

Confirm that docker is running:

docker images

Should produce an empty table similar to the following:

REPOSITORY          TAG                 IMAGE ID            CREATED             SIZE

Clone the Ray repository

git clone

Build Docker images

Run the script to create Docker images.

cd ray

This script creates several Docker images:

  • The ray-project/deploy image is a self-contained copy of code and binaries suitable for end users.
  • The ray-project/examples adds additional libraries for running examples.
  • The ray-project/base-deps image builds from Ubuntu Xenial and includes Anaconda and other basic dependencies and can serve as a starting point for developers.

Review images by listing them:

docker images

Output should look something like the following:

REPOSITORY                          TAG                 IMAGE ID            CREATED             SIZE
ray-project/examples                latest              7584bde65894        4 days ago          3.257 GB
ray-project/deploy                  latest              970966166c71        4 days ago          2.899 GB
ray-project/base-deps               latest              f45d66963151        4 days ago          2.649 GB
ubuntu                              xenial              f49eec89601e        3 weeks ago         129.5 MB

Launch Ray in Docker

Start out by launching the deployment container.

docker run --shm-size=<shm-size> -t -i ray-project/deploy

Replace <shm-size> with a limit appropriate for your system, for example 512M or 2G. The -t and -i options here are required to support interactive use of the container.

Note: Ray requires a large amount of shared memory because each object store keeps all of its objects in shared memory, so the amount of shared memory will limit the size of the object store.

You should now see a prompt that looks something like:


Test if the installation succeeded

To test if the installation was successful, try running some tests. Within the container shell enter the following commands:

python -m pytest -v test/  # This tests some basic functionality.

You are now ready to continue with the tutorial.

Running examples in Docker

Ray includes a Docker image that includes dependencies necessary for running some of the examples. This can be an easy way to see Ray in action on a variety of workloads.

Launch the examples container.

docker run --shm-size=1024m -t -i ray-project/examples

Batch L-BFGS

python /ray/examples/lbfgs/

Learning to play Pong

python /ray/examples/rl_pong/