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.
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 docker.io sudo service docker start
ubuntu user to the
docker group to allow running Docker commands
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:
Should produce an empty table similar to the following:
REPOSITORY TAG IMAGE ID CREATED SIZE
Clone the Ray repository¶
git clone https://github.com/ray-project/ray.git
Build Docker images¶
Run the script to create Docker images.
cd ray ./build-docker.sh
This script creates several Docker images:
ray-project/deployimage is a self-contained copy of code and binaries suitable for end users.
ray-project/examplesadds additional libraries for running examples.
ray-project/base-depsimage 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:
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
<shm-size> with a limit appropriate for your system, for example
-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/mini_test.py # 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
Learning to play Pong¶