mirror of
https://github.com/vale981/ray
synced 2025-03-10 21:36:39 -04:00
ad4b03bf7f
* new path for python build * add flag * build tar using git archive * no exit from start_ray.sh * update Docker instructions * update build docker script * add git revision * fix typo * bug fixes and clarifications * mend * add objectmanager ports to docker instructions * rewording * Small updates to documentation.
151 lines
4.2 KiB
Markdown
151 lines
4.2 KiB
Markdown
# Installation on Docker
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You can install Ray 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.
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Using Docker can streamline the build process and provide a reliable way to get up and running quickly.
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## Install Docker
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### Mac, Linux, Windows platforms
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The Docker Platform release is available for Mac, Windows, and Linux platforms. Please download the appropriate version from the [Docker website](https://www.docker.com/products/overview#/install_the_platform) and follow the corresponding installation instructions.
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Linux user may find these [alternate instructions](https://www.digitalocean.com/community/tutorials/how-to-install-and-use-docker-on-ubuntu-16-04) helpful.
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### Docker installation on EC2 with Ubuntu
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The instructions below show in detail how to prepare an Amazon EC2 instance running Ubuntu 16.04 for use with Docker.
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Apply initialize the package repository and apply system updates:
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```
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sudo apt-get update
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sudo apt-get -y dist-upgrade
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```
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Install Docker and start the service:
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```
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sudo apt-get install -y docker.io
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sudo service docker start
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```
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Add the `ubuntu` user to the `docker` group to allow running Docker commands without sudo:
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```
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sudo usermod -a -G docker ubuntu
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```
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Initiate a new login to gain group permissions (alternatively, log out and log back in again):
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```
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exec sudo su -l ubuntu
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```
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Confirm that docker is running:
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```
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docker images
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```
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Should produce an empty table similar to the following:
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```
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REPOSITORY TAG IMAGE ID CREATED SIZE
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```
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## Clone the Ray repository
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```
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git clone https://github.com/ray-project/ray.git
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```
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## Build Docker images
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Run the script to create Docker images.
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```
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cd ray
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./build-docker.sh
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```
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This script creates several Docker images:
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* The `ray-project/deploy` image is a self-contained copy of code and binaries suitable for end users.
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* The `ray-project/examples` adds additional libraries for running examples.
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* 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.
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Review images by listing them:
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```
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$ docker images
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```
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Output should look something like the following:
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```
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REPOSITORY TAG IMAGE ID CREATED SIZE
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ray-project/examples latest 7584bde65894 4 days ago 3.257 GB
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ray-project/deploy latest 970966166c71 4 days ago 2.899 GB
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ray-project/base-deps latest f45d66963151 4 days ago 2.649 GB
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ubuntu xenial f49eec89601e 3 weeks ago 129.5 MB
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```
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## Launch Ray in Docker
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Start out by launching the deployment container.
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```
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docker run --shm-size=<shm-size> -t -i ray-project/deploy
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```
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Replace `<shm-size>` with a limit appropriate for your system, for example `512M` or `2G`.
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The `-t` and `-i` options here are required to support interactive use of the container.
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**Note:** Ray requires a **large** amount of shared memory because each object
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store keeps all of its objects in shared memory, so the amount of shared memory
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will limit the size of the object store.
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You should now see a prompt that looks something like:
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```
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root@ebc78f68d100:/ray#
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```
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## Test if the installation succeeded
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To test if the installation was successful, try running some tests. Within the container shell enter the following commands:
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```
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python test/runtest.py # This tests basic functionality.
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python test/array_test.py # This tests some array libraries.
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```
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You are now ready to continue with the [Tutorial](tutorial.md).
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## Running examples in Docker
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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.
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Launch the examples container.
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```
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docker run --shm-size=1024m -t -i ray-project/examples
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```
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### Hyperparameter optimization
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```
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cd /ray/examples/hyperopt/
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python driver.py
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```
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### Batch L-BFGS
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```
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cd /ray/examples/lbfgs/
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python driver.py
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```
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### Learning to play Pong
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```
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cd /ray/examples/rl_pong/
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python driver.py
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```
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