Ray

Ray is an experimental distributed execution framework with a Python-like programming model. It is under development and not ready for general use.

Example Code

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Design Decisions

For a description of our design decisions, see

• Reference Counting
• Aliasing
• Scheduler

Setup

Linux, Mac, and other Unix-based systems

After running these instruction, add the line source "$RAY_ROOT/setup-env.sh" in your ~/.bashrc file manually, where "$RAY_ROOT" is the path of the directory containing setup-env.sh.

1. sudo apt-get update
2. sudo apt-get install git
3. git clone https://github.com/amplab/ray.git
4. cd ray
5. ./setup.sh
6. ./build.sh
7. source setup-env.sh

Windows

Note: A batch file is provided that clones any missing third-party libraries and applies patches to them. Do not attempt to open the solution before the batch file applies the patches; otherwise, if the projects have been modified, the patches may be rejected, and you may be forced to revert your changes before re-running the batch file.

1. Install Microsoft Visual Studio 2015
2. Install Git
3. git clone https://github.com/amplab/ray.git
4. ray\thirdparty\download_thirdparty.bat

Installing Ray on a cluster

These instructions work on EC2, but they may require some modifications to run on your own cluster. In particular, on EC2, running sudo does not require a password, and we currently don't handle the case where a password is needed.

1. Create a file nodes.txt of the IP addresses of the nodes in the cluster. For example

    52.50.28.103
    52.51.210.207
   

2. Make sure that the nodes can all communicate with one another. On EC2, this can be done by creating a new security group and adding the inbound rule "all traffic" and adding the outbound rule "all traffic". Then add all of the nodes in your cluster to that security group.

3. Run something like

   python scripts/cluster.py --nodes nodes.txt \
                             --key-file key.pem \
                             --username ubuntu \
                             --installation-directory /home/ubuntu/
   

where you replace nodes.txt, key.pem, ubuntu, and /home/ubuntu/ by the appropriate values. This assumes that you can connect to each IP address in nodes.txt with the command ssh -i key.pem ubuntu@<ip-address> 4. The previous command should open a Python interpreter. To install Ray on the cluster, run install_ray() in the interpreter. The interpreter should block until the installation has completed. 5. To check that the installation succeeded, you can ssh to each node, cd into the directory ray/test/, and run the tests (e.g., python runtest.py). 6. Now that Ray has been installed, you can start the cluster (the scheduler, object stores, and workers) with the command start_ray("/home/ubuntu/ray/scripts/default_worker.py"), where the argument is the path on each node in the cluster to the worker code that you would like to use. The workers can be restarted with restart_workers("/home/ubuntu/ray/scripts/default_worker.py"), for example if you wish to update the application code running on the workers. The cluster processes (the scheduler, the object stores, and the workers) can be stopped with stop_ray().