ray/doc/using-ray-on-a-cluster.md

Using Ray on a cluster

Running Ray on a cluster is still experimental.

Ray can be used in several ways. In addition to running on a single machine, Ray is designed to run on a cluster of machines. This document is about how to use Ray on a cluster.

Getting started with 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

    12.34.56.789
    12.34.567.89
   

The first node in the file is the "head" node. The scheduler will be started on the head node, and the driver should run on the head node as well.

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 <ip-address> in nodes.txt with the command ssh -i <key-file> <username>@<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 second argument is the path on each node in the cluster to the worker code that you would like to use. After completing successfully, this command will print out a command that can be run on the head node to attach a shell (the driver) to the cluster. For example,

```
source "$RAY_HOME/setup-env.sh";
python "$RAY_HOME/scripts/shell.py" --scheduler-address=52.50.28.103:10001 --objstore-address=52.50.28.103:20001 --worker-address=52.50.28.103:30001 --attach
```

7. Note that there are several more commands that can be run from within cluster.py.

   • install_ray() - This pulls the Ray source code on each node, builds all of the third party libraries, and builds the project itself.
   • start_ray(worker_path, num_workers_per_node=10) - This starts a scheduler process on the head node, and it starts an object store and some workers on each node.
   • stop_ray() - This shuts down the cluster (killing all of the processes).
   • restart_workers(worker_path, num_workers_per_node=10) - This kills the current workers and starts new workers using the worker code from the given file. Currently, this can only run when there are no tasks currently executing on any of the workers.
   • update_ray() - This pulls the latest Ray source code and builds it.

Running Ray on a cluster

Once you've started a Ray cluster using the above instructions, to actually use Ray, ssh to the head node (the first node listed in the nodes.txt file) and attach a shell to the already running cluster.