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using-ray-on-a-cluster.md

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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.

Launching a cluster on EC2

This section describes how to start a cluster on EC2. These instructions are copied and adapted from https://github.com/amplab/spark-ec2.

Before you start

  • Create an Amazon EC2 key pair for yourself. This can be done by logging into your Amazon Web Services account through the AWS console, clicking Key Pairs on the left sidebar, and creating and downloading a key. Make sure that you set the permissions for the private key file to 600 (i.e. only you can read and write it) so that ssh will work.
  • Whenever you want to use the ec2.py script, set the environment variables AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY to your Amazon EC2 access key ID and secret access key. These can be obtained from the AWS homepage by clicking Account > Security Credentials > Access Credentials.

Launching a Cluster

  • Go into the ray/scripts directory.

  • Run python ec2.py -k <keypair> -i <key-file> -s <num-slaves> launch <cluster-name>, where <keypair> is the name of your EC2 key pair (that you gave it when you created it), <key-file> is the private key file for your key pair, <num-slaves> is the number of slave nodes to launch (try 1 at first), and <cluster-name> is the name to give to your cluster.

    For example:

    export AWS_SECRET_ACCESS_KEY=AaBbCcDdEeFGgHhIiJjKkLlMmNnOoPpQqRrSsTtU
export AWS_ACCESS_KEY_ID=ABCDEFG1234567890123
python ec2.py --key-pair=awskey --identity-file=awskey.pem --region=us-west-1 launch my-ray-cluster

The following options are worth pointing out:

  • --instance-type=<instance-type> can be used to specify an EC2 instance type to use. For now, the script only supports 64-bit instance types, and the default type is m3.large (which has 2 cores and 7.5 GB RAM).
  • --region=<ec2-region> specifies an EC2 region in which to launch instances. The default region is us-east-1.
  • --zone=<ec2-zone> can be used to specify an EC2 availability zone to launch instances in. Sometimes, you will get an error because there is not enough capacity in one zone, and you should try to launch in another.
  • --spot-price=<price> will launch the worker nodes as Spot Instances, bidding for the given maximum price (in dollars).

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. If you launched a cluster using the ec2.py script from the previous section, then the file ray/scripts/nodes.txt will already have been created. Otherwise, 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.

  1. 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.

  2. 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 cluster.install_ray() in the interpreter. The interpreter should block until the installation has completed. The standard output from the nodes will be redirected to your terminal. 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. Start the cluster (the scheduler, object stores, and workers) with the command cluster.start_ray("~/example_ray_code"), where the argument is the local path to the directory that contains your Python code. This command will copy this source code to each node and will start the cluster. Each worker that is started will have a local copy of the ~/example_ray_code directory in their PYTHONPATH. After completing successfully, you can connect to the ssh to the head node and attach a shell to the cluster by first running the following code. cd "$RAY_HOME/../user_source_files/example_ray_code"; source "$RAY_HOME/setup-env.sh"; Then within Python, run the following. python import ray ray.init(scheduler_address="12.34.56.789:10001", objstore_address="12.34.56.789:20001", driver_address="12.34.56.789:30001")

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

    • cluster.install_ray() - This pulls the Ray source code on each node, builds all of the third party libraries, and builds the project itself.
    • cluster.start_ray(user_source_directory, 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.
    • cluster.stop_ray() - This shuts down the cluster (killing all of the processes).
    • cluster.update_ray() - This pulls the latest Ray source code and builds it.

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.