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.

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 generated from the AWS homepage by clicking My Account > Security Credentials > Access Keys, or by creating an IAM user.

Launching a Cluster

• Install the required dependencies on the machine you will be using to run the cluster launch scripts.

  sudo pip install --upgrade boto
  

• 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 \
                --instance-type=c4.4xlarge \
                --spot-price=2.50 \
                --slaves=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).
• --slaves=<num-slaves> will launch a cluster with (1 + num_slaves) instances. The first instance is the head node, which in addition to hosting workers runs the Ray scheduler and application driver programs.

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. If the nodes have public and private IP addresses (as in the case of EC2 instances), you can list the <public-ip-address>, <private-ip-address> in nodes.txt like

    12.34.56.789, 98.76.54.321
    12.34.567.89, 98.76.543.21

The cluster.py administrative script will use the public IP addresses to ssh to the nodes. Ray will use the private IP addresses to send messages between the nodes during execution.

2. Make sure that the nodes can all communicate with one another. On EC2, this can be done by creating a new security group with the appropriate inbound and outbound rules and adding all of the nodes in your cluster to that security group. This is done automatically by the ec2.py script. If you have used the ec2.py script you can log into the hosts with the username ubuntu.

3. From the ray/scripts directory, run something like

   python cluster.py --nodes=nodes.txt \
                     --key-file=awskey.pem \
                     --username=ubuntu
   

where you replace nodes.txt, key.pem, and 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 and run the tests. cd $HOME/ray/ source setup-env.sh # Add Ray to your Python path. python test/runtest.py # This tests basic functionality. python test/array_test.py # This tests some array libraries.

6. Start the cluster with cluster.start_ray(). The cluster.start_ray command will start the Ray scheduler, object stores, and workers, and before finishing it will print instructions for connecting to the cluster via ssh.

7. To connect to the cluster (either with a Python shell or with a script), ssh to the cluster's head node (as described by the output of the cluster.start_ray command. E.g.,

   The cluster has been started. You can attach to the cluster by sshing to the head node with the following command.
   
       ssh -i awskey.pem [email protected]
   
   Then run the following commands.
   
       source $HOME/ray/setup-env.sh  # Add Ray to your Python path.
   
   Then within a Python interpreter, run the following commands.
   
       import ray
       ray.init(node_ip_address="98.76.54.321", scheduler_address="98.76.54.321:10001")
   

8. If you would like to run the example applications on the cluster. You will need to install a few more Python packages. This can be done, within cluster.py, by running the following.

   install_example_dependencies_command = """
     # Install TensorFlow
     sudo pip install --upgrade https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-0.9.0-cp27-none-linux_x86_64.whl;
     # Install SciPy
     sudo apt-get -y install libatlas-base-dev gfortran;
     sudo pip install scipy;
     # Install Gym
     sudo apt-get -y install zlib1g-dev libjpeg-dev xvfb libav-tools xorg-dev python-opengl libsdl2-dev swig wget;
     sudo pip install gym[atari]
   """
   cluster.run_command_over_ssh_on_all_nodes_in_parallel(install_example_dependencies_command)

9. 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(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.copy_code_to_cluster(user_source_directory) - This copies the contents of user_source_directory locally to the cluster under ~/ray_source_files/.
   • cluster.update_ray() - This pulls the latest Ray source code and builds it.
   • cluster.run_command_over_ssh_on_all_nodes_in_parallel(command) - This will ssh to each node in the cluster and run a command.