Distributed Ray Overview

One of Ray’s strengths is the ability to leverage multiple machines in the same program. Ray can, of course, be run on a single machine (and is done so often) but the real power is using Ray on a cluster of machines.

Key Concepts

  • Ray Nodes: A Ray cluster consists of a head node and a set of worker nodes. The head node needs to be started first, and the worker nodes are given the address of the head node to form the cluster. The Ray cluster itself can also “auto-scale,” meaning that it can interact with a Cloud Provider to request or release instances according to application workload.

  • Ports: Ray processes communicate via TCP ports. When starting a Ray cluster, either on prem or on the cloud, it is important to open the right ports so that Ray functions correctly. See the Ray Ports documentation for more details.

  • Ray Cluster Launcher: The Ray Cluster Launcher is a simple tool that automatically provisions machines and launches a multi-node Ray cluster. You can use the cluster launcher on GCP, Amazon EC2, Azure, or even Kubernetes.


Clusters are started with the Ray Cluster Launcher or manually.

You can also create a Ray cluster using a standard cluster manager such as Kubernetes, YARN, or SLURM.

After a cluster is started, you need to connect your program to the Ray cluster by starting a driver process on the same node as where you ran ray start:

# This must
import ray

and then the rest of your script should be able to leverage Ray as a distributed framework!

Using the cluster launcher

The ray up command uses the Ray Cluster Launcher to start a cluster on the cloud, creating a designated “head node” and worker nodes. Any Python process that runs ray.init(address=...) on any of the cluster nodes will connect to the ray cluster.


Calling ray.init on your laptop will not work if using ray up, since your laptop will not be the head node.

Here is an example of using the Cluster Launcher on AWS:

# First, run `pip install boto3` and `aws configure`
# Create or update the cluster. When the command finishes, it will print
# out the command that can be used to SSH into the cluster head node.
$ ray up ray/python/ray/autoscaler/aws/example-full.yaml

You can monitor the Ray cluster status with ray monitor cluster.yaml and ssh into the head node with ray attach cluster.yaml.

Manual Ray Cluster Setup

The most preferable way to run a Ray cluster is via the Ray Cluster Launcher. However, it is also possible to start a Ray cluster by hand.

This section assumes that you have a list of machines and that the nodes in the cluster can communicate with each other. It also assumes that Ray is installed on each machine. To install Ray, follow the installation instructions.

Starting Ray on each machine

On the head node (just choose some node to be the head node), run the following. If the --port argument is omitted, Ray will choose port 6379, falling back to a random port.

$ ray start --head --port=6379
Next steps
  To connect to this Ray runtime from another node, run
    ray start --address='<ip address>:6379' --redis-password='<password>'

If connection fails, check your firewall settings and network configuration.

The command will print out the address of the Redis server that was started (the local node IP address plus the port number you specified).

Then on each of the other nodes, run the following. Make sure to replace <address> with the value printed by the command on the head node (it should look something like

Note that if your compute nodes are on their own subnetwork with Network Address Translation, to connect from a regular machine outside that subnetwork, the command printed by the head node will not work. You need to find the address that will reach the head node from the second machine. If the head node has a domain address like compute04.berkeley.edu, you can simply use that in place of an IP address and rely on the DNS.

$ ray start --address=<address> --redis-password='<password>'
Ray runtime started.

To terminate the Ray runtime, run
  ray stop

If you wish to specify that a machine has 10 CPUs and 1 GPU, you can do this with the flags --num-cpus=10 and --num-gpus=1. See the Configuration page for more information.

If you see Unable to connect to Redis. If the Redis instance is on a different machine, check that your firewall is configured properly., this means the --port is inaccessible at the given IP address (because, for example, the head node is not actually running Ray, or you have the wrong IP address).

If you see Ray runtime started., then the node successfully connected to the IP address at the --port. You should now be able to connect to the cluster with ray.init(address='auto').

If ray.init(address='auto') keeps repeating redis_context.cc:303: Failed to connect to Redis, retrying., then the node is failing to connect to some other port(s) besides the main port.

If connection fails, check your firewall settings and network configuration.

If the connection fails, to check whether each port can be reached from a node, you can use a tool such as nmap or nc.

$ nmap -sV --reason -p $PORT $HEAD_ADDRESS
Nmap scan report for compute04.berkeley.edu (123.456.78.910)
Host is up, received echo-reply ttl 60 (0.00087s latency).
rDNS record for 123.456.78.910: compute04.berkeley.edu
6379/tcp open  redis   syn-ack ttl 60 Redis key-value store
Service detection performed. Please report any incorrect results at https://nmap.org/submit/ .
$ nc -vv -z $HEAD_ADDRESS $PORT
Connection to compute04.berkeley.edu 6379 port [tcp/*] succeeded!

If the node cannot access that port at that IP address, you might see

$ nmap -sV --reason -p $PORT $HEAD_ADDRESS
Nmap scan report for compute04.berkeley.edu (123.456.78.910)
Host is up (0.0011s latency).
rDNS record for 123.456.78.910: compute04.berkeley.edu
6379/tcp closed redis   reset ttl 60
Service detection performed. Please report any incorrect results at https://nmap.org/submit/ .
$ nc -vv -z $HEAD_ADDRESS $PORT
nc: connect to compute04.berkeley.edu port 6379 (tcp) failed: Connection refused

Stopping Ray

When you want to stop the Ray processes, run ray stop on each node.

Running a Ray program on the Ray cluster

To run a distributed Ray program, you’ll need to execute your program on the same machine as one of the nodes.

Within your program/script, you must call ray.init and add the address parameter to ray.init (like ray.init(address=...)). This causes Ray to connect to the existing cluster. For example:



A common mistake is setting the address to be a cluster node while running the script on your laptop. This will not work because the script needs to be started/executed on one of the Ray nodes.

To verify that the correct number of nodes have joined the cluster, you can run the following.

import time

def f():
    return ray.services.get_node_ip_address()

# Get a list of the IP addresses of the nodes that have joined the cluster.
set(ray.get([f.remote() for _ in range(1000)]))