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.
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.
After a cluster is started, you need to connect your program to the Ray cluster.
and then the rest of your script should be able to leverage Ray as a distributed framework!
Using the cluster launcher¶
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.
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.
To configure the Ray cluster to run Java code, you need to add the
--code-search-path option. See Code Search Path for more details.
Starting Ray on each machine¶
On the head node (just choose some node to be the head node), run the following.
--port argument is omitted, Ray will choose port 6379, falling back to a
ray start --head --port=6379
The command will print out the address of the Redis server that was started (and some other address information).
Then on all 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
ray start --address=<address>
If you wish to specify that a machine has 10 CPUs and 1 GPU, you can do this
with the flags
--num-gpus=1. See the Configuration page for more information.
Now we’ve started the Ray runtime.
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.
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 @ray.remote def f(): time.sleep(0.01) 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)]))