This page covers how to start Ray on your single machine or cluster of machines.
Tip
Be sure to have installed Ray <installation>
before following the instructions on this page.
Ray programs are able to parallelize and distribute by leveraging an underlying Ray runtime. The Ray runtime consists of multiple services/processes started in the background for communication, data transfer, scheduling, and more. The Ray runtime can be started on a laptop, a single server, or multiple servers.
There are three ways of starting the Ray runtime:
- Implicitly via
ray.init()
(start-ray-init
) - Explicitly via CLI (
start-ray-cli
) - Explicitly via the cluster launcher (
start-ray-up
)
In all cases, ray.init()
will try to automatically find a Ray instance to connect to. It checks, in order: 1. The RAY_ADDRESS
OS environment variable. 2. The concrete address passed to ray.init(address=<address>)
. 3. If no address is provided, the latest Ray instance that was started on the same machine using ray start
.
Calling ray.init()
starts a local Ray instance on your laptop/machine. This laptop/machine becomes the "head node".
Note
In recent versions of Ray (>=1.5), ray.init()
will automatically be called on the first use of a Ray remote API.
Python
import ray ray.shutdown()
import ray # Other Ray APIs will not work until ray.init() is called. ray.init()
Java
import io.ray.api.Ray;
public class MyRayApp {
public static void main(String[] args) {
// Other Ray APIs will not work until `Ray.init()` is called.
Ray.init();
...
}
}
When the process calling ray.init()
terminates, the Ray runtime will also terminate. To explicitly stop or restart Ray, use the shutdown API.
Python
ray.shutdown()
import ray ray.init() ... # ray program ray.shutdown()
Java
import io.ray.api.Ray;
public class MyRayApp {
public static void main(String[] args) {
Ray.init();
... // ray program
Ray.shutdown();
}
}
To check if Ray is initialized, use the is_initialized
API.
Python
import ray ray.init() assert ray.is_initialized()
ray.shutdown() assert not ray.is_initialized()
Java
import io.ray.api.Ray;
public class MyRayApp {
public static void main(String[] args) {
Ray.init();
Assert.assertTrue(Ray.isInitialized());
Ray.shutdown();
Assert.assertFalse(Ray.isInitialized());
}
}
See the Configuration documentation for the various ways to configure Ray.
Use ray start
from the CLI to start a 1 node ray runtime on a machine. This machine becomes the "head node".
$ ray start --head --port=6379
Local node IP: 192.123.1.123
2020-09-20 10:38:54,193 INFO services.py:1166 -- View the Ray dashboard at http://localhost:8265
--------------------
Ray runtime started.
--------------------
...
You can connect to this Ray instance by starting a driver process on the same node as where you ran ray start
. ray.init()
will now automatically connect to the latest Ray instance.
Python
import ray ray.init()
java
import io.ray.api.Ray;
public class MyRayApp {
public static void main(String[] args) {
Ray.init();
...
}
}
java -classpath <classpath> \
-Dray.address=<address> \
<classname> <args>
C++
RAY_ADDRESS=<address> ./<binary> <args>
You can connect other nodes to the head node, creating a Ray cluster by also calling ray start
on those nodes. See on-prem
for more details. Calling ray.init()
on any of the cluster machines will connect to the same Ray cluster.
Ray clusters can be launched with the Cluster Launcher <cluster-index>
. The ray up
command uses the Ray cluster launcher to start a cluster on the cloud, creating a designated "head node" and worker nodes. Underneath the hood, it automatically calls ray start
to create a Ray cluster.
Your code only needs to execute on one machine in the cluster (usually the head node). Read more about running programs on a Ray cluster <cluster-index>
.
To connect to the Ray cluster, call ray.init
from one of the machines in the cluster. This will connect to the latest Ray cluster:
ray.shutdown()
ray.init()
Note that the machine calling ray up
will not be considered as part of the Ray cluster, and therefore calling ray.init
on that same machine will not attach to the cluster.
Check out our Deployment section for more information on deploying Ray in different settings, including Kubernetes, YARN, and SLURM.