This page covers how to start Ray on your single machine or cluster of machines.
Install Ray with
pip install -U ray. For the latest wheels (a snapshot of the
master branch), you can use the instructions at Latest Snapshots (Nightlies).
You can start Ray by calling
ray.init() in your Python script. This will start the local services that Ray uses to schedule remote tasks and actors and then connect to them. Note that you must initialize Ray before any tasks or actors are called (i.e.,
function.remote() will not work until ray.init() is called).
import ray ray.init()
To stop or restart Ray, use
import ray ray.init() ... # ray program ray.shutdown()
To check if Ray is initialized, you can call
import ray ray.init() assert ray.is_initialized() == True ray.shutdown() assert ray.is_initialized() == False
See the Configuration documentation for the various ways to configure Ray.
There are two steps needed to use Ray in a distributed setting:
You must first start the Ray cluster.
You need to add the
ray.init(address=...)). This causes Ray to connect to the existing cluster instead of starting a new one on the local node.
If you have a Ray cluster specification (Using the Ray Cluster Launcher), you can launch a multi-node cluster with Ray initialized on each node with
ray up. From your local machine/laptop:
ray up cluster.yaml
You can monitor the Ray cluster status with
ray monitor cluster.yaml and ssh into the head node with
ray attach cluster.yaml.
Your Python script only needs to execute on one machine in the cluster (usually the head node). To connect your program to the Ray cluster, add the following to your Python script:
ray.init(address...), your Ray program will only be parallelized across a single machine!
You can also use the manual cluster setup (Ray with Cluster Managers) by running initialization commands on each node.
On the head node:
# If the ``--redis-port`` argument is omitted, Ray will choose a port at random. $ ray start --head --redis-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>
This feature is maintained solely to help with debugging, so it’s possible you may encounter some issues. If you do, please file an issue.
By default, Ray will parallelize its workload. However, if you need to debug your Ray program, it may be easier to do everything on a single process. You can force all Ray functions to occur on a single process with
local_mode by calling the following:
Note that some behavior such as setting global process variables may not work as expected.