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Using Ray on a cluster
Deploying Ray on a cluster currently requires a bit of manual work.
Deploying Ray on a cluster.
This section assumes that you have a cluster running and that the node in the cluster can communicate with each other. It also assumes that Ray is installed on each machine. To install Ray, follow the instructions for installation on Ubuntu.
Starting Ray on each machine.
On the head node (just choose some node to be the head node), run the following,
replacing <redis-port> with a port of your choice, e.g., 6379.
./ray/scripts/start_ray.sh --head --redis-port <redis-port>
The --redis-port arugment is optional, and if not provided Ray starts Redis
on a port selected at random.
In either case, 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
<redis-address> with the value printed by the command on the head node (it
should look something like 123.45.67.89:6379).
./ray/scripts/start_ray.sh --redis-address <redis-address>
To specify the number of processes to start, use the flag --num-workers, as
follows:
./ray/scripts/start_ray.sh --num-workers <int>
Now we've started all of the Ray processes on each node Ray. This includes
- Some worker processes on each machine.
- An object store on each machine.
- A local scheduler on each machine.
- One Redis server (on the head node).
- One global scheduler (on the head node).
- Optionally, this may start up some processes for visualizing the system state through a web UI.
To run some commands, start up Python on one of the nodes in the cluster, and do the following.
import ray
ray.init(redis_address="<redis-address>")
Now you can define remote functions and execute tasks. For example:
@ray.remote
def f(x):
return x
ray.get([f.remote(f.remote(f.remote(0))) for _ in range(1000)])
Stopping Ray
When you want to stop the Ray processes, run ./ray/scripts/stop_ray.sh
on each node.