This page covers how to start Ray on your single machine or cluster of machines.
Be sure to have installed Ray before following the instructions on this page.
What is the Ray runtime?¶
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:
Starting Ray on a single machine¶
ray.init() (without any
address args) starts a Ray runtime on your laptop/machine. This laptop/machine becomes the “head node”.
You must initialize Ray before any tasks or actors are called.
When the process calling
ray.init() terminates, the Ray runtime will also terminate. To explicitly stop or restart Ray, use the shutdown API.
See the Configuration documentation for the various ways to configure Ray.
Starting Ray via the CLI (
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: 18.104.22.168 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 other nodes to the head node, creating a Ray cluster by also calling
ray start on those nodes. See Manual Ray Cluster Setup for more details. Calling
ray.init(address="auto") on any of the cluster machines will connect to the ray cluster.
Launching a Ray cluster (
Ray clusters can be launched with 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. 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.
To connect to the existing cluster, similar to the method outlined in Starting Ray via the CLI (ray start), you must call
ray.init and specify the address of the Ray cluster when initializing Ray in your code. This allows Ray to connect to the cluster.
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.
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 and run tasks on multiple processes and multiple nodes. 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 by enabling local mode as the following:
Note that there are some known issues with local mode. Please read these tips for more information.