Ray Jobs Overview#
Ray Jobs API#
The recommended way to run a job on a Ray cluster is to use the Ray Jobs API, which consists of a CLI tool, Python SDK, and a REST API.
The Ray Jobs API allows you to submit locally developed applications to a remote Ray Cluster for execution. It simplifies the experience of packaging, deploying, and managing a Ray application.
A submission to the Ray Jobs API consists of:
An entrypoint command, like
python my_script.py, and
A runtime environment, which specifies the application’s file and package dependencies.
A job can be submitted by a remote client that lives outside of the Ray Cluster. We will show this workflow in the following user guides.
After a job is submitted, it runs once to completion or failure, regardless of the original submitter’s connectivity. Retries or different runs with different parameters should be handled by the submitter. Jobs are bound to the lifetime of a Ray cluster, so if the cluster goes down, all running jobs on that cluster will be terminated.
To get started with the Ray Jobs API, check out the quickstart guide, which walks you through the CLI tools for submitting and interacting with a Ray Job. This is suitable for any client that can communicate over HTTP to the Ray Cluster. If needed, the Ray Jobs API also provides APIs for programmatic job submission and job submission using REST.
Running Jobs Interactively#
If you would like to run an application interactively and see the output in real time (for example, during development or debugging), you can:
(Recommended) Run your script directly on a cluster node (e.g. after SSHing into the node using
ray attach), or
(For Experts only) Use Ray Client to run a script from your local machine while maintaining a connection to the cluster.
Note that jobs started in these ways are not managed by the Ray Jobs API, so the Ray Jobs API will not be able to see them or interact with them (with the exception of
ray job list and