These are some Frequently Asked Questions that we’ve seen pop up for using Ray clusters. If you still have questions after reading this FAQ, please reach out on our Discourse!

Do Ray clusters support multi-tenancy?#

Yes, you can run multiple jobs from different users simultaneously in a Ray cluster but it’s NOT recommended in production. The reason is that Ray currently still misses some features for multi-tenancy in production:

  • Ray doesn’t provide strong resource isolation: Ray resources are logical and they don’t limit the physical resources a task or actor can use while running. This means simultaneous jobs can interfere with each other and makes them less reliable to run in production.

  • Ray doesn’t support priorities: All jobs, tasks and actors have the same priority so there is no way to prioritize important jobs under load.

  • Ray doesn’t support access control: jobs have full access to a Ray cluster and all of the resources within it.

On the other hand, you can run the same job multiple times using the same cluster to save the cluster startup time.


A Ray namespace is just a logical grouping of jobs and named actors. Unlike a Kubernetes namespace, it doesn’t provide any other multi-tenancy functions like resource quotas.

I have multiple Ray users. What’s the right way to deploy Ray for them?#

It’s recommended to start a Ray cluster for each user so that their workloads are isolated.

What is the difference between --node-ip-address and --address?#

When starting a head node on a machine with more than one network address, you may need to specify the externally-available address so worker nodes can connect. This is done with:

ray start --head --node-ip-address xx.xx.xx.xx --port nnnn``

Then when starting the worker node, use this command to connect to the head node:

ray start --address xx.xx.xx.xx:nnnn

What does a worker node failure to connect look like?#

If the worker node cannot connect to the head node, you should see this error

Unable to connect to GCS at xx.xx.xx.xx:nnnn. Check that (1) Ray GCS with matching version started successfully at the specified address, and (2) there is no firewall setting preventing access.

The most likely cause is that the worker node cannot access the IP address given. You can use ip route get xx.xx.xx.xx on the worker node to start debugging routing issues.

You may also see failures in the log like

This node has an IP address of xx.xx.xx.xx, while we can not found the matched Raylet address. This maybe come from when you connect the Ray cluster with a different IP address or connect a container.

which can be caused by overloading the head node with too many simultaneous connections. The solution for this is to start the worker nodes more slowly.

I am having problems getting my SLURM cluster to work#

There seem to be a class of issues starting ray on SLURM clusters. While we have not been able to pin down the exact causes (as of June 2023), work has been done to mitigate some of the resource contention. Some of the issues reported:

  • Using a machine with a large number of CPUs, and starting one worker per CPU together with OpenBLAS (as used in NumPy) may allocate too many threads. This is an known OpenBLAS limitation and can be mitigated by limiting OpenBLAS to one thread per process as explained in the link.

  • Resource allocation is not what was expected: usually too many CPUs per node were allocated. Best practice is to verify your SLURM configuration without starting ray to verify that the allocations are as expected. For more detailed information see Deploying on Slurm.