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Ray Client

Warning

Ray Client requires pip package ray[client]. If you installed the minimal Ray (e.g. pip install ray), please reinstall by executing pip install ray[client].

What is the Ray Client?

The Ray Client is an API that connects a Python script to a remote Ray cluster. Effectively, it allows you to leverage a remote Ray cluster just like you would with Ray running on your local machine.

By changing ray.init() to ray.init("ray:https://<head_node_host>:<port>"), you can connect from your laptop (or anywhere) directly to a remote cluster and scale-out your Ray code, while maintaining the ability to develop interactively in a Python shell. This will only work with Ray 1.5+.

# You can run this code outside of the Ray cluster!
import ray

# Starting the Ray client. This connects to a remote Ray cluster.
ray.init("ray:https://<head_node_host>:10001")

# Normal Ray code follows
@ray.remote
def do_work(x):
    return x ** x

do_work.remote(2)
#....

When to use Ray Client

Note

Ray Client has architectural limitations and may not work as expected when using Ray for ML workloads (like Ray Tune or Ray Train). Use :ref:`Ray Jobs API<jobs-overview>` for interactive development on ML projects.

Ray Client can be used when you want to connect an interactive Python shell to a remote cluster.

  • Use ray.init("ray:https://<head_node_host>:10001") (Ray Client) if you've set up a remote cluster at <head_node_host> and you want to do interactive work. This will connect your shell to the cluster. See the section on :ref:`using Ray Client<how-do-you-use-the-ray-client>` for more details on setting up your cluster.
  • Use ray.init() (non-client connection, no address specified) if you're developing locally and want to connect to an existing cluster (i.e. ray start --head has already been run), or automatically create a local cluster and attach directly to it. This can also be used for :ref:`Ray Job <jobs-overview>` submission.

Ray Client is useful for developing interactively in a local Python shell. However, it requires a stable connection to the remote cluster and will terminate the workload if the connection is lost for :ref:`more than 30 seconds <client-disconnections>`. If you have a long running workload that you want to run on your cluster, we recommend using :ref:`Ray Jobs <jobs-overview>` instead.

Client arguments

Ray Client is used when the address passed into ray.init is prefixed with ray:https://. Besides the address, Client mode currently accepts two other arguments:

  • namespace (optional): Sets the namespace for the session.
  • runtime_env (optional): Sets the :ref:`runtime environment <runtime-environments>` for the session, allowing you to dynamically specify environment variables, packages, local files, and more.
# Connects to an existing cluster at 1.2.3.4 listening on port 10001, using
# the namespace "my_namespace". The Ray workers will run inside a cluster-side
# copy of the local directory "files/my_project", in a Python environment with
# `toolz` and `requests` installed.
ray.init(
    "ray:https://1.2.3.4:10001",
    namespace="my_namespace",
    runtime_env={"working_dir": "files/my_project", "pip": ["toolz", "requests"]},
)
#....

How do you use the Ray Client?

Step 1: Set up your Ray cluster

If you have a running Ray cluster (version >= 1.5), Ray Client server is likely already running on port 10001 of the head node by default. Otherwise, you'll want to create a Ray cluster. To start a Ray cluster locally, you can run

ray start --head

To start a Ray cluster remotely, you can follow the directions in :ref:`vm-cluster-quick-start`.

If necessary, you can modify the Ray Client server port to be other than 10001, by specifying --ray-client-server-port=... to the ray start :ref:`command <ray-start-doc>`.

Step 2: Check ports

Ensure that the Ray Client port on the head node is reachable from your local machine. This means opening that port up by configuring security groups or other access controls (on EC2) or proxying from your local machine to the cluster (on K8s).

.. tab-set::

    .. tab-item:: AWS

        With the Ray cluster launcher, you can configure the security group
        to allow inbound access by defining :ref:`cluster-configuration-security-group`
        in your `cluster.yaml`.

        .. code-block:: yaml

            # An unique identifier for the head node and workers of this cluster.
            cluster_name: minimal_security_group

            # Cloud-provider specific configuration.
            provider:
                type: aws
                region: us-west-2
                security_group:
                    GroupName: ray_client_security_group
                    IpPermissions:
                          - FromPort: 10001
                            ToPort: 10001
                            IpProtocol: TCP
                            IpRanges:
                                # This will enable inbound access from ALL IPv4 addresses.
                                - CidrIp: 0.0.0.0/0

Step 3: Run Ray code

Now, connect to the Ray Cluster with the following and then use Ray like you normally would:

import ray

# replace with the appropriate host and port
ray.init("ray:https://<head_node_host>:10001")

# Normal Ray code follows
@ray.remote
def do_work(x):
    return x ** x

do_work.remote(2)

#....

Alternative Approach: SSH Port Forwarding

As an alternative to configuring inbound traffic rules, you can also set up Ray Client via port forwarding. While this approach does require an open SSH connection, it can be useful in a test environment where the head_node_host often changes.

First, open up an SSH connection with your Ray cluster and forward the listening port (10001).

$ ray up cluster.yaml
$ ray attach cluster.yaml -p 10001

Then, you can connect to the Ray cluster from another terminal using localhost as the head_node_host.

import ray

# This will connect to the cluster via the open SSH session.
ray.init("ray:https://localhost:10001")

# Normal Ray code follows
@ray.remote
def do_work(x):
    return x ** x

do_work.remote(2)

#....

Connect to multiple Ray clusters (Experimental)

Ray Client allows connecting to multiple Ray clusters in one Python process. To do this, just pass allow_multiple=True to ray.init:

import ray
# Create a default client.
ray.init("ray:https://<head_node_host_cluster>:10001")

# Connect to other clusters.
cli1 = ray.init("ray:https://<head_node_host_cluster_1>:10001", allow_multiple=True)
cli2 = ray.init("ray:https://<head_node_host_cluster_2>:10001", allow_multiple=True)

# Data is put into the default cluster.
obj = ray.put("obj")

with cli1:
    obj1 = ray.put("obj1")

with cli2:
    obj2 = ray.put("obj2")

with cli1:
    assert ray.get(obj1) == "obj1"
    try:
        ray.get(obj2)  # Cross-cluster ops not allowed.
    except:
        print("Failed to get object which doesn't belong to this cluster")

with cli2:
    assert ray.get(obj2) == "obj2"
    try:
        ray.get(obj1)  # Cross-cluster ops not allowed.
    except:
        print("Failed to get object which doesn't belong to this cluster")
assert "obj" == ray.get(obj)
cli1.disconnect()
cli2.disconnect()

When using Ray multi-client, there are some different behaviors to pay attention to:

  • The client won't be disconnected automatically. Call disconnect explicitly to close the connection.
  • Object references can only be used by the client from which it was obtained.
  • ray.init without allow_multiple will create a default global Ray client.

Things to know

Client disconnections

When the client disconnects, any object or actor references held by the server on behalf of the client are dropped, as if directly disconnecting from the cluster.

If the client disconnects unexpectedly, i.e. due to a network failure, the client will attempt to reconnect to the server for 30 seconds before all of the references are dropped. You can increase this time by setting the environment variable RAY_CLIENT_RECONNECT_GRACE_PERIOD=N, where N is the number of seconds that the client should spend trying to reconnect before giving up.

Versioning requirements

Generally, the client Ray version must match the server Ray version. An error will be raised if an incompatible version is used.

Similarly, the minor Python (e.g., 3.6 vs 3.7) must match between the client and server. An error will be raised if this is not the case.

Starting a connection on older Ray versions

If you encounter socket.gaierror: [Errno -2] Name or service not known when using ray.init("ray:https://...") then you may be on a version of Ray prior to 1.5 that does not support starting client connections through ray.init.

Connection through the Ingress

If you encounter the following error message when connecting to the Ray Cluster using an Ingress, it may be caused by the Ingress's configuration.

grpc._channel._MultiThreadedRendezvous: <_MultiThreadedRendezvous of RPC that terminated with:
    status = StatusCode.INVALID_ARGUMENT
    details = ""
    debug_error_string = "{"created":"@1628668820.164591000","description":"Error received from peer ipv4:10.233.120.107:443","file":"src/core/lib/surface/call.cc","file_line":1062,"grpc_message":"","grpc_status":3}"
>
Got Error from logger channel -- shutting down: <_MultiThreadedRendezvous of RPC that terminated with:
    status = StatusCode.INVALID_ARGUMENT
    details = ""
    debug_error_string = "{"created":"@1628668820.164713000","description":"Error received from peer ipv4:10.233.120.107:443","file":"src/core/lib/surface/call.cc","file_line":1062,"grpc_message":"","grpc_status":3}"
>

If you are using the nginx-ingress-controller, you may be able to resolve the issue by adding the following Ingress configuration.

metadata:
  annotations:
     nginx.ingress.kubernetes.io/server-snippet: |
       underscores_in_headers on;
       ignore_invalid_headers on;

Ray client logs

Ray client logs can be found at /tmp/ray/session_latest/logs on the head node.

Uploads

If a working_dir is specified in the runtime env, when running ray.init() the Ray client will upload the working_dir on the laptop to /tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents>.

Ray workers are started in the /tmp/ray/session_latest/runtime_resources/_ray_pkg_<hash of directory contents> directory on the cluster. This means that relative paths in the remote tasks and actors in the code will work on the laptop and on the cluster without any code changes. For example, if the working_dir on the laptop contains data.txt and run.py, inside the remote task definitions in run.py one can just use the relative path "data.txt". Then python run.py will work on my laptop, and also on the cluster. As a side note, since relative paths can be used in the code, the absolute path is only useful for debugging purposes.

Troubleshooting

Error: Attempted to reconnect a session that has already been cleaned up

This error happens when Ray Client reconnects to a head node that does not recognize the client. This can happen if the head node restarts unexpectedly and loses state. On Kubernetes, this can happen if the head pod restarts after being evicted or crashing.