ray/doc/source/cluster/ray-client.rst

.. include:: we_are_hiring.rst

.. _ray-client:

Ray Client: Interactive Development
===================================

**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://<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+.** If you're using an older version of ray, see the `1.4.1 docs <https://docs.ray.io/en/releases-1.4.1/cluster/ray-client.html>`_


.. code-block:: python

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

   # Starting the Ray client. This connects to a remote Ray cluster.
   # If you're using a version of Ray prior to 1.5, use the 1.4.1 example
   # instead: https://docs.ray.io/en/releases-1.4.1/cluster/ray-client.html
   ray.init("ray://<head_node_host>:10001")

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

   do_work.remote(2)
   #....

Client arguments
----------------

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

- ``namespace`` (optional): Sets the namespace for the session.
- ``runtime_env`` (optional): Sets the `runtime environment <../ray-core/handling-dependencies.html#runtime-environments>`_ for the session, allowing you to dynamically specify environment variables, packages, local files, and more.

.. code-block:: python

   # 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://1.2.3.4:10001",
       namespace="my_namespace",
       runtime_env={"working_dir": "files/my_project", "pip": ["toolz", "requests"]},
   )
   #....

When to use Ray Client
----------------------

Ray Client should be used when you want to connect a script or an interactive shell session to a **remote** cluster.

* Use ``ray.init("ray://<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 local script or 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("localhost:<port>")`` (non-client connection, local address) if you're developing locally or on the head node of your cluster and you have already started the cluster (i.e. ``ray start --head`` has already been run)
* Use ``ray.init()`` (non-client connection, no address specified) if you're developing locally and want to automatically create a local cluster and attach directly to it OR if you are using Ray Job submission.

.. _how-do-you-use-the-ray-client:

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

.. code-block:: bash

   ray start --head

To start a Ray cluster remotely, you can follow the directions in :ref:`ref-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 <https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/authorizing-access-to-an-instance.html>`_)
or proxying from your local machine to the cluster (on `K8s <https://kubernetes.io/docs/tasks/access-application-cluster/port-forward-access-application-cluster/#forward-a-local-port-to-a-port-on-the-pod>`_).

.. tabbed:: 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:

..
.. code-block:: python

   import ray

   # replace with the appropriate host and port
   ray.init("ray://<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``).

.. code-block:: bash

  $ 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``.

.. code-block:: python

   import ray

   # This will connect to the cluster via the open SSH session.
   ray.init("ray://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``:

.. code-block:: python

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

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


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://...")`` then you may be on a version of Ray prior to 1.5 that does not support starting client connections through ``ray.init``. If this is the case, see the `1.4.1 docs <https://docs.ray.io/en/releases-1.4.1/cluster/ray-client.html>`_ for Ray Client.

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.

..
.. code-block:: python

   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.


.. code-block:: yaml

   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.