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https://github.com/vale981/ray
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69 lines
2.4 KiB
ReStructuredText
69 lines
2.4 KiB
ReStructuredText
**********
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Ray Client
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**********
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.. note::
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This feature is still in beta and subject to changes.
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===========
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Basic usage
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===========
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While in beta, the server is available as an executable module. To start the server, run
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``python -m ray.util.client.server [--host host_ip] [--port port] [--redis-address address] [--redis-password password]``
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This runs ``ray.init()`` with default options and exposes the client gRPC port at ``host_ip:port`` (by default, ``0.0.0.0:50051``). Providing ``redis-address`` and ``redis-password`` will be passed into ``ray.init()`` when the server starts, allowing connection to an existing Ray cluster, as per the `cluster setup <cluster/index.html>`_ instructions.
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From here, another Ray script can access that server from a networked machine with ``ray.util.connect()``
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.. code-block:: python
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import ray
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import ray.util
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ray.util.connect("0.0.0.0:50051") # replace with the appropriate host and port
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# Normal Ray code follows
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@ray.remote
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def f(x):
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return x ** x
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do_work.remote(2)
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#....
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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
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===================
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``RAY_CLIENT_MODE``
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===================
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Because Ray client mode affects the behavior of the Ray API, larger scripts or libraries imported before ``ray.util.connect()`` may not realize they're in client mode. This feature is being tracked with `issue #13272 <https://github.com/ray-project/ray/issues/13272>`_ but the workaround here is provided for beta users.
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One option is to defer the imports from a ``main`` script that calls ``ray.util.connect()`` first. However, some older scripts or libraries might not support that.
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Therefore, an environment variable is also available to force a Ray program into client mode: ``RAY_CLIENT_MODE`` An example usage:
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.. code-block:: bash
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RAY_CLIENT_MODE=1 python my_ray_program.py
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===================================
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Programatically creating the server
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===================================
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For larger use-cases, it may be desirable to connect remote Ray clients to an existing Ray environment. The server can be started separately via
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.. code-block:: python
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from ray.util.client.server import serve
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server = serve("0.0.0.0:50051")
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# Server does some work
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# ...
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# Time to clean up
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server.stop(0)
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