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## Multiprocessing with gRPC Python
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Multiprocessing allows application developers to sidestep the Python global
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interpreter lock and achieve true concurrency on multicore systems.
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Unfortunately, using multiprocessing and gRPC Python is not yet as simple as
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instantiating your server with a `futures.ProcessPoolExecutor`.
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The library is implemented as a C extension, maintaining much of the state that
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drives the system in native code. As such, upon calling
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[`fork`](http://man7.org/linux/man-pages/man2/fork.2.html), much of the
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state copied into the child process is invalid, leading to hangs and crashes.
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However, calling `fork` without `exec` in your python process is supported
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*before* any gRPC servers have been instantiated. Application developers can
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take advantage of this to parallelize their CPU-intensive operations.
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## Running the Example
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This example calculates the first 10,000 prime numbers as an RPC. We instantiate
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one server per subprocess, balancing requests between the servers using the
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[`SO_REUSEPORT`](https://lwn.net/Articles/542629/) socket option.
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To run the server,
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[ensure `bazel` is installed](https://docs.bazel.build/versions/master/install.html)
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and run:
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```
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bazel run //examples/python/multiprocessing:server &
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```
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Note the address at which the server is running. For example,
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```
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...
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[PID 107153] Binding to '[::]:33915'
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[PID 107507] Starting new server.
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[PID 107508] Starting new server.
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...
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```
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Now, start the client by running
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```
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bazel run //examples/python/multiprocessing:client -- [SERVER_ADDRESS]
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```
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For example,
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```
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bazel run //examples/python/multiprocessing:client -- [::]:33915
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```
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