context
is an optional argument in the constructor of class multiprocessing.pool.Pool
. Documentation only says:
context
can be used to specify the context used for starting the worker processes. Usually a pool is created using the functionmultiprocessing.Pool()
or thePool()
method of a context object. In both cases context is set appropriately.
It doesn't clarify what a "context object" is, why class Pool
constructor needs it, and what it means that it "is set appropriately" in the mentioned scenarios.
context can be used to specify the context used for starting the worker processes. Usually a pool is created using the function multiprocessing. Pool() or the Pool() method of a context object. In both cases context is set appropriately.
Functional Programming in Python In this lesson, you'll dive deeper into how you can use multiprocessing. Pool . It creates multiple Python processes in the background and spreads out your computations for you across multiple CPU cores so that they all happen in parallel without you needing to do anything.
Python multiprocessing Pool can be used for parallel execution of a function across multiple input values, distributing the input data across processes (data parallelism).
The apply_async() function can be called directly to execute a target function in the process pool. The call will not block, but will instead immediately return an AsyncResult object that we can ignore if our function does not return a value.
Depending on the platform, multiprocessing supports three ways to start a process. These start methods are:
spawn:
The parent process starts a fresh python interpreter process.
Available on Unix and Windows. The default on Windows.
fork:
The parent process uses os.fork() to fork the Python interpreter. Available on Unix only. The default on Unix.
forkserver
When the program starts and selects the forkserver start method, a server process is started. From then on, whenever a new process is needed, the parent process connects to the server and requests that it fork a new process. The fork server process is single threaded so it is safe for it to use os.fork(). No unnecessary resources are inherited.
Available on Unix platforms which support passing file descriptors over Unix pipes.
To select a start method you use the set_start_method() in the if __name__ == '__main__'
clause of the main module. For example:
import multiprocessing as mp def foo(q): q.put('hello') if __name__ == '__main__': mp.set_start_method('spawn') q = mp.Queue() p = mp.Process(target=foo, args=(q,)) p.start() print(q.get()) p.join()
Alternatively, you can use get_context() to obtain a context object. Context objects have the same API as the multiprocessing module, and allow one to use multiple start methods in the same program.
import multiprocessing as mp def foo(q): q.put('hello') if __name__ == '__main__': ctx = mp.get_context('spawn') q = ctx.Queue() p = ctx.Process(target=foo, args=(q,)) p.start() print(q.get()) p.join()
This is where the context object came from!
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