Now I'm developing C# app running on Windows. Some of processes are written in Python, that called via pythonnet (Python for .NET). The processes are calculation-heavy, so I want to do them in parallel.
They are CPU-bounded and can be handled independently.
As far as I know, there are 2 possible ways to realize it:
Launch multiple Python runtime
The first way is launching multiple Python interpreters but it seems unfeasible. Because pythonnet aparently can manage only one interpreter that initialialized by static method, PythonEngine.Initialize().
From the Python.NET documentation:Important Note for embedders: Python is not free-threaded and uses a global interpreter lock to allow multi-threaded applications to interact safely with the Python interpreter. Much more information about this is available in the Python C-API documentation on the www.python.org Website.
When embedding Python in a managed application, you have to manage the GIL in just the same way you would when embedding Python in a C or C++ application.
Before interacting with any of the objects or APIs provided by the Python.Runtime namespace, calling code must have acquired the Python global interpreter lock by calling the PythonEngine.AcquireLock method. The only exception to this rule is the PythonEngine.Initialize method, which may be called at startup without having acquired the GIL.Use multiprocessing package in Python
The other way is using multiprocessing package. According to Python documentation, following statement is necessary if the code runs on Windows to ensure spawn finite process:
if __name__ == "__main__":
However, the function written in Python is taken as a part of module since it's embedded to .NET.
For example, following code is executable, but spawns processes infinitely.
//C#
static void Main(string[] args)
{
using (Py.GIL())
{
PythonEngine.Exec(
"print(__name__)\n" + //output is "buitlins"
"if __name__ == 'builtins':\n" +
" import test_package\n" + //import Python code below
" test_package.async_test()\n"
);
}
}
# Python
import concurrent.futures
def heavy_calc(x):
for i in range(int(1e7) * x):
i*2
def async_test():
# multiprocessing
with concurrent.futures.ProcessPoolExecutor(max_workers=8) as executor:
futures = [executor.submit(heavy_calc,x) for x in range(10)]
(done, notdone) = concurrent.futures.wait(futures)
for future in futures:
print(future.result())
Is there good idea to solve above problem? Any comments would be appreciated. Thanks in advance.
