loky 1.1.3

A robust implementation of concurrent.futures.ProcessPoolExecutor

# Reusable Process Pool Executor [](https://travis-ci.org/tomMoral/loky) [](https://ci.appveyor.com/project/tomMoral/loky/branch/master) [](https://codecov.io/gh/tomMoral/loky)


### Goal

The aim of this project is to provide a robust, cross-platform and
cross-version implementation of the `ProcessPoolExecutor` class of
`concurrent.futures`. It notably features:

* __Deadlock free implementation__: one of the major concern in
standard `multiprocessing` and `concurrent.futures` libraries is the
ability of the `Pool/Executor` to handle crashes of worker
processes. This library intends to fix those possible deadlocks and
send back meaningful errors.

* __Consistent spawn behavior__: All processes are started using
fork/exec on POSIX systems. This ensures safer interactions with
third party libraries.

* __Reusable executor__: strategy to avoid respawning a complete
executor every time. A singleton executor instance can be reused (and
dynamically resized if necessary) across consecutive calls to limit
spawning and shutdown overhead. The worker processes can be shutdown
automatically after a configurable idling timeout to free system
resources.

* __Transparent cloudpickle integration__: to call interactively
defined functions and lambda expressions in parallel. It is also
possible to register a custom pickler implementation to handle
inter-process communications.

* __No need for ``if __name__ == "__main__":`` in scripts__: thanks
to the use of ``cloudpickle`` to call functions defined in the
``__main__`` module, it is not required to protect the code calling
parallel functions under Windows.

### Usage

```python
import os
from time import sleep
from loky import get_reusable_executor


def say_hello(k):
pid = os.getpid()
print("Hello from {} with arg {}".format(pid, k))
sleep(.01)
return pid


# Create an executor with 4 worker processes, that will
# automatically shutdown after idling for 2s
executor = get_reusable_executor(max_workers=4, timeout=2)

res = executor.submit(say_hello, 1)
print("Got results:", res.result())

results = executor.map(say_hello, range(50))
n_workers = len(set(results))
print("Number of used processes:", n_workers)
assert n_workers == 4
```

### Acknowledgement

This work is supported by the Center for Data Science, funded by the IDEX
Paris-Saclay, ANR-11-IDEX-0003-02