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A robust implementation of concurrent.futures.ProcessPoolExecutor

Project description

# Reusable Process Pool Executor [![Build Status](https://travis-ci.org/tomMoral/loky.svg?branch=master)](https://travis-ci.org/tomMoral/loky) [![Build status](https://ci.appveyor.com/api/projects/status/oifqilb5sb0p7fdp/branch/master?svg=true)](https://ci.appveyor.com/project/tomMoral/loky/branch/master) [![codecov](https://codecov.io/gh/tomMoral/loky/branch/master/graph/badge.svg)](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:

* __Consistent and robust spawn behavior__: All processes are started
using fork + exec on POSIX systems. This ensures safer interactions with
third party libraries. On the contrary, `multiprocessing.Pool` uses
fork without exec by default, causing third party runtimes to crash
(e.g. OpenMP, macOS Accelerate...).

* __Reusable executor__: strategy to avoid re-spawning 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.

* __Deadlock free implementation__: one of the major concern in
standard `multiprocessing` and `concurrent.futures` modules 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. Note that the implementation of
`concurrent.futures.ProcessPoolExecutor` that comes with Python 3.7+
is as robust as the executor from loky but the later also works for
older versions of Python.


### Installation

The recommended way to install `loky` is with `pip`,
```bash
pip install loky
```

`loky` can also be installed from sources using
```bash
python setup.py install
```

### 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
```

For more advance usage, see our [documentation](https://loky.readthedocs.io/en/stable/)

### Workflow to contribute

To contribute to **loky**, first create an account on [github](http://github.com/).
Once this is done, fork the [loky repository](http://github.com/loky/loky) to
have your own repository, clone it using 'git clone' on the computers where you
want to work. Make your changes in your clone, push them to your github account,
test them on several computers, and when you are happy with them, send a pull
request to the main repository.

### Running the test suite

To run the test suite, you need the `pytest` (version >= 3) and `psutil`
modules. Run the test suite using:

```sh
pip install -e .
pytest .
```

from the root of the project.

### Acknowledgement

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


Project details


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