parfun 7.2.2

Lightweight parallelisation library for Python

Citi/Parfun

Lightweight parallelization library for Python.

Parfun is a lightweight library making it easy to write and run Python in parallel and distributed systems.

The main feature of the library is its @parallel decorator that transparently executes standard Python functions in parallel following the map-reduce pattern:

from typing import List

import parfun as pf


@pf.parallel(
    # parallelize by chunking the argument list (map)
    split=pf.per_argument(
        values=pf.py_list.by_chunk
    ),

    # merge the output by concatenating the results (reduce)
    combine_with=pf.py_list.concat,
)
def list_pow(values: List[float], factor: float) -> List[float]:
    """compute powers of a list of numbers"""
    return [v**factor for v in values]


if __name__ == "__main__":
    with pf.set_parallel_backend_context("local_multiprocessing"):  # use a local pool of processes
        print(list_pow([1, 2, 3], 2))  # runs in parallel, prints [1, 4, 9]

Features

• Provides significant speedups to existing Python functions.
• Only requires basic understanding of parallel and distributed computing systems.
• Automatically estimates the optimal sub-task splitting strategy (the partition size).
• Transparently handles data transmission, caching, and synchronization.
• Supports various distributed computing backends:
  • Python's built-in multiprocessing module.
  • Scaler.
  • Dask.

Quick Start

Install Parfun directly from PyPI:

pip install parfun
pip install "parfun[pandas,scaler,dask]"  # with optional dependencies

The official documentation is available at citi.github.io/parfun/.

Take a look at our documentation's quickstart tutorial to get more examples and a deeper overview of the library.

Alternatively, you can build the documentation from source:

cd docs
pip install -r requirements.txt
make html

The documentation's main page can then be found at docs/build/html/index.html.

Benchmarks

Parfun effectively parallelizes even short-duration functions.

For example, when running a short 0.28-second machine learning function on an AMD Epyc 7313 16-Core Processor, we found that Parfun provided an impressive 7.4x speedup. Source code for this experiment here.

Contributing

Your contributions are at the core of making this a true open source project. Any contributions you make are greatly appreciated.

We welcome you to:

• Fix typos or touch up documentation
• Share your opinions on existing issues
• Help expand and improve our library by opening a new issue

Please review our community contribution guidelines and functional contribution guidelines to get started 👍.

Code of Conduct

We are committed to making open source an enjoyable and respectful experience for our community. See CODE_OF_CONDUCT for more information.

License

This project is distributed under the Apache-2.0 License. See LICENSE for more information.

Contact

If you have a query or require support with this project, raise an issue. Otherwise, reach out to [e-mail].