---
id: para
version: "0.0.8"
license: MIT
license_treatment: permissive
maintenance: abandoned
---

# para — a set utilities that ake advantage of python's 'multiprocessing' module to distribute CPU-intensive tasks

License: permissive · Maintenance: abandoned · Downloads: 247.1K/mo

## What it is and what it does

Para is a lightweight wrapper around Python's multiprocessing module that simplifies distributing CPU-bound work across multiple processor cores on a single machine. It centers on a map() function that applies a function to each item in a collection, executing the work in parallel across available cores rather than sequentially. The library is designed for straightforward parallel processing tasks without the complexity of lower-level multiprocessing APIs.

The package has no external runtime dependencies and installs with minimal friction. However, it has been abandoned since 2020-01-17 and receives no maintenance. For projects already using it or for simple parallel-map use cases where the API fits, it remains functional; for new projects, you should evaluate whether modern alternatives better suit your Python version and concurrency needs.

Use it for:

- Process multiple large gzip-compressed files in parallel, extracting and filtering lines across CPU cores.
- Distribute CPU-intensive computations over a collection of data items without writing explicit multiprocessing boilerplate.
- Parallelize operations like reading or transforming files when the work can be divided into independent tasks.
- Leverage multiple cores for batch processing tasks in legacy codebases already using para.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Distributes CPU-intensive processing of collections across multiple cores using Python's multiprocessing module, with a primary map() function for parallel execution.

Yes, but with strong conditions. Para is worth installing only if you are maintaining existing code that already depends on it, or if you need a minimal, zero-dependency parallel-map utility and can accept that the package is unmaintained. For new projects, evaluate modern alternatives that receive active maintenance and may offer better compatibility with current Python versions. The permissive MIT license and low install friction are advantages, but abandonment is a significant drawback.

## Install

pip install para
uv add para
poetry add para

## Installing para

Before you install: Installation is frictionless with no runtime dependencies. However, the package is abandoned—last release was 2020-01-17, so expect no maintenance, bug fixes, or compatibility updates.

License in practice: MIT license is permissive and imposes no restrictions on use, modification, or distribution in proprietary or open-source projects.

Quickstart:

pip install para

import para
import gzip

items = ["file1.gz", "file2.gz"]
def process(path):
    with gzip.open(path, 'rt') as f:
        for line in f:
            yield line

for result in para.map(process, items):
    print(result)

Requires Python 3; no explicit minimum version specified in package metadata.

Verify before relying:

- Compatibility with Python versions released after 2020-01-17, given the package's abandonment.
- Performance characteristics and scalability limits compared to modern alternatives.
- Whether the map() function handles generator functions and exception propagation as expected in current Python releases.

## Package facts

- License: MIT (permissive)
- Python support: unspecified
- Install friction: low
- Maintenance: abandoned
- Downloads: 247.1K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

multiprocessing map function, parallel processing multiple cores, cpu intensive task distribution, python multicore execution, distribute work across cpus, multiprocessing, parallel-computing, abandoned

[View on SkillFed](https://skillfed.io/packages/para) · [View on PyPI](https://pypi.org/project/para/)