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        "description": "# parallelize\n\n<img src=\"logo.png\" width=\"500px\" style=\"justify:left\">\n\n[![Documentation Status](https://readthedocs.org/projects/parallelize/badge/?version=latest)](https://parallelize.readthedocs.io/en/latest/?badge=latest)\n![AUR](https://img.shields.io/aur/license/yaourt.svg)\n[![CircleCI](https://circleci.com/gh/ismailuddin/parallelize.svg?style=svg)](https://circleci.com/gh/ismailuddin/parallelize)\n\n`parallelize` is a Python package to simplify the process of parallelising your taks in Python. It takes advantage of the `multiprocessing` module to spawn new processes for your job.\n\n### Requirements\n- Python 3.X\n\n### Installation\nTo install `parallelize`, you can either install from the source code or using `pip`.\n\nTo install from source code, first clone the repository. Then, run `python setup.py install` in the root directory.\n\n### Documentation\nTo build the documentation, run `make html` inside the `docs/` folder. The documention will be found in the `docs/build/html` directory. Alternatively, view documentation [here](https://parallelize.readthedocs.io).\n\n### Usage\n\nTo parallelise a task in Python, you should wrap the entire code inside a function and have the first argument of your function receive the iterable your function will be operating over.\n\n```python\n>>> from parallelize import parallelize\n>>> def foo(iterable: list) -> int:\n...    output = 0\n...    for i in iterable:\n...        output = i**4\n...    return output\n\n>>> numbers = list(range(50000000))\n>>> %time foo(numbers)\nWall time: 21.5 s\n>>> parallelize.parallel(foo, numbers, 6)\nCompleted 'parallel' in 6.2743 secs\n```\n\n\n\n",
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