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        "author": "Florian Finkernagel",
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        "description": "# pypipegraph \n\n| Build status: | [![Build Status](https://travis-ci.com/TyberiusPrime/pypipegraph.svg?branch=master)](https://travis-ci.com/TyberiusPrime/pypipegraph)|\n|---------------|-----------------------------------------------------------------------------|\n| Documentation | https://pypipegraph.readthedocs.io/en/latest/\n| Code style    | ![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)\n\n## Introduction\n\n[pypipegraph](https://github.com/IMTMarburg/pypipegraph): is an\nMIT-licensed library for constructing a workflow piece by piece and\nexecuting just the parts of it that need to be (re-)done. It supports\nusing multiple cores (SMP) and (eventually, alpha code right now)\nmachines (cluster) and is a hybrid between a dependency tracker (think\n'make') and a cluster engine.\n\nMore specifically, you construct Jobs, which encapsulate output (i.e.\nstuff that needs to be done), invariants (which force re-evaluation of\noutput jobs if they change), and stuff inbetween (e.g. load data from\ndisk).\n\nFrom your point of view, you create a pypipegraph, you create jobs,\nchain them together, then ask the pypipegraph to run. It examines all\njobs for their need to run (either because the have not been finished,\nor because they have been invalidated), distributes them across multiple\npython instances, and get's them executed in a sensible order.\n\nIt is robust against jobs dying for whatever reason (only the failed job\nand everything 'downstream' will be affected, independend jobs will\ncontinue running), allows you to resume at any point 'in between' jobs,\nand isolates jobs against each other.\n\npypipegraph supports Python 3 only.\n\n## 30 second summary\n\n```python\n    pypipegraph.new_pipeline()\n    output_filenameA = 'sampleA.txt'\n    def do_the_work():\n        op = open(output_filename, 'wb').write(\"hello world\")\n    jobA = pypipegraph.FileGeneratingJob(output_filenameA, do_the_work)\n    output_filenameB = 'sampleB.txt'\n    def do_the_work():\n         op = open(output_filenameB, 'wb').write(open(output_filenameA, 'rb').read() + \",  once again\")\n    jobB = pypipegraph.FileGeneratingJob(output_filenameB, do_the_work)\n    jobB.depends_on(jobA)\n    pypipegraph.run()\n    print('the pipegraph is done and has returned control to you.')\n    print('sampleA.txt contains \"hello world\"')\n    print('sampleB.txt contains \"hello world, once again\")\n```",
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