{ "info": { "author": "Ivan Bestvina", "author_email": "ivan.bestvina@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "License :: OSI Approved :: MIT License", "Programming Language :: Python :: 3.4" ], "description": "DAGpy\n=====\n\nDAGpy is a data science collaboration tool based on iPython notebooks enabling data science teams to:\n\n- easily collaborate by branching out of others' notebooks \n- minimize code duplication \n- give a clean overview of the project \n- cache intermediate outputs so team members can use them without re-evaluation \n- automate the process of code execution upon data changes or on schedule \n- provide a clean interface to the data visualization dashboard designers and developers\n\nDAGpy manages a DAG (directed acyclic graph) of blocks of code, with\neach block being a sequence of iPython notebook cells, together with\ntheir outputs. It is designed to work seamlessly with popular VC systems\nlike git and can be run locally or as a server application.\n\nGitHub: `github.com/ibestvina/dagpy/\n`_.\n\nAuthor: Ivan Bestvina\n\n\nExample project\n---------------\n\nTo play around with the example project, you can:\n\n- view the project DAG: ``dagpy view``\n- run all the blocks: ``dagpy execute -a``\n- add blocks through flows (with block B as a parent) and run them automatically: ``dagpy makeflow B -r``\n- commit the changes: ``dagpy submitflow dagpy_flow.ipynb``\n- explore other DAGpy options with ``dagpy -h``\n\nPlease note that notebook execution time includes a significant overhead\nof over a second, because a kernel must be started for each one. 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