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        "author": "Gergely Svigruha",
        "author_email": "gergely.svigruha@prodmodel.com",
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            "Development Status :: 4 - Beta",
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            "Programming Language :: Python :: 3.5",
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        "description": "![Prodmodel](https://github.com/prodmodel/prodmodel/blob/master/logo.png)\n\nProdmodel is a [build system](https://en.wikipedia.org/wiki/List_of_build_automation_software) for data science pipelines.\nUsers, testers, contributors are welcome!\n\n<p align=\"center\">\n  <a href=\"https://pypi.org/project/prodmodel\">\n    <img src=\"https://img.shields.io/pypi/v/prodmodel.svg\"></img></a>\n  <a href=\"https://pypi.org/project/prodmodel\" alt=\"Downloads\">\n    <img src=\"https://img.shields.io/pypi/dw/prodmodel.svg\" /></a>\n  <a href=\"https://github.com/prodmodel/prodmodel/graphs/contributors\" alt=\"Contributors\">\n    <img src=\"https://img.shields.io/github/contributors/prodmodel/prodmodel.svg\" /></a>\n  <a href=\"https://github.com/prodmodel/prodmodel/pulse\" alt=\"Activity\">\n    <img src=\"https://img.shields.io/github/commit-activity/m/prodmodel/prodmodel.svg\" /></a>\n  <a href=\"https://github.com/prodmodel/prodmodel/issues\" alt=\"Issues\">\n    <img src=\"https://img.shields.io/github/issues/prodmodel/prodmodel.svg\" /></a>\n  <a href=\"https://github.com/prodmodel/prodmodel/issues?utf8=%E2%9C%93&q=is%3Aissue+is%3Aclosed\" alt=\"Closed issues\">\n    <img src=\"https://img.shields.io/github/issues-closed/prodmodel/prodmodel.svg\" /></a>\n  <a href=\"https://github.com/prodmodel/prodmodel/pulls\" alt=\"Pulls\">\n    <img src=\"https://img.shields.io/github/issues-pr/prodmodel/prodmodel.svg\" /></a>\n</p>\n\n<h3 align=\"center\">\n  <a href=\"#motivation\">Motivation</a>\n  <span> \u00b7 </span>\n  <a href=\"#concepts\">Concepts</a>\n  <span> \u00b7 </span>\n  <a href=\"#installation\">Installation</a>\n  <span> \u00b7 </span>\n  <a href=\"#usage\">Usage</a>\n  <span> \u00b7 </span>\n  <a href=\"#contributing\">Contributing</a>\n  <span> \u00b7 </span>\n  <a href=\"#contact\">Contact</a>\n  <span> \u00b7 </span>\n  <a href=\"#licence\">Licence</a>\n</h3>\n\n## Motivation\n\n * Performance. No need to rerun things, everything is cached, switching between multiple versions is super easy. Prodmodel can\n   **figure out if a particular partial code path has already been executed using a particular piece of data** and just use the cached output.\n * Easy debugging. Every single dependency - code or data - is version controlled and tracked.\n * Deploy to production. Models are more than just a file. Prodmodel makes sure that the correct version of label encoders,\n   feature transformation code and data and model files are all packaged together.\n\n## Concepts\n\nA build system is a [DAG](https://en.wikipedia.org/wiki/Directed_acyclic_graph) of `rules` (transformations), `inputs` and `targets`.\nIn Prodmodel `inputs` can be\n * data,\n * Python code,\n * and configuration.\n\nA `rule` is transforming any of the above to an output (which can in turn be depended on by other rules). Therefore rules need to be\nre-executed (and their outputs re-created) if any of their dependencies change. Prodmodel keeps track all of these dependencies.\n\nThe outputs of the `rules` are `targets`. Every `target` corresponds to an output (e.g. a model or a dataset). These outputs\nare cached and version controlled.\n\nProdmodel therefore ensures\n * correctness, by executing every code (e.g. feature transformation, model building, tests) which can potentially be affected by a change, and\n * performance, by executing only the necessary code, saving time compared to rerunning the whole pipeline.\n\n### Rules\n\nEvery rule is a statically typed function, where the inputs are targets, data, or configs. The execution of\na rule outputs some data (e.g. a different feature set or a model), which can be used in other rules.\n\nIn order to use Prodmodel your code has to be structured as functions which the rules can call into.\n\n### Targets\n\nTargets are created by rule functions. Targets can be executed to generate output files. `IterableDataTarget` is a special target\nwhich can be used as an iterable of `dicts` to make iterating over datasets easier. Regular `DataTargets` can represent any\nPython object.\n\n## Installation\n\nProdmodel requires at least Python3.6. Use [pip](https://pip.pypa.io/en/stable/) to install prodmodel.\n\n```bash\npip install prodmodel --user\n```\n\n## Usage\n\nCreate a `build.py` file in your data science folder. The build file contains references to your inputs and the build rules you can execute.\n\n```python\nfrom prodmodel.rules import rules\n\ncsv_data = rules.data_source(file='data.csv', type='csv', dtypes={...})\n\nmy_model = rules.transform(objects={'data': csv_data}, file='kmeans.py', fn='compute_kmeans')\n```\n\nNow you can build your model by running `prodmodel my_model` from the directory of `build.py`,\nor `prodmodel <path_to_my_directory>:my_model` from any directory.\n\nProdmodel creates a `.prodmodel` directory under the home directory of the user to store log and config files.\n\n### Documentation\n\nCheck out a complete [example project](https://github.com/prodmodel/prodmodel/tree/master/example) for more examples.\n\nThe complete list of build rules can be found [here](https://github.com/prodmodel/prodmodel/blob/master/doc/api_doc.md).\n\nProdmodel searches for a config file under `<user home dir>/.prodmodel/config`. The config file can be created manually\nbased on this [template](https://github.com/prodmodel/prodmodel/blob/master/doc/config).\n\n### Arguments\n\n * `--force_external`: Some data sources are remote (e.g. an SQL server), therefore tracking changes is not always feasible.\n   This argument gives the user manual control over when to reload these data sources.\n * `--cache_data`: Cache local data files if changed. This can be useful for debugging / reproducibility by making sure every\n   data source used for a specific build is saved.\n * `--output_format`: One of `none`, `str`, `bytes` and `log`. The output format of the data produced by the build target\n   written to stdout.\n\n### List targets in build file\n\n * Run `prodmodel ls <path_to_build>` to list targets in a build file where `<path_to_build>` to the build file or its directory.\n\n### Cleaning old cache files\n\n * Run `prodmodel clean <target> --cutoff_date=<cutoff datetime>` to delete output cache files of a target created before\n   the cutoff datetime, which has to be in `%Y-%m-%dT%H:%M%S` (`YYYY-mm-ddTHH:MM:SS`) format.\n\n## Contributing\nPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.\n\n## Contact\nFeel free to email me at gergely.svigruha@prodmodel.com if you have any question, need help or would like to contribute to the code.\n\n## Licence\n[Apache 2.0](https://github.com/prodmodel/prodmodel/blob/master/LICENCE)\n\n\n",
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