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        "description": "[![PyPI version](https://badge.fury.io/py/trainbench.svg)](https://badge.fury.io/py/trainbench)\n\n# Trainbench \n\nTrainbench is a tool for quickly setup a (Deep Learning) cross-validation training session with configuration instantiation, learned weights, plots, etc, recording at each training epoch.\n\nCompatible with Keras.\n\n**\u26a0\ufe0f This  package is currently under active development!**\n\n## Getting started\n\n##### Install\n```bash\npip install trainbench\n```\n\n##### Create a train session\n```python\n# train.py\n\n# name\nname = 'experiment_01'\n\n# parameters to cross validate against\nparameters = {\n    'fc_size': [256, 512, 1024]    \n    # ...\n}\n\ndef train(parameters):\n    xyz = parameters['xyz']\n    # ...\n    \n    \n```\n\n##### Running from the command line\n```bash\ntrainbench .\n```\n\n##### Checkout your results\n```\n<name>/crosses/\n               /001 \n                   \u251c\u2500 meta.yml \n                   \u251c\u2500 history.pkl \n                   \u2514\u2500 weights/\n                          \u251c\u2500 001.h5\n                          \u2514\u2500 002.h5\n                             ...\n```\n\n\n\n\n##### In Jupyter Notebooks/Python script\n```python\nfrom trainbench import Bench\n\nparameters = {\n\n}\n\ndef train_fn(parameters):\n    pass\n    \nbench = Bench()\nbench.train('experiment_xyz', train_fn, parameters)\n```\n\n\n### Author notes\n\nI'm wrote this tool for my own usage. Feel free to use it at your own will. \nIf you would like to see any additional features / report existing issues please submit a pull request and/or open an issue.",
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