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        "author": "Stefan Otte",
        "author_email": "stefan.otte@gmail.com",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Artificial Intelligence"
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        "description": "``LolaML`` - track your ML experiments\n======================================\n\n.. image:: https://readthedocs.org/projects/lolaml/badge/?version=latest\n   :target: https://lolaml.readthedocs.io/en/latest/?badge=latest\n   :alt: Documentation Status\n\n.. image:: https://gitlab.com/stefan-otte/lolaml/badges/master/pipeline.svg\n   :target: https://gitlab.com/stefan-otte/lolaml/commits/master\n   :alt: Pipeline Status\n\n.. image:: https://gitlab.com/stefan-otte/lolaml/badges/master/coverage.svg\n   :target: https://gitlab.com/stefan-otte/lolaml/commits/master\n   :alt: Coverage Report\n\n.. image:: https://img.shields.io/pypi/v/lolaml.svg\n   :target: https://pypi.org/project/lolaml\n   :alt: PyPI Version\n\n.. [![Windows Build Status](https://img.shields.io/appveyor/ci/sotte/lolaml/master.svg?label=window)](https://ci.appveyor.com/project/sotte/lolaml)\n.. [![Scrutinizer Code Quality](https://img.shields.io/scrutinizer/g/sotte/lolaml.svg)](https://scrutinizer-ci.com/g/sotte/lolaml/?branch=master)\n\nTrack your machine learning experiments with LolaML,\nnever lose any information or forget which parameter yielded which results.\nLola creates a simple JSON representation of the run that contains all the\ninformation you logged.\nThe JSON can easily be shared to collaborate with friends and colleagues.\nLola strives to be non-magic and simple but configurable.\n\n**Features:**\n\n- a simple logging interface\n- a simple JSON representation of the logged data\n- works with any machine learning library\n- automatically creates an artifact folder for each run\n- automatically uploads artifacts to a remote bucket (if you want)\n- simple jupyter notebook dashboard (more to come...)\n\n::\n\n    import lolaml as lola\n\n    # Use the run context manager to start/end a run\n    with lola.Run(project=\"mnist\", prefix_path=\"data/experiments\") as run:\n        # a unique id for the run\n        print(run.run_id)\n        # store all artifacts (model files, images, etc.) here\n        print(run.path)  # -> data/experiments/mnist/<run_id>\n\n        run.log_param(\"lr\", 0.1)\n        run.log_param(\"epochs\", 10)\n\n        run.log_tags(\"WIP\", \"RNN\")\n\n        # Create and train your model...\n\n        run.log_metric(\"loss\", loss, step=1)\n        run.log_metric(\"train_acc\", train_acc, step=1)\n        run.log_metric(\"val_acc\", val_acc, step=1)\n\n        model.save(os.path.join(run.path, \"model.pkl\"))\n\n    # After a run there is a lola_run*.json file under run.path.\n    # It contails all the information you logged.\n\nAfter the run there is a JSON file that looks something like this::\n\n    {\n        \"project\": \"mnist\",\n        \"run_id\": \"9a531da0-dc43-4dcc-8968-77fd480ff7ee\",\n        \"status\": \"done\",\n        \"path\": \"data/experiments/mnist/9a531da0-dc43-4dcc-8968-77fd480ff7ee\",\n        \"run_file\": \"data/experiments/mnist/9a531da0-dc43-4dcc-8968-77fd480ff7ee/lola_run.json\",\n        \"user\": \"stefan\",\n        \"start_time\": \"2019-02-16 12:49:32.782958\",\n        \"end_time\": \"2019-02-16 12:49:32.814529\",\n        \"metrics\": [\n            {\n                \"name\": \"loss\",\n                \"value\": 1.5\n                \"step\": 1,\n                \"ts\": \"2019-02-16 12:49:32.813750\"\n            },\n            ...\n        ],\n        \"params\": {\n            \"lr\": \"0.1\",\n            \"epochs\": 10,\n        },\n        \"tags\": [\"WIP\", \"RNN\"],\n        \"artifacts\": {\n            \"data/experiments/mnist/9a531da0-dc43-4dcc-8968-77fd480ff7ee/lola_run_9a531da0-dc43-4dcc-8968-77fd480ff7ee.json\": {},\n            ...\n        },\n        \"git\": {\n            \"sha\": \"41cb4fb11b7e937c602c2282b9275200c88b8797\",\n            \"status\": \"...\",\n            \"diff\": \"...\",\n        },\n        \"call_info\": {\n            \"cwd\": \"some/where\",\n            \"__file__\": \"somefile.py\",\n            \"argv\": [],\n        }\n    }\n\n\nLola can automatically upload all artifacts to a remote storage bucket for you::\n\n  with lola.run(\n      remote_location=\"gs://somewhere\",\n      remote_credentials=\"service_account.json\",\n  ) as run:\n      # train and log ...\n\n  # All artifacts are uploaded now\n\n\nThe remote location can also be configured with the `.lola.toml` file.\n\nAdditionally, Lola offers some helpers to analyse the your experiments:\n\nTODO add image of dashboard\n\nSetup\n=====\n\nRequirements\n------------\n\n* Python 3.6+\n\nInstallation\n------------\n\nInstall this library directly into an activated virtual environment::\n\n    $ pip install lolaml\n\nor add it to your `Poetry <https://poetry.eustace.io/>`_ project::\n\n    $ poetry add lolaml\n\nMisc\n====\n\nThis project was generated with `cookiecutter <https://github.com/audreyr/cookiecutter>`_\nusing `jacebrowning/template-python <https://github.com/jacebrowning/template-python>`_.\nThanks!\n",
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