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        "author": "R\u00fcdiger Busche",
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            "Intended Audience :: Science/Research",
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        "description": ".. image:: https://mybinder.org/badge_logo.svg\n    :target: https://mybinder.org/v2/gh/JarnoRFB/incense/master?urlpath=lab/tree/demo.ipynb\n\n.. image:: https://travis-ci.org/JarnoRFB/incense.svg?branch=master\n    :target: https://travis-ci.org/JarnoRFB/incense\n\n.. image:: https://codecov.io/gh/JarnoRFB/incense/branch/master/graph/badge.svg\n  :target: https://codecov.io/gh/JarnoRFB/incense\n\n.. image:: https://img.shields.io/lgtm/grade/python/g/JarnoRFB/incense.svg?logo=lgtm&logoWidth=18\n    :target: https://lgtm.com/projects/g/JarnoRFB/incense/context:python\n\n.. image:: https://img.shields.io/badge/code%20style-black-000000.svg\n    :target: https://github.com/ambv/black\n\nIncense\n=======\n\nThough automated logging of machine learning experiments results is\ncrucial, it does not replace manual interpretation. Incense is a toolbox\nto facilitate manual interpretation of experiments that are logged using\n`sacred <https://github.com/IDSIA/sacred>`__. It lets you find and\nevaluate experiments directly in Jupyter notebooks. Incense lets you\nquery the database for experiments by id, name or any hyperparmeter\nvalue. For each found experiment, configuration, artifacts and metrics\ncan be displayed. The artifacts are rendered according to their type,\ne.g.\u00a0a PNG image is displayed as an image, while a CSV file gets\ntransformed to a ``pandas DataFrame``. Metrics are by default\ntransformed into ``pandas Series``, which allows for flexible plotting.\nTogether with sacred and incense, Jupyter notebooks offer the perfect\nsolution for interpreting experiments as they allow for a combination of\ncode that reproducibly displays the experiment\u2019s results, as well as\ntext that contains the interpretation.\n\nInstallation\n------------\n\nInstall the latest release\n\n::\n\n   pip install incense\n   \nOr install the latest development version\n\n::\n\n   pip install git+https://github.com/JarnoRFB/incense.git\n\nDocumentation\n-------------\n\n`demo.ipynb <demo.ipynb>`_ demonstrates the basic functionality of\nincense. You can also try it out interactively on\n`binder <https://mybinder.org/v2/gh/JarnoRFB/incense/master?urlpath=lab/tree/demo.ipynb>`_.\n\nContributing\n------------\nWe recommend using the `VSCode devcontainer <https://code.visualstudio.com/docs/remote/containers>`_ for development.\nIt will automatically install all dependencies and start\nnecessary services, such as mongoDB and JupyterLab.\nSee `<.devcontainer/docker-compose.yml>`_ for details.\nIf the output of ``id -u`` is something different than ``1000`` on your system, please add\n\n::\n\n   export UID\n\nto your ``.bashrc`` or ``.zshrc``.\n\nBuilding the container for the first time may take some time.\nOnce in the container run\n\n::\n\n  $ pre-commit install\n  $ python tests/example_experiment/conduct.py\n\n\nto set up the pre-commit hooks and populate the example database.\n\nAlternatively, you can use conda to set up your local development environment.\n\n::\n\n  $ conda create -n incense-dev python=3.6\n  $ conda activate incense-dev\n  # virtualenv is required for the precommit environments.\n  $ conda install virtualenv\n  # tox-conda is required for using tox with conda.\n  $ pip install tox-conda\n  $ pip install -r requirements-dev.txt\n  $ pre-commit install",
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