https://github.com/JarnoRFB/incense

Interactively retrieve data from sacred experiments.
https://github.com/JarnoRFB/incense

machine-learning mongodb sacred

Last synced: 3 months ago
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Interactively retrieve data from sacred experiments.

• Host: GitHub
• URL: https://github.com/JarnoRFB/incense
• Owner: JarnoRFB
• License: mit
• Created: 2018-09-18T10:02:52.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2024-07-29T17:50:57.000Z (3 months ago)
• Last Synced: 2024-07-29T23:37:14.046Z (3 months ago)
• Topics: machine-learning, mongodb, sacred
• Language: Python
• Homepage:
• Size: 511 KB
• Stars: 80
• Watchers: 2
• Forks: 8
• Open Issues: 48
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE

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README

        
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Incense

=======

Though automated logging of machine learning experiments results is

crucial, it does not replace manual interpretation. Incense is a toolbox

to facilitate manual interpretation of experiments that are logged using

`sacred `__. It lets you find and

evaluate experiments directly in Jupyter notebooks. Incense lets you

query the database for experiments by id, name or any hyperparmeter

value. For each found experiment, configuration, artifacts and metrics

can be displayed. The artifacts are rendered according to their type,

e.g. a PNG image is displayed as an image, while a CSV file gets

transformed to a ``pandas DataFrame``. Metrics are by default

transformed into ``pandas Series``, which allows for flexible plotting.

Together with sacred and incense, Jupyter notebooks offer the perfect

solution for interpreting experiments as they allow for a combination of

code that reproducibly displays the experiment’s results, as well as

text that contains the interpretation.

Installation

------------

Install the latest release

::

   pip install incense

   

Or install the latest development version

::

   pip install git+https://github.com/JarnoRFB/incense.git

Documentation

-------------

`demo.ipynb `_ demonstrates the basic functionality of

incense. You can also try it out interactively on

`binder `_.

Contributing

------------

We recommend using the `VSCode devcontainer `_ for development.

It will automatically install all dependencies and start

necessary services, such as mongoDB and JupyterLab.

See `<.devcontainer/docker-compose.yml>`_ for details.

If the output of ``id -u`` is something different than ``1000`` on your system, please add

::

   export UID

to your ``.bashrc`` or ``.zshrc``.

Building the container for the first time may take some time.

Once in the container run

::

  $ pre-commit install

  $ python tests/example_experiment/conduct.py

to set up the pre-commit hooks and populate the example database.

Alternatively, you can use conda to set up your local development environment.

::

  $ conda create -n incense-dev python=3.7

  $ conda activate incense-dev

  # virtualenv is required for the precommit environments.

  $ conda install virtualenv

  # tox-conda is required for using tox with conda.

  $ pip install tox-conda

  $ pip install -r requirements-dev.txt

  $ pre-commit install