Interactively retrieve data from sacred experiments.
Project description
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
To use incense you need the newest development version of sacred, so that content-types of artifacts are automatically detected. Therefore, you first have to install sacred from github and then install incense from PyPI.
pip install git+https://github.com/IDSIA/sacred.git pip install incense
Documentation
demo.ipynb demonstrates the basic functionality of incense. You can also try it out interactively on binder.
Contributing
We recommend using conda to set up your local development environment.
$ conda create -n incense-dev python=3.6 $ conda activate incense-dev # virtualenv is required for the precommit environments. $ conda virtualenv # tox-conda is required for using tox with conda. $ pip install tox-conda $ pip install -r requirements-dev.txt $ pre-commit install
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.