Metadata-Version: 2.1
Name: declair
Version: 0.1.2
Summary: Package for declarative hyperparameter search experiments.
Home-page: https://gitlab.com/k-cybulski/declair
Author: Krzysztof Cybulski
Author-email: declair@kcyb.eu
License: EUPL-1.2-or-later
Platform: UNKNOWN
Requires-Python: >=3.5
Description-Content-Type: text/markdown
Requires-Dist: sacred (>=0.8.2)
Requires-Dist: hyperopt (>=0.2.4)
Requires-Dist: pymongo
Requires-Dist: pyyaml
Requires-Dist: tblib
Requires-Dist: GitPython
Requires-Dist: wrapt

# Declair :cake:
[![pipeline status](https://gitlab.com/k-cybulski/declair/badges/master/pipeline.svg)](https://gitlab.com/k-cybulski/declair/-/commits/master)
[![coverage report](https://gitlab.com/k-cybulski/declair/badges/master/coverage.svg)](https://gitlab.com/k-cybulski/declair/-/commits/master)

Declair is a framework for declaratively defining hyperparameter optimization experiments. It uses [Sacred](https://github.com/IDSIA/sacred) for storing experiment results and supports [hyperopt](https://github.com/hyperopt/hyperopt) for optimization.

It came about from attempts to recreate DeepSolaris results in PyTorch instead of Keras. However, it grew to be a more extensive and general framework than originally planned.

# Usage
For detailed instructions on how to use Declair, see the [documentation](https://k-cybulski.gitlab.io/declair/).

## Installation
Install required Python packages in your favourite virtual environment
```
pip install -r requirements.txt
```

## Running the tests
Go into the root of the repository (i.e. where this README.md is) and run 
```
python -m pytest
```

# Credits
This project has been heavily inspired by [cbds_common](https://gitlab.com/CBDS/cbds_common).


