Looking to improve your experimental workflow? Tired of dealing with all the different experimental setups?
If so, I would like to introduce you to Hydra (https://github.com/facebookresearch/hydra).
Hydra elegantly configures complex applications and has loads of advantages like
- easy and clear experimental configuration
- no boilerplate regarding IO
- dynamic composition of configurations
- easy parallel running on local machine and on compute clusters
- easy optimization of your function
Hydra basically is a supercharged argument parser and we will show you how to use it!
Together, we will configure a basic (Auto)ML experiment.
This tutorial can serve you as an entry point and reference for your own experimental configurations with Hydra.
Check out tutorial.ipynb and try yourself. 🤗
We will also give you a sneak peek to our Hydra-SMAC-sweeper in the tutorial, the combined power of hydra and HPO via SMAC!
Run bash install.sh to create and activate a conda environment and to install this repo.
git clone git@github.com:automl/hydra_tutorial.git
cd hydra_tutorial
conda create -n hydratutorial python=3.11
conda activate hydratutorial
# Install for usage
pip install .
# Install for development
make install-dev