Starter conda environment files for easy and lightweight data science environment setup
If reproducibility is a big concern, we recommend using instead Docker with our docker images as a starting point.
name | description |
---|---|
base-data-science | Basic data science environment, with Jupyter Lab, scikit-learn , pandas , seaborn and standard software engineering utilities |
advanced-machine-learning | Same a base-data-science , with the addition of more models, automation and diagnostic tools:xgboost , optuna , yellowbrick and snakemake |
conda
3 We recommend using miniconda 3 instead of the full Anaconda 3 distribution
To get started, clone this repository, or download the file for the desired environment
Go to the folder of the desired environment file, run
conda env create -f environment.yml
conda activate ENVIRONMENT_NAME
View available environments with
conda env list
Launch Jupyter Lab in that environment with
jupyter lab
Install the desired environment. Then,
conda create --name NEW_ENVIRONMENT --clone OLD_ENVIRONMENT
This repository is licensed under the terms of the MIT License.