Installation Guide#
DataEval is a lightweight toolkit that offers powerful metric classes that can be extended through additional package installations.
This guide will show you how to install the DataEval that fits your needs!
Supported Python Versions
We currently support python versions 3.9 - 3.11
Base DataEval Packages#
To keep DataEval lightweight but powerful, only the following metrics come with the base installation
Packages |
|---|
Bayes Error Rate |
Divergence |
Upper bound Average Precision |
Extras#
However, DataEval also has installable extras that provide a more expansive and powerful toolkit for any user.
These extras are torch, tensorflow and all. Any extra can be installed using dataeval[extra]
Extras |
Additional Packages |
|---|---|
torch |
Sufficiency |
tensorflow |
OOD Detection |
all |
Sufficiency, OOD Detection |
Installation#
Now that you have a chosen which DataEval to install, the following methods will show you how to install using your preferred method.
Be sure to add [extra] if you are not installing the base DataEval
Installing from pip
pip install dataeval[all]
Installing from conda
conda install dataeval[all]
To install DataEval from source locally on Ubuntu, you will need git-lfs to download larger, binary source files and poetry for project dependency management.
sudo apt-get install git-lfs
pip install poetry
Pull the source down and change to the DataEval project directory.
git clone https://github.com/aria-ml/dataeval.git
cd dataeval
Install DataEval with optional dependencies for development.
poetry install --all-extras --with dev
Alternatively, you can install with optional dependencies used to generate documentation as well.
poetry install --all-extras --with dev --with docs
Now that DataEval is installed, you can run commands in the poetry virtual environment by prefixing shell commands with poetry run, or activate the virtual environment directly in the shell.
poetry shell