Installation

Installation

To get started we recommend using Anaconda or Virtual Environment with Python 3.11 or higher.

Install with pip (Recommended)

pip install vame-py

Install from Github repository

1. Clone the VAME repository to your local machine by running

git clone https://github.com/LINCellularNeuroscience/VAME.git

2. Installing VAME from local source

Option 1: Using VAME.yaml file to create a conda environment and install VAME in it by running

conda env create -f VAME.yaml

Option 2: Installing local VAME with pip in your active virtual environment by running

cd VAME
pip install .

warning

You should make sure that you have a GPU powerful enough to train deep learning networks. In our original 2022 paper, we were using a single Nvidia GTX 1080 Ti GPU to train our network. A hardware guide can be found here.

tip

VAME can also be trained in Google Colab or on a HPC cluster.

Once you have your computing setup ready, begin using VAME by following the demo workflow guide.

References

Original VAME publication: Identifying Behavioral Structure from Deep Variational Embeddings of Animal Motion
Kingma & Welling: Auto-Encoding Variational Bayes
Pereira & Silveira: Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection

License: GPLv3

See the LICENSE file for the full statement.