Welcome to pythae’s documentation!

This library aims at gathering some of the common (Variational) Autoencoders implementations so that we can conduct benchmark analysis and reproducible research!

News 📢

As of v0.1.0, Pythae now supports distributed training using PyTorch’s DDP). You can now train your favorite VAE faster and on larger datasets, still with a few lines of code. See Distributed Training with Pythae.

Pythae

• Models
• Samplers
• Trainers
• Pipelines

Setup

To install the latest stable release of this library run the following using pip

$ pip install pythae

To install the latest version of this library run the following using pip

$ pip install git+https://github.com/clementchadebec/benchmark_VAE.git

or alternatively you can clone the github repo to access to tests, tutorials and scripts.

$ git clone https://github.com/clementchadebec/benchmark_VAE.git

and install the library

$ cd benchmark_VAE
$ pip install -e .

If you clone the pythae’s repository you will access to the following:

• docs: The folder in which the documentation can be retrieved.

• tests: pythae’s unit-testing using pytest.

• examples: A list of ipynb tutorials and script describing the main functionalities of pythae.

• src/pythae: The main library which can be installed with pip.