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Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)
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| 2026-08-06 | 1997 |
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<p align="center">
<a href="https://pypi.org/project/pythae/">
<img src='https://badge.fury.io/py/pythae.svg' alt='Python' />
</a>
<a>
<img src='https://img.shields.io/badge/python-3.7%7C3.8%7C3.9%2B-blueviolet' alt='Python' />
</a>
<a href='https://pythae.readthedocs.io/en/latest/?badge=latest'>
<img src='https://readthedocs.org/projects/pythae/badge/?version=latest' alt='Documentation Status' />
</a>
<a href='https://opensource.org/licenses/Apache-2.0'>
<img src='https://img.shields.io/github/license/clementchadebec/benchmark_VAE?color=blue' />
</a><br>
<a>
<img src='https://img.shields.io/badge/code%20style-black-black' />
</a>
<a href="https://codecov.io/gh/clementchadebec/benchmark_VAE">
<img src="https://codecov.io/gh/clementchadebec/benchmark_VAE/branch/main/graph/badge.svg?token=KEM7KKISXJ"/>
</a>
<a href="https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/overview_notebook.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg"/>
</a>
</a>
</p>
</p>
<p align="center">
<a href="https://pythae.readthedocs.io/en/latest/">Documentation</a>
</p>
# pythae
This library implements some of the most common (Variational) Autoencoder models under a unified implementation. In particular, it
provides the possibility to perform benchmark experiments and comparisons by training
the models with the same autoencoding neural network architecture. The feature *make your own autoencoder*
allows you to train any of these models with your own data and own Encoder and Decoder neural networks. It integrates experiment monitoring tools such [wandb](https://wandb.ai/), [mlflow](https://mlflow.org/) or [comet-ml](https://www.comet.com/signup?utm_source=pythae&utm_medium=partner&utm_campaign=AMS_US_EN_SNUP_Pythae_Comet_Integration) 🧪 and allows model sharing and loading from the [HuggingFace Hub](https://huggingface.co/models) 🤗 in a few lines of code.
**News** 📢
As of v0.1.0, `Pythae` now supports distributed training using PyTorch's [DDP](https://pytorch.org/docs/stable/notes/ddp.html). You can now train your favorite VAE faster and on larger datasets, still with a few lines of code.
See our speed-up [benchmark](#benchmark).
## Quick access:
- [Installation](#installation)
- [Implemented models](#available-models) / [Implemented samplers](#available-samplers)
- [Reproducibility statement](#reproducibility) / [Results flavor](#results)
- [Model training](#launching-a-model-training) / [Data generation](#launching-data-generation) / [Custom network architectures](#define-you-own-autoencoder-architecture) / [Distributed training](#distributed-training-with-pythae)
- [Model sharing with 🤗 Hub](#sharing-your-models-with-the-huggingface-hub-) / [Experiment tracking with `wandb`](#monitoring-your-experiments-with-wandb-) / [Experiment tracking with `mlflow`](#monitoring-your-experiments-with-mlflow-) / [Experiment tracking with `comet_ml`](#monitoring-your-experiments-with-comet_ml-)
- [Tutorials](#getting-your-hands-on-the-code) / [Documentation](https://pythae.readthedocs.io/en/latest/)
- [Contributing 🚀](#contributing-) / [Issues 🛠️](#dealing-with-issues-%EF%B8%8F)
- [Citing this repository](#citation)
# Installation
To install the latest stable release of this library run the following using ``pip``
```bash
$ pip install pythae
```
To install the latest github version of this library run the following using ``pip``
```bash
$ 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.
```bash
$ git clone https://github.com/clementchadebec/benchmark_VAE.git
```
and install the library
```bash
$ cd benchmark_VAE
$ pip install -e .
```
## Available Models
Below is the list of the models currently implemented in the library.
| Models | Excerpt of 52,160 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:530fe0da0acd2016, topic:pytorch, readme:distributed training
