Update 04/14/2022: This fork is written for supporting the Numerai's dataset
A collection of Variational AutoEncoders (VAEs) implemented in pytorch with focus on reproducibility. The aim of this project is to provide a quick and simple working example for many of the cool VAE models out there.
- Python >= 3.5
- PyTorch >= 1.3
- Pytorch Lightning >= 0.6.0 (GitHub Repo)
- CUDA enabled computing device
$ git clone https://github.com/AntixK/PyTorch-VAE
$ cd PyTorch-VAE
$ pip install -r requirements.txt
$ cd PyTorch-VAE
$ python3 run.py --config configs/numerai_vae.yaml
View TensorBoard Logs
$ cd logs/<experiment name>/version_<the version you want>
$ tensorboard --logdir .
Note: The default dataset is Numerai's train set. You need to download the train.parquet file from the Numerai's API and set the path in the configs/numerai_vae.yaml file.
Apache License 2.0
| Permissions | Limitations | Conditions |
|---|---|---|
| ✔️ Commercial use | ❌ Trademark use | ⓘ License and copyright notice |
| ✔️ Modification | ❌ Liability | ⓘ State changes |
| ✔️ Distribution | ❌ Warranty | |
| ✔️ Patent use | ||
| ✔️ Private use |
@misc{Subramanian2020,
author = {Subramanian, A.K},
title = {PyTorch-VAE},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/AntixK/PyTorch-VAE}}
}