A PyTorch model fitting library designed for use by researchers (or anyone really) working in deep learning or differentiable programming. Specifically, we aim to dramatically reduce the amount of boilerplate code you need to write without limiting the functionality and openness of PyTorch.
Examples
General
Deep Learning
Differentiable Programming
Install
The easiest way to install torchbearer is with pip:
pip install torchbearer
Alternatively, build from source with:
pip install git+https://github.com/pytorchbearer/torchbearer
Citing Torchbearer
If you find that torchbearer is useful to your research then please consider citing our preprint: Torchbearer: A Model Fitting Library for PyTorch, with the following BibTeX entry:
@article{torchbearer2018,
author = {Ethan Harris and Matthew Painter and Jonathon Hare},
title = {Torchbearer: A Model Fitting Library for PyTorch},
journal = {arXiv preprint arXiv:1809.03363},
year = {2018}
}
Related
Torchbearer isn't the only library for training PyTorch models. Here are a few others that might better suit your needs (this is by no means a complete list, see the awesome pytorch list or the incredible pytorch for more):
- skorch, model wrapper that enables use with scikit-learn - crossval etc. can be very useful
- PyToune, simple Keras style API
- ignite, advanced model training from the makers of PyTorch, can need a lot of code for advanced functions (e.g. Tensorboard)
- TorchNetTwo (TNT), can be complex to use but well established, somewhat replaced by ignite
- Inferno, training utilities and convenience classes for PyTorch
- Pytorch Lightning, lightweight wrapper on top of PyTorch with advanced multi-gpu and cluster support
- Pywick, high-level training framework, based on torchsample, support for various segmentation models