Note:
We're moving to PyTorch Lightning! Read about the move here. From the end of February, torchbearer will no longer be actively maintained. We'll continue to fix bugs when they are found and ensure that torchbearer runs on new versions of pytorch. However, we won't plan or implement any new functionality (if there's something you'd like to see in a training library, consider creating an issue on PyTorch Lightning).
<img alt="logo" src="https://raw.githubusercontent.com/pytorchbearer/torchbearer/master/docs/_static/img/logo_dark_text.svg?sanitize=true" width="100%"/>
<p align="center">
<a href="http://pytorchbearer.org">Website</a> •
<a href="https://torchbearer.readthedocs.io/en/latest/">Docs</a> •
<a href="#examples">Examples</a> •
<a href="#install">Install</a> •
<a href="#citing">Citing</a> •
<a href="#related">Related</a>
</p>
<a id="about"></a>
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.
<a id="examples"></a>
Examples
<a id="general"></a>
General
<table>
<tr>
<td rowspan="3" width="160">
<img src="http://www.pytorchbearer.org/assets/img/examples/quickstart.jpg" width="256">
</td>
<td rowspan="3">
<b>Quickstart:</b> Get up and running with torchbearer, training a simple CNN on CIFAR-10.
</td>
<td align="center" width="80">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/quickstart.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/quickstart.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/github_logo.png" height="32">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/quickstart.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/colab_logo.png" height="28">
</a>
</td>
</tr>
<tr>
<td rowspan="3">
<img src="http://www.pytorchbearer.org/assets/img/examples/callbacks.jpg" width="256">
</td>
<td rowspan="3">
<b>Callbacks:</b> A detailed exploration of callbacks in torchbearer, with some useful visualisations.
</td>
<td align="center">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/callbacks.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/callbacks.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/github_logo.png" height="32">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/callbacks.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/colab_logo.png" height="28">
</a>
</td>
</tr>
<tr>
<td rowspan="3">
<img src="http://www.pytorchbearer.org/assets/img/examples/imaging.jpg" width="256">
</td>
<td rowspan="3">
<b>Imaging:</b> A detailed exploration of the imaging sub-package in torchbearer, useful for showing visualisations during training.
</td>
<td align="center">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/imaging.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/imaging.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/github_logo.png" height="32">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/imaging.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/colab_logo.png" height="28">
</a>
</td>
</tr>
<tr>
<td rowspan="3" colspan="2">
<b>Serialization:</b> This guide gives an introduction to serializing and restarting training in torchbearer.
</td>
<td align="center">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/serialization.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/serialization.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/github_logo.png" height="32">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/serialization.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/colab_logo.png" height="28">
</a>
</td>
</tr>
<tr>
<td rowspan="3" colspan="2">
<b>History and Replay:</b> This guide gives an introduction to the history returned by a trial and the ability to replay training.
</td>
<td align="center">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/history.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/history.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/github_logo.png" height="32">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/history.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/colab_logo.png" height="28">
</a>
</td>
</tr>
<tr>
<td rowspan="3" colspan="2">
<b>Custom Data Loaders:</b> This guide gives an introduction on how to run custom data loaders in torchbearer.
</td>
<td align="center">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/custom_loaders.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/custom_loaders.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/github_logo.png" height="32">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://colab.research.google.com/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/custom_loaders.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/colab_logo.png" height="28">
</a>
</td>
</tr>
<tr>
<td rowspan="3" colspan="2">
<b>Data Parallel:</b> This guide gives an introduction to using torchbearer with DataParrallel.
</td>
<td align="center">
<a href="https://nbviewer.jupyter.org/github/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/data_parallel.ipynb">
<img src="http://www.pytorchbearer.org/assets/img/nbviewer_logo.svg" height="34">
</a>
</td>
</tr>
<tr>
<td align="center">
<a href="https://github.com/pytorchbearer/torchbearer/blob/master/docs/_static/notebooks/data_pa
