<div align="center">
<img src="https://github.com/Lightning-AI/lightning-bolts/raw/0.6.0.post1/docs/source/_images/logos/bolts_logo.png" width="400px">
**Deep Learning components for extending PyTorch Lightning**
______________________________________________________________________
<p align="center">
<a href="#install">Installation</a> •
<a href="https://lightning-bolts.readthedocs.io/en/latest/">Latest Docs</a> •
<a href="https://lightning-bolts.readthedocs.io/en/0.6.0.post1">Stable Docs</a> •
<a href="#what-is-bolts">About</a> •
<a href="#team">Community</a> •
<a href="https://www.pytorchlightning.ai/">Website</a> •
<a href="https://www.grid.ai/">Grid AI</a> •
<a href="#license">License</a>
</p>
[](https://badge.fury.io/py/lightning-bolts)
[](https://pepy.tech/project/lightning-bolts)
[](https://dev.azure.com/Lightning-AI/lightning%20Bolts/_build?definitionId=31&_a=summary&repositoryFilter=13&branchFilter=4923%2C4923)
[](https://codecov.io/gh/Lightning-AI/lightning-bolts)
[](https://lightning-bolts.readthedocs.io/en/latest/)
[](https://www.pytorchlightning.ai/community)
[](https://github.com/PytorchLightning/lightning-bolts/blob/master/LICENSE)
</div>
______________________________________________________________________
## Getting Started
Pip / Conda
```bash
pip install lightning-bolts
```
<details>
<summary>Other installations</summary>
Install bleeding-edge (no guarantees)
```bash
pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgrade
```
To install all optional dependencies
```bash
pip install lightning-bolts["extra"]
```
</details>
## What is Bolts
Bolts provides a variety of components to extend PyTorch Lightning such as callbacks & datasets, for applied research and production.
## News
- Sept 22: [Leverage Sparsity for Faster Inference with Lightning Flash and SparseML](https://devblog.pytorchlightning.ai/leverage-sparsity-for-faster-inference-with-lightning-flash-and-sparseml-cdda1165622b)
- Aug 26: [Fine-tune Transformers Faster with Lightning Flash and Torch ORT](https://devblog.pytorchlightning.ai/fine-tune-transformers-faster-with-lightning-flash-and-torch-ort-ec2d53789dc3)
#### Example 1: Accelerate Lightning Training with the Torch ORT Callback
Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. See the [documentation](https://lightning-bolts.readthedocs.io/en/latest/callbacks/torch_ort.html) for more details.
```python
from pytorch_lightning import LightningModule, Trainer
import torchvision.models as models
from pl_bolts.callbacks import ORTCallback
class VisionModel(LightningModule):
def __init__(self):
super().__init__()
self.model = models.vgg19_bn(pretrained=True)
...
model = VisionModel()
trainer = Trainer(gpus=1, callbacks=ORTCallback())
trainer.fit(model)
```
#### Example 2: Introduce Sparsity with the SparseMLCallback to Accelerate Inference
We can introduce sparsity during fine-tuning with [SparseML](https://github.com/neuralmagic/sparseml), which ultimately allows us to leverage the [DeepSparse](https://github.com/neuralmagic/deepsparse) engine to see performance improvements at inference time.
```python
from pytorch_lightning import LightningModule, Trainer
import torchvision.models as models
from pl_bolts.callbacks import SparseMLCallback
class VisionModel(LightningModule):
def __init__(self):
super().__init__()
self.model = models.vgg19_bn(pretrained=True)
...
model = VisionModel()
trainer = Trainer(gpus=1, callbacks=SparseMLCallback(recipe_path="recipe.yaml"))
trainer.fit(model)
```
## Are specific research implementations supported?
We'd like to encourage users to contribute general components that will help a broad range of problems, however components that help specifics domains will also be welcomed!
For example a callback to help train SSL models would be a great contribution, however the next greatest SSL model from your latest paper would be a good contribution to [Lightning Flash](https://github.com/PyTorchLightning/lightning-flash).
Use [Lightning Flash](https://github.com/PyTorchLightning/lightning-flash) to train, predict and serve state-of-the-art models for applied research. We suggest looking at our [VISSL](https://lightning-flash.readthedocs.io/en/latest/integrations/vissl.html) Flash integration for SSL based tasks.
## Contribute!
Bolts is supported by the PyTorch Lightning team and the PyTorch Lightning community!
Join our Slack and/or read our [CONTRIBUTING](./.github/CONTRIBUTING.md) guidelines to get help becoming a contributor!
______________________________________________________________________
## License
Please observe the Apache 2.0 license that is listed in this repository.
In addition the Lightning framework is Patent Pending.
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