lightning-bolts 0.7.0
Lightning Bolts is a community contribution for ML researchers.
License: Apache-2.0
Requires Python: >=3.8
Keywords: deep learning,pytorch,AI

<div align="center">

<img src="https://github.com/Lightning-AI/lightning-bolts/raw/0.7.0/docs/source/_images/logos/bolts_logo.png" width="400px">

**Deep Learning components for extending PyTorch Lightning**

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<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/0.7.0">Stable Docs</a> •
  <a href="#what-is-bolts">About</a> •
  <a href="#team">Community</a> •
  <a href="https://www.lightning.ai/">Website</a> •
  <a href="#license">License</a>
</p>

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## Getting Started

Pip / Conda

```bash
pip install lightning-bolts
```

<details>
  <summary>Other installations</summary>

Install bleeding-edge (no guarantees)

```bash
pip install https://github.com/Lightning-Universe/lightning-bolts/archive/refs/heads/master.zip
```

To install all optional dependencies

```bash
pip install lightning-bolts["extra"]
```

</details>

## What is Bolts?

Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production.

#### 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 specific 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!

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## License

Please observe the Apache 2.0 license that is listed in this repository.
In addition, the Lightning framework is Patent Pending.