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https://github.com/lightning-universe/lightning-bolts

Toolbox of models, callbacks, and datasets for AI/ML researchers.
https://github.com/lightning-universe/lightning-bolts

ai gan image-processing machine-learning natural-language-processing pytorch supervised-learning

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Toolbox of models, callbacks, and datasets for AI/ML researchers.

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README

        

**Deep Learning components for extending PyTorch Lightning**

______________________________________________________________________


Installation
Latest Docs
Stable Docs
About
Community
Website
License

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______________________________________________________________________

## Getting Started

Pip / Conda

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

Other installations

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"]
```

## 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.