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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
Last synced: about 1 month ago
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Toolbox of models, callbacks, and datasets for AI/ML researchers.
- Host: GitHub
- URL: https://github.com/Lightning-Universe/lightning-bolts
- Owner: Lightning-Universe
- License: apache-2.0
- Created: 2020-03-25T16:03:30.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-04-08T11:54:55.000Z (2 months ago)
- Last Synced: 2024-05-02T06:03:48.322Z (about 2 months ago)
- Topics: ai, gan, image-processing, machine-learning, natural-language-processing, pytorch, supervised-learning
- Language: Python
- Homepage: https://lightning-bolts.readthedocs.io
- Size: 5.56 MB
- Stars: 1,645
- Watchers: 23
- Forks: 320
- Open Issues: 60
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: .github/CONTRIBUTING.md
- License: LICENSE
- Codeowners: .github/CODEOWNERS
Lists
- AI - PyTorch Lightning Bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers. (Python / General-Purpose Machine Learning)
- awesome-stars - Lightning-Universe/lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers. (Python)
- awesome-stars - Lightning-Universe/lightning-bolts - Toolbox of models, callbacks, and datasets for AI/ML researchers. (Python)
README
**Deep Learning components for extending PyTorch Lightning**
______________________________________________________________________
Installation •
Latest Docs •
Stable Docs •
About •
Community •
Website •
License
[](https://badge.fury.io/py/lightning-bolts)
[](https://pepy.tech/project/lightning-bolts)
[](https://dev.azure.com/Lightning-AI/compatibility/_build/latest?definitionId=51&branchName=master)
[](https://codecov.io/gh/Lightning-Universe/lightning-bolts)
[](https://lightning-bolts.readthedocs.io/en/latest/)
[](https://www.pytorchlightning.ai/community)
[](https://github.com/PytorchLightning/lightning-bolts/blob/master/LICENSE)
[](https://zenodo.org/badge/latestdoi/250025410)
______________________________________________________________________
## 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!
______________________________________________________________________
## License
Please observe the Apache 2.0 license that is listed in this repository.
In addition, the Lightning framework is Patent Pending.