Sxela / lightning-bolts

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

Deep Learning components for extending PyTorch Lightning

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Installation • Latest Docs • Stable Docs • About • Community • Website • Grid AI • Licence

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Getting Started

Pip / Conda

pip install lightning-bolts

Other installations

Install bleeding-edge (no guarantees)

pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgrade

To install all optional dependencies

pip install lightning-bolts["extra"]

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
• Aug 26: Fine-tune Transformers Faster with Lightning Flash and Torch ORT

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 for more details.

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, which ultimately allows us to leverage the DeepSparse engine to see performance improvements at inference time.

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We've deprecated a bunch of specific model research, primarily because they've grown outdated or support for them was not possible. This also means in the future we'll not accept any model specific research. 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.

Use Lightning Flash to train, predict and serve state-of-the-art models for applied research. We suggest looking at our VISSL Flash integration for SSL based tasks.

See Deprecated Modules for more information.

Contribute!

Bolts is supported by the PyTorch Lightning team and the PyTorch Lightning community!

Join our Slack and/or read our CONTRIBUTING guidelines to get help becoming a contributor!

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Citation

To cite bolts use:

@article{falcon2020framework,
  title={A Framework For Contrastive Self-Supervised Learning And Designing A New Approach},
  author={Falcon, William and Cho, Kyunghyun},
  journal={arXiv preprint arXiv:2009.00104},
  year={2020}
}

To cite other contributed models or modules, please cite the authors directly (if they don't have bibtex, ping the authors on a GH issue)

Licence

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