lightning-bolts
Toolbox of models, callbacks, and datasets for AI/ML researchers.
Stars: 1656
Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production. Users can accelerate Lightning training with the Torch ORT Callback to optimize ONNX graph for faster training & inference. Additionally, users can introduce sparsity with the SparseMLCallback to accelerate inference by leveraging the DeepSparse engine. Specific research implementations are encouraged, with contributions that help train SSL models and integrate with Lightning Flash for state-of-the-art models in applied research.
README:
Deep Learning components for extending PyTorch Lightning
Installation • Latest Docs • Stable Docs • About • Community • Website • License
Pip / Conda
pip install lightning-boltsOther installations
Install bleeding-edge (no guarantees)
pip install https://github.com/Lightning-Universe/lightning-bolts/archive/refs/heads/master.zipTo install all optional dependencies
pip install lightning-bolts["extra"]Bolts package provides a variety of components to extend PyTorch Lightning, such as callbacks & datasets, for applied research and production.
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)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)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.
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.
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!
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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