Deep Learning components for extending PyTorch Lightning
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Pip / Conda
pip install lightning-boltsOther installations
Install bleeding-edge (no guarantees)
pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgradeTo install all optional dependencies
pip install lightning-bolts["extra"]Bolts provides a variety of components to extend PyTorch Lightning such as callbacks & datasets, for applied research and production.
- Sept 22: Leverage Sparsity for Faster Inference with Lightning Flash and SparseML
- Aug 26: Fine-tune Transformers Faster with Lightning Flash and Torch ORT
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trainer.fit(model)We've deprecated a bunch of specific model research, primarily because they've grown outdated or support for them was not possible.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.
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!
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)
Please observe the Apache 2.0 license that is listed in this repository. In addition the Lightning framework is Patent Pending.