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) License To cite bolts use: Framework For Contrastive Self-Supervised Learning And Designing A New Approach},Īuthor=, Join our Slack and/or read our CONTRIBUTING guidelines to get help becoming a contributor! Contribute!īolts is supported by the PyTorch Lightning team and the PyTorch Lightning community! See Deprecated Modules for more information. We suggest looking at our VISSL Flash integration for SSL based tasks. Use Lightning Flash to train, predict and serve state-of-the-art models for applied 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!įor 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. This also means in the future we'll not accept any model specific research. We've deprecated a bunch of specific model research, primarily because they've grown outdated or support for them was not possible. fit ( model ) Are specific research implementations supported? model = VisionModel () trainer = Trainer ( gpus = 1, callbacks = SparseMLCallback ( recipe_path = "recipe.yaml" )) trainer. from pytorch_lightning import LightningModule, Trainer import torchvision.models as models from pl_bolts.callbacks import SparseMLCallback class VisionModel ( LightningModule ): def _init_ ( self ): super (). 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. fit ( model ) Example 2: Introduce Sparsity with the SparseMLCallback to Accelerate Inference model = VisionModel () trainer = Trainer ( gpus = 1, callbacks = ORTCallback ()) trainer. from pytorch_lightning import LightningModule, Trainer import torchvision.models as models from pl_bolts.callbacks import ORTCallback class VisionModel ( LightningModule ): def _init_ ( self ): super (). Torch ORT converts your model into an optimized ONNX graph, speeding up training & inference when using NVIDIA or AMD GPUs. Aug 26: Fine-tune Transformers Faster with Lightning Flash and Torch ORTĮxample 1: Accelerate Lightning Training with the Torch ORT Callback.
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