pytorch |
您所在的位置：网站首页 › installing package pytorch › pytorch |
pytorch
WARNING: Deprecated in favor of Lightning Bolts - https://pypi.org/project/lightning-bolts
Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch Website • Installation • Main goals • latest Docs • stable Docs • Community • Grid AI • Licence
OSX py3.{6,8}
Windows py3.7*
* testing just the package itself, we skip full test suite - excluding tests folder
Install
View install
Simple installation from PyPI pip install lightning-boltsInstall bleeding-edge (no guarantees) pip install git+https://github.com/PytorchLightning/lightning-bolts.git@master --upgradeIn case you want to have full experience you can install all optional packages at once pip install lightning-bolts["extra"] What is BoltsBolts is a Deep learning research and production toolbox of: SOTA pretrained models. Model components. Callbacks. Losses. Datasets. Main Goals of BoltsThe main goal of Bolts is to enable rapid model idea iteration. Example 1: Finetuning on data from pl_bolts.models.self_supervised import SimCLR from pl_bolts.models.self_supervised.simclr.transforms import SimCLRTrainDataTransform, SimCLREvalDataTransform import pytorch_lightning as pl # data train_data = DataLoader(MyDataset(transforms=SimCLRTrainDataTransform(input_height=32))) val_data = DataLoader(MyDataset(transforms=SimCLREvalDataTransform(input_height=32))) # model weight_path = 'https://pl-bolts-weights.s3.us-east-2.amazonaws.com/simclr/bolts_simclr_imagenet/simclr_imagenet.ckpt' simclr = SimCLR.load_from_checkpoint(weight_path, strict=False) simclr.freeze() # finetune Example 2: Subclass and ideate from pl_bolts.models import ImageGPT from pl_bolts.models.self_supervised import SimCLR class VideoGPT(ImageGPT): def training_step(self, batch, batch_idx): x, y = batch x = _shape_input(x) logits = self.gpt(x) simclr_features = self.simclr(x) # ----------------- # do something new with GPT logits + simclr_features # ----------------- loss = self.criterion(logits.view(-1, logits.size(-1)), x.view(-1).long()) logs = {"loss": loss} return {"loss": loss, "log": logs} Who is Bolts for? Corporate production teams Professional researchers Ph.D. students Linear + Logistic regression heroes I don't need deep learningGreat! We have LinearRegression and LogisticRegression implementations with numpy and sklearn bridges for datasets! But our implementations work on multiple GPUs, TPUs and scale dramatically... Check out our Linear Regression on TPU demo from pl_bolts.models.regression import LinearRegression from pl_bolts.datamodules import SklearnDataModule from sklearn.datasets import load_boston import pytorch_lightning as pl # sklearn dataset X, y = load_boston(return_X_y=True) loaders = SklearnDataModule(X, y) model = LinearRegression(input_dim=13) # try with gpus=4! # trainer = pl.Trainer(gpus=4) trainer = pl.Trainer() trainer.fit(model, train_dataloader=loaders.train_dataloader(), val_dataloaders=loaders.val_dataloader()) trainer.test(test_dataloaders=loaders.test_dataloader()) Is this another model zoo?No! Bolts is unique because models are implemented using PyTorch Lightning and structured so that they can be easily subclassed and iterated on. For example, you can override the elbo loss of a VAE, or the generator_step of a GAN to quickly try out a new idea. The best part is that all the models are benchmarked so you won't waste time trying to "reproduce" or find the bugs with your implementation. TeamBolts is supported by the PyTorch Lightning team and the PyTorch Lightning community! LicencePlease observe the Apache 2.0 license that is listed in this repository. In addition the Lightning framework is Patent Pending. CitationTo 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) |
【本文地址】