Metadata-Version: 2.1
Name: pytorch-lightning-bolts
Version: 0.2.2
Summary: PyTorch Lightning Bolts is a community contribution for ML researchers.
Home-page: https://github.com/PyTorchLightning/pytorch-lightning-bolts
Author: PyTorchLightning et al.
Author-email: name@pytorchlightning.ai
License: Apache-2.0
Download-URL: https://github.com/PyTorchLightning/pytorch-lightning-bolts
Project-URL: Bug Tracker, https://github.com/PyTorchLightning/pytorch-lightning-bolts/issues
Project-URL: Documentation, https://pytorch-lightning-bolts.rtfd.io/en/latest/
Project-URL: Source Code, https://github.com/PyTorchLightning/pytorch-lightning-bolts
Description: <div align="center">
        
        ![Logo](https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/docs/source/_images/logos/lightning_logo.png)
        
        # PyTorch Lightning Bolts    
        
        **Pretrained SOTA Deep Learning models, callbacks and more for research and production with PyTorch Lightning and PyTorch**
        
        [![PyPI Status](https://badge.fury.io/py/pytorch-lightning-bolts.png)](https://badge.fury.io/py/pytorch-lightning-bolts)
        [![PyPI Status](https://pepy.tech/badge/pytorch-lightning-bolts)](https://pepy.tech/project/pytorch-lightning-bolts)
        [![codecov](https://codecov.io/gh/PyTorchLightning/pytorch-lightning-bolts/branch/master/graph/badge.png)](https://codecov.io/gh/PyTorchLightning/pytorch-lightning-bolts)
        
        [![Documentation Status](https://readthedocs.org/projects/pytorch-lightning-bolts/badge/?version=latest)](https://pytorch-lightning-bolts.readthedocs.io/en/latest/)
        [![Slack](https://img.shields.io/badge/slack-chat-green.png?logo=slack)](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A)
        [![Discourse status](https://img.shields.io/discourse/status?server=https%3A%2F%2Fforums.pytorchlightning.ai)](https://forums.pytorchlightning.ai/)
        [![license](https://img.shields.io/badge/License-Apache%202.0-blue.png)](https://github.com/PytorchLightning/pytorch-lightning/blob/master/LICENSE)
        [![Next Release](https://img.shields.io/badge/Next%20Release-Oct%2005-purple.png)](https://shields.io/)
        
        </div>
        
        ---
        
        ## Trending contributors
        
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/0)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/0)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/1)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/1)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/2)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/2)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/3)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/3)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/4)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/4)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/5)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/5)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/6)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/6)
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/images/7)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning-bolts/links/7)
        
        
        ## Continuous Integration
        
        <center>
        
        | System / PyTorch ver. | 1.4 (min. req.) | 1.6 (latest) |
        | :---: | :---: | :---: |
        | Linux py3.6 / py3.7 / py3.8 | ![CI testing](https://github.com/PyTorchLightning/pytorch-lightning-bolts/workflows/CI%20testing/badge.png?branch=master&event=push) | ![CI testing](https://github.com/PyTorchLightning/pytorch-lightning-bolts/workflows/CI%20testing/badge.png?branch=master&event=push) |
        | OSX py3.6 / py3.7 / py3.8 | ![CI testing](https://github.com/PyTorchLightning/pytorch-lightning-bolts/workflows/CI%20testing/badge.png?branch=master&event=push) | ![CI testing](https://github.com/PyTorchLightning/pytorch-lightning-bolts/workflows/CI%20testing/badge.png?branch=master&event=push) |
        | Windows py3.6 / py3.7 / py3.8 | wip | wip |
        
        </center>
        
        ## Install
        
        Simple installation from PyPI
        ```bash
        pip install pytorch-lightning-bolts
        ```
        
        Install bleeding-edge (no guarantees)   
        ```bash
        pip install git+https://github.com/PytorchLightning/pytorch-lightning-bolts.git@master --upgrade
        ```
        
        ## Docs
        
        - [master](https://pytorch-lightning-bolts.readthedocs.io/en/latest)
        - [stable](https://pytorch-lightning-bolts.readthedocs.io/en/stable)
        - [0.2.0](https://pytorch-lightning-bolts.readthedocs.io/en/0.2.0/)
        - [0.1.1](https://pytorch-lightning-bolts.readthedocs.io/en/0.1.1/)
        
        ## What is Bolts
        
        Bolts is a Deep learning research and production toolbox of:
        
        - SOTA pretrained models.
        - Model components.
        - Callbacks.
        - Losses.
        - Datasets.
        
        ## Main Goals of Bolts
        
        The main goal of Bolts is to enable rapid model idea iteration.
        
        #### Example 1: Finetuning on data
        
        ```python
        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/simclr-cifar10-v1-exp12_87_52/epoch%3D960.ckpt'
        simclr = SimCLR.load_from_checkpoint(weight_path, strict=False)
        
        simclr.freeze()
        
        # finetune
        ```
        
        #### Example 2: Subclass and ideate
        
        ```python
        from pl_bolts.models import ImageGPT
        from pl_bolts.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 learning
        
        Great! 
        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](https://colab.research.google.com/drive/13glsKiwMu1-H24cBLYaWdJ4_TxC2Z3ox?usp=sharing)
        
        ```python
        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, loaders.train_dataloader(), 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.
        
        ## Team
        
        Bolts is supported by the PyTorch Lightning team and the PyTorch Lightning community!
        
        
Keywords: deep learning,pytorch,AI
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
