Metadata-Version: 2.1
Name: pytorch-lightning
Version: 0.10.0
Summary: PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate.
Home-page: https://github.com/PyTorchLightning/pytorch-lightning
Author: William Falcon et al.
Author-email: waf2107@columbia.edu
License: Apache-2.0
Download-URL: https://github.com/PyTorchLightning/pytorch-lightning
Project-URL: Bug Tracker, https://github.com/PyTorchLightning/pytorch-lightning/issues
Project-URL: Documentation, https://pytorch-lightning.rtfd.io/en/latest/
Project-URL: Source Code, https://github.com/PyTorchLightning/pytorch-lightning
Description: <div align="center">
        
        ![Logo](https://github.com/PyTorchLightning/pytorch-lightning/raw/0.10.0/docs/source/_images/logos/lightning_logo.png)
        
        # PyTorch Lightning
        
        **The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.**
        
        <p align="center">
          <a href="https://www.youtube.com/watch?v=DbESHcCoWbM&t=2s">Masterclass</a> •
          <a href="#key-features">Key Features</a> •
          <a href="#how-to-use">How To Use</a> •
          <a href="https://pytorch-lightning.readthedocs.io/en/stable/">Docs</a> •
          <a href="#examples">Examples</a> •
          <a href="#community">Community</a> •
          <a href="#licence">Licence</a>
        </p>
        
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        ###### *Codecov is > 90%+ but build delays may show less
        
        ---
        
        ## PyTorch Lightning is just organized PyTorch
        Lightning disentangles PyTorch code to decouple the science from the engineering.
        ![PT to PL](/docs/source/_images/general/pl_quick_start_full_compressed.gif)
        
        ---
        
        ## Lightning Philosophy
        Lightning is designed with these principles in mind:
        
        Principle 1: Enable maximal flexibility.   
        Principle 2: Abstract away unecessary boilerplate, but make it accessible when needed.    
        Principle 3: Systems should be self-contained (ie: optimizers, computation code, etc).    
        Principle 4: Deep learning code should be organized into 4 distinct categories.    
        
          - Research code (the LightningModule).
          - Engineering code (you delete, and is handled by the Trainer).
          - Non-essential research code (logging, etc... this goes in Callbacks).
          - Data (use PyTorch Dataloaders or organize them into a LightningDataModule).
        
        Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
        
        Get started with our [3 steps guide](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
        
        ---
        ## Trending contributors
        
        [![](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/images/0)](https://sourcerer.io/fame/williamFalcon/pytorchlightning/pytorch-lightning/links/0)
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        ---
        
        ## Continuous Integration
        <center>
        
        | System / PyTorch ver. | 1.3 (min. req.)* | 1.4 | 1.5 | 1.6 (latest) | 1.7 (nightly) |
        | :---: | :---: | :---: | :---: | :---: | :---: |
        | Conda py3.7 [linux] | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) |
        | Linux py3.7 [GPUs**] | - | - |[![Build Status](http://104.154.220.231/api/badges/PyTorchLightning/pytorch-lightning/status.png)](http://104.154.220.231/PyTorchLightning/pytorch-lightning) | - | - |
        | Linux py3.7 [TPUs***] | - | - | - | [![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.png)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22TPU+tests%22+branch%3Amaster) | - |
        | Linux py3.6 / py3.7 / py3.8 | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | - | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
        | OSX py3.6 / py3.7 | - | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
        | Windows py3.6 / py3.7 / py3.8 | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - | - | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.png?event=push)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
        
        - _\* `torch>=1.4` is the minimal pytorch version for Python 3.8_
        - _\** tests run on two NVIDIA K80_
        - _\*** tests run on Google GKE TPUv2/3_
        - _TPU w/ py3.6/py3.7 means we support Colab and Kaggle env._
        
        </center>
        
        ---
        
        ## How To Use
        
        #### Step 0: Install
        
        Simple installation from PyPI
        ```bash
        pip install pytorch-lightning
        ```
        
        From Conda
        ```bash
        conda install pytorch-lightning -c conda-forge
        ```
        
        Install bleeding-edge (no guarantees)   
        ```bash
        pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@master --upgrade
        ```
        
        #### Step 0: Add these imports
        
        ```python
        import os
        import torch
        from torch import nn
        import torch.nn.functional as F
        from torchvision.datasets import MNIST
        from torch.utils.data import DataLoader, random_split
        from torchvision import transforms
        import pytorch_lightning as pl
        ```
        
        #### Step 1: Define a LightningModule (nn.Module subclass)
        A LightningModule defines a full *system* (ie: a GAN, autoencoder, BERT or a simple Image Classifier).
        
        ```python
        class LitAutoEncoder(pl.LightningModule):
        
            def __init__(self):
                super().__init__()
                self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))
                self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))
            
            def forward(self, x):
                # in lightning, forward defines the prediction/inference actions
                embedding = self.encoder(x)
                return embedding
        
            def training_step(self, batch, batch_idx):
                # training_step defined the train loop. It is independent of forward
                x, y = batch
                x = x.view(x.size(0), -1)
                z = self.encoder(x)
                x_hat = self.decoder(z)
                loss = F.mse_loss(x_hat, x)
                return loss
        
            def configure_optimizers(self):
                optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
                return optimizer
        ```
        
        ###### Note: Training_step defines the training loop. Forward defines how the LightningModule behaves during inference/prediction.
        
        #### Step 2: Train!
        
        ```python
        dataset = MNIST(os.getcwd(), download=True, transform=transforms.ToTensor())
        train, val = random_split(dataset, [55000, 5000])
        
        autoencoder = LitAutoEncoder()
        trainer = pl.Trainer()
        trainer.fit(autoencoder, DataLoader(train), DataLoader(val))
        ```
        
        #### And without changing a single line of code, you could run on GPUs
        ```python
        # 8 GPUs
        trainer = Trainer(max_epochs=1, gpus=8)
        
        # 256 GPUs
        trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
        ```
        
        Or TPUs
        ```python
        # Distributes TPU core training
        trainer = Trainer(tpu_cores=8)
        
        # Single TPU core training
        trainer = Trainer(tpu_cores=[1])
        ```
        
        ---
        
        ## Key Features
        
        * Scale your models to run on any hardware (CPU, GPUs, TPUs) without changing your model
        * Making code more readable by decoupling the research code from the engineering
        * Easier to reproduce
        * Less error prone by automating most of the training loop and tricky engineering
        * Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate
        * Lightning has out-of-the-box integration with the popular logging/visualizing frameworks ([Tensorboard](https://pytorch.org/docs/stable/tensorboard.html), [MLFlow](https://mlflow.org/), [Neptune.ai](https://neptune.ai/), [Comet.ml](https://www.comet.ml/site/), [Wandb](https://www.wandb.com/)).
        * [Tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests). We test every combination of PyTorch and Python supported versions, every OS, multi GPUs and even TPUs.
        * Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
        
        ### Lightning automates 40+ parts of DL/ML research
        - GPU training
        - Distributed GPU (cluster) training
        - TPU training
        - EarlyStopping
        - Logging/Visualizing
        - Checkpointing
        - Experiment management
        - [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases)
        
        ---
        
        ## Examples
        
        ###### Hello world
        [MNIST hello world](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/01-mnist-hello-world.ipynb)  
        [MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)
        
        ###### Contrastive Learning
        [BYOL](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#byol)    
        [CPC v2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#cpc-v2)    
        [Moco v2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#moco-v2)    
        [SIMCLR](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html#simclr) 
        
        ###### NLP
        [BERT](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb)   
        [GPT-2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2) 
        
        
        ###### Reinforcement Learning
        [DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5)   
        [Dueling-DQN](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dueling-dqn)   
        [Reinforce](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#reinforce)
        
        ###### Vision
        [GAN](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/03-basic-gan.ipynb)   
        
        ###### Classic ML
        [Logistic Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#logistic-regression)   
        [Linear Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#linear-regression)    
        
        ---
        
        ## Community
        
        The lightning community is maintained by
        - [16 core contributors](https://pytorch-lightning.readthedocs.io/en/latest/governance.html) who are all a mix of professional engineers, Research Scientists, Ph.D. students from top AI labs.
        - 280+ community contributors.
        
        Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
        
        ### Asking for help
        If you have any questions please:
        1. [Read the docs](https://pytorch-lightning.rtfd.io/en/latest/).
        2. [Look it up in our forum (or add a new question)](https://forums.pytorchlightning.ai/)
        2. [Search through the issues](https://github.com/PytorchLightning/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question).
        3. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A).
        4. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning.
        
        ### Funding
        Building open-source software with only a few part-time people is hard! We've secured funding to make sure we can
        hire a full-time staff, attend conferences, and move faster through implementing features you request.
        
        Our goal is to build an incredible research platform and a big supportive community. Many open-source projects
        have gone on to fund operations through things like support and special help for big corporations!
        
        If you are one of these corporations, please feel free to reach out to will@pytorchlightning.ai!
        
        ---
        
        ## Licence
        
        Please observe the Apache 2.0 license that is listed in this repository. In addition
        the Lightning framework is Patent Pending.
        
        ## BibTeX
        If you want to cite the framework feel free to use this (but only if you loved it 😊):
        
        ```bibtex
        @article{falcon2019pytorch,
          title={PyTorch Lightning},
          author={Falcon, WA},
          journal={GitHub. Note: https://github.com/PyTorchLightning/pytorch-lightning},
          volume={3},
          year={2019}
        }
        ```
        
Keywords: deep learning,pytorch,AI
Platform: UNKNOWN
Classifier: Environment :: Console
Classifier: Natural Language :: English
Classifier: Development Status :: 4 - Beta
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
Provides-Extra: examples
Provides-Extra: loggers
Provides-Extra: extra
Provides-Extra: test
Provides-Extra: dev
Provides-Extra: all
