Metadata-Version: 2.1
Name: pytorch-lightning
Version: 1.1.7
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">
        
        <img src="https://github.com/PyTorchLightning/pytorch-lightning/raw/1.1.7/docs/source/_images/logos/lightning_logo-name.png" width="400px">
        
        
        **The lightweight PyTorch wrapper for high-performance AI research.
        Scale your models, not the boilerplate.**
        
        ---
        
        <p align="center">
          <a href="https://www.pytorchlightning.ai/">Website</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/1.1.7">Docs</a> •
          <a href="#examples">Examples</a> •
          <a href="#community">Community</a> •
          <a href="#grid-ai">Grid AI</a> •
          <a href="#licence">Licence</a>
        </p>
        
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        ###### *Codecov is > 90%+ but build delays may show less
        
        ---
        
        ## NEWS
        [Dec 2020 - Read about how Facebook uses Lightning to standardize deep learning across research and production teams](https://ai.facebook.com/blog/reengineering-facebook-ais-deep-learning-platforms-for-interoperability)
        
        ---
        
        ## PyTorch Lightning is just organized PyTorch
        Lightning disentangles PyTorch code to decouple the science from the engineering.
        
        
        ---
        
        ## Lightning Philosophy
        Lightning is designed with these principles in mind:
        
        Principle 1: Enable maximal flexibility.
        Principle 2: Abstract away unnecessary 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 [2 step guide](https://pytorch-lightning.readthedocs.io/en/1.1.7new-project.html)
        
        ---
        
        ## Inference
        Lightning is also designed for the fast inference AI researchers and production teams need to scale up things like BERT and self-supervised learning.
        Lightning can automatically export to ONNX or TorchScript for those cases.
        
        ---
        
        ## Continuous Integration
        <center>
        
        | System / PyTorch ver. | 1.3 (min. req.)* | 1.4 | 1.5 | 1.6 | 1.7 (latest) | 1.8 (nightly) |
        | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
        | Conda py3.7 [linux] | [![PyTorch & Conda](https://github.com/PyTorchLightning/pytorch-lightning/workflows/PyTorch%20&%20Conda/badge.svg?tag=1.1.7)](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.svg?tag=1.1.7)](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.svg?tag=1.1.7)](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.svg?tag=1.1.7)](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.svg?tag=1.1.7)](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.svg?tag=1.1.7)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22PyTorch+%26+Conda%22+branch%3Amaster) |
        | Linux py3.7 [GPUs**] | - | - | - | [![GPUs Status](http://104.154.220.231/api/badges/PyTorchLightning/pytorch-lightning/status.svg)](http://104.154.220.231/PyTorchLightning/pytorch-lightning) | - | - |
        | Linux py3.{6,7} [TPUs***] | - | - | - | [![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?tag=1.1.7)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22TPU+tests%22+branch%3Amaster) | [![TPU tests](https://github.com/PyTorchLightning/pytorch-lightning/workflows/TPU%20tests/badge.svg?tag=1.1.7)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22TPU+tests%22+branch%3Amaster) | - |
        | Linux py3.{6,7} | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?tag=1.1.7)](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.svg?tag=1.1.7)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
        | OSX py3.{6,7,8} | - | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?tag=1.1.7)](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.svg?tag=1.1.7)](https://github.com/PyTorchLightning/pytorch-lightning/actions?query=workflow%3A%22CI+testing%22) | - |
        | Windows py3.{6,7,8} | [![CI complete testing](https://github.com/PyTorchLightning/pytorch-lightning/workflows/CI%20complete%20testing/badge.svg?tag=1.1.7)](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.svg?tag=1.1.7)](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
        ```
        _To get full package experience you can install also all optional dependencies with `pytorch-lightning['extra']` or for CPU users with `pytorch-lightning['cpu-extra']`._
        
        From Conda
        ```bash
        conda install pytorch-lightning -c conda-forge
        ```
        
        <!--  -->
        
        ### Step 1: 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 2: 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)
                self.log('train_loss', loss)
                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 3: 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/TPUs
        ```python
        # 8 GPUs
        trainer = Trainer(max_epochs=1, gpus=8)
        
        # 256 GPUs
        trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
        
        # TPUs
        trainer = Trainer(tpu_cores=8)
        ```
        
        #### And even export for production via onnx or torchscript
        ```python
        # torchscript
        autoencoder = LitAutoEncoder()
        torch.jit.save(autoencoder.to_torchscript(), "model.pt")
        
        # onnx
        with tempfile.NamedTemporaryFile(suffix='.onnx', delete=False) as tmpfile:
            autoencoder = LitAutoEncoder()
            input_sample = torch.randn((1, 64))
            autoencoder.to_onnx(tmpfile.name, input_sample, export_params=True)
            os.path.isfile(tmpfile.name)
        ```
        
        #### For advanced users, you can still own complex training loops
        
        ```python
        class LitAutoEncoder(pl.LightningModule):
            def training_step(self, batch, batch_idx, optimizer_idx):
                # access your optimizers with use_pl_optimizer=False. Default is True
                (opt_a, opt_b) = self.optimizers(use_pl_optimizer=True)
        
                loss_a = ...
                self.manual_backward(loss_a, opt_a)
                opt_a.step()
                opt_a.zero_grad()
        
                loss_b = ...
                self.manual_backward(loss_b, opt_b, retain_graph=True)
                self.manual_backward(loss_b, opt_b)
                opt_b.step()
                opt_b.zero_grad()
        ```
        ---
        
        ## 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/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/06-mnist-tpu-training.ipynb)
        
        ###### 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://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dqn-models)
        - [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. [Search through the Discussions](https://github.com/PyTorchLightning/pytorch-lightning/discussions).
        3. [Look it up in our forum (or add a new question)](https://forums.pytorchlightning.ai)
        4. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A).
        
        ### Funding
        Building open-source software with only a few part-time people is hard!
        
        [We're venture funded](https://techcrunch.com/2020/10/08/grid-ai-raises-18-6m-series-a-to-help-ai-researchers-and-engineers-bring-their-models-to-production/)
        and backed by some of the top VC funds in the world, [Index Ventures](https://www.indexventures.com/companies/), [Bain Capital Ventures](https://www.baincapitalventures.com/portfolio/), [First Minute Capital](https://firstminute.capital/companies).
        
        Their funding ensures we can continue to build awesome tooling like Grid, give you around the clock support,
        hire a full-time staff, attend conferences, and move faster through implementing features you request.
        
        To supercharge your research and production work, visit our [Grid.ai platform](https://www.grid.ai/)
        
        ---
        
        ## Grid AI
        Grid AI is our native platform for training models at scale on the cloud!
        
        **Sign up for [early access here](https://www.grid.ai/)**
        
        To use grid, take your regular command:
        
        ```
            python my_model.py --learning_rate 1e-6 --layers 2 --gpus 4
        ```
        
        And change it to use the grid train command:
        
        ```
            grid train --grid_gpus 4 my_model.py --learning_rate 'uniform(1e-6, 1e-1, 20)' --layers '[2, 4, 8, 16]'
        ```
        
        The above command will launch (20 * 4) experiments each running on 4 GPUs (320 GPUs!) - by making ZERO changes to
        your code.
        
        ---
        
        ## 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
Provides-Extra: cpu
Provides-Extra: cpu-extra
