Readme changes (#3078)
* Readme changes * Update README.md * Update README.md * 0.9.0 readme * 0.9.0 readme Co-authored-by: William Falcon <waf2107@columbia.edu>
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README.md
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README.md
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**The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.**
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<p align="center">
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<a href="#pytorch-lightning-masterclass">Masterclass</a> •
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<a href="#key-features">Key Features</a> •
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<a href="#how-to-use">How To Use</a> •
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<a href="#docs">Docs</a> •
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<a href="#resources">Resources</a> •
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<a href="#community">Community</a> •
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<a href="#faq">FAQ</a> •
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<a href="#licence">Licence</a>
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</p>
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[](https://badge.fury.io/py/pytorch-lightning)
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[](https://pepy.tech/project/pytorch-lightning)
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@ -24,6 +35,21 @@
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###### *Codecov is > 90%+ but build delays may show less
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## PyTorch Lightning is just organized PyTorch
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Lightning disentangles PyTorch code to decouple the science from the engineering
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by organizing it into 4 categories:
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1. Research code (the LightningModule).
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2. Engineering code (you delete, and is handled by the Trainer).
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3. Non-essential research code (logging, etc... this goes in Callbacks).
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4. Data (use PyTorch Dataloaders or organize them into a LightningDataModule).
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Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
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Get started with our [3 steps guide](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
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---
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## Trending contributors
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@ -56,67 +82,10 @@
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</center>
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## Install
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Simple installation from PyPI
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```bash
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pip install pytorch-lightning
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```
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From Conda
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```bash
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conda install pytorch-lightning -c conda-forge
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```
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## Docs
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- [master](https://pytorch-lightning.readthedocs.io/en/latest)
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- [stable](https://pytorch-lightning.readthedocs.io/en/stable)
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- [0.9.0](https://pytorch-lightning.readthedocs.io/en/0.9.0/)
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- [0.8.5](https://pytorch-lightning.readthedocs.io/en/0.8.5/)
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- [0.8.4](https://pytorch-lightning.readthedocs.io/en/0.8.4/)
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- [0.8.3](https://pytorch-lightning.readthedocs.io/en/0.8.3/)
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- [0.8.1](https://pytorch-lightning.readthedocs.io/en/0.8.1/)
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## PyTorch Lightning is just organized PyTorch
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Lightning disentangles PyTorch code to decouple the science from the engineering
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by organizing it into 4 categories:
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1. Research code (the LightningModule).
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2. Engineering code (you delete, and is handled by the Trainer).
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3. Non-essential research code (logging, etc... this goes in Callbacks).
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4. Data (use PyTorch Dataloaders or organize them into a LightningDataModule)
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Once you do this, you can train on multiple-GPUs, TPUs, CPUs and even in 16-bit precision without changing your code!
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Get started with our [QUICK START PAGE](https://pytorch-lightning.readthedocs.io/en/stable/new-project.html)
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---
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## README Table of Contents
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- [Masterclass](https://github.com/PytorchLightning/pytorch-lightning#pytorch-lightning-masterclass-new-lessons-weekly)
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- [Demo](https://github.com/PytorchLightning/pytorch-lightning#demo)
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- [Advanced Examples](https://github.com/PytorchLightning/pytorch-lightning#advanced-examples)
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- [Testing Rigour](https://github.com/PytorchLightning/pytorch-lightning#testing-rigour)
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- [Does Lightning slow my PyTorch](https://github.com/PytorchLightning/pytorch-lightning#does-lightning-slow-my-pytorch)
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- [Flexibility](https://github.com/PytorchLightning/pytorch-lightning#how-flexible-is-it)
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- [What does Lightning control for me?](https://github.com/PytorchLightning/pytorch-lightning#what-does-lightning-control-for-me)
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- [Converting to Lightning](https://github.com/PytorchLightning/pytorch-lightning#how-much-effort-is-it-to-convert)
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- [New Project](https://github.com/PytorchLightning/pytorch-lightning#starting-a-new-project)
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- [Why do I need Lightning?](https://github.com/PytorchLightning/pytorch-lightning#why-do-i-want-to-use-lightning)
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- [Support](https://github.com/PytorchLightning/pytorch-lightning#support)
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- [Supported Research use cases](https://github.com/PytorchLightning/pytorch-lightning#what-types-of-research-works)
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- [Visualization](https://github.com/PytorchLightning/pytorch-lightning#visualization)
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- [Tutorials](https://github.com/PytorchLightning/pytorch-lightning#tutorials)
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- [Asking for help](https://github.com/PytorchLightning/pytorch-lightning#asking-for-help)
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- [FAQ](https://github.com/PytorchLightning/pytorch-lightning#faq)
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- [Bleeding edge install](https://github.com/PytorchLightning/pytorch-lightning#bleeding-edge)
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- [Lightning team](https://github.com/PytorchLightning/pytorch-lightning#lightning-team)
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- [BibTex](https://github.com/PytorchLightning/pytorch-lightning#bibtex)
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---
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### [PyTorch Lightning Masterclass (new lessons weekly)](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
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## [PyTorch Lightning Masterclass](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
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### [New lessons weekly!](https://www.youtube.com/watch?v=DbESHcCoWbM&list=PLaMu-SDt_RB5NUm67hU2pdE75j6KaIOv2)
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<div style="display: flex">
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<div>
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@ -133,11 +102,45 @@ Get started with our [QUICK START PAGE](https://pytorch-lightning.readthedocs.io
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</div>
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</div>
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---
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## Key Features
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* Scale your models to run on any hardware (CPU, GPUs, TPUs) without changing your model
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* Making code more readable by decoupling the research code from the engineering
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* Easier to reproduce
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* Less error prone by automtaing most of the training loop and tricky engineering
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* Keeps all the flexibility (LightningModules are still PyTorch modules), but removes a ton of boilerplate
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* 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/)).
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* [Tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests). We test every combination og PyTorch and Python supported versions, every OS, multi GPUs and even TPUs.
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* Minimal running speed overhead (about 300 ms per epoch compared with pure PyTorch).
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### Lightning automates 40+ parts of DL/ML research
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- GPU training
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- Distributed GPU (cluster) training
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- TPU training
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- EarlyStopping
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- Logging/Visualizing
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- Checkpointing
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- Experiment management
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- [Full list here](https://pytorch-lightning.readthedocs.io/en/latest/#common-use-cases)
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---
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## Demo
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Here's a minimal example without a test loop.
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## How To Use
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##### Install
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Simple installation from PyPI
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```bash
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pip install pytorch-lightning
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```
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From Conda
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```bash
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conda install pytorch-lightning -c conda-forge
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```
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##### Here's a minimal example without a test loop.
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```python
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import os
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trainer.fit(model, DataLoader(train), DataLoader(val))
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```
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#### And without changing a single line of code, you could run on GPUs
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```python
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# 8 GPUs
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trainer = Trainer(max_epochs=1, gpus=8)
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# 256 GPUs
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trainer = Trainer(max_epochs=1, gpus=8, num_nodes=32)
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```
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Or TPUs
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```python
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# Distributes TPU core training
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trainer = Trainer(tpu_cores=8)
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# Single TPU core training
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trainer = Trainer(tpu_cores=[1])
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```
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---
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## Advanced Examples
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### Docs
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- [master](https://pytorch-lightning.readthedocs.io/en/latest)
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- [stable](https://pytorch-lightning.readthedocs.io/en/stable)
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- [0.9.0](https://pytorch-lightning.readthedocs.io/en/0.9.0/)
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- [0.8.5](https://pytorch-lightning.readthedocs.io/en/0.8.5/)
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- [0.8.4](https://pytorch-lightning.readthedocs.io/en/0.8.4/)
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- [0.8.3](https://pytorch-lightning.readthedocs.io/en/0.8.3/)
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- [0.8.1](https://pytorch-lightning.readthedocs.io/en/0.8.1/)
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---
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## Resources
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### Examples
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###### Hello world
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[MNIST hello world](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=gEulmrbxwaYL)
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[MNIST on TPUs](https://colab.research.google.com/drive/1-_LKx4HwAxl5M6xPJmqAAu444LTDQoa3)
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[BERT](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=7uQVI-xv9Ddj)
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[GPT-2](https://pytorch-lightning-bolts.readthedocs.io/en/latest/convolutional.html#gpt-2)
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###### Reinforcement Learning
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[DQN](https://colab.research.google.com/drive/1F_RNcHzTfFuQf-LeKvSlud6x7jXYkG31#scrollTo=NWvMLBDySQI5)
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[Dueling-DQN](https://pytorch-lightning-bolts.readthedocs.io/en/latest/reinforce_learn.html#dueling-dqn)
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[Logistic Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#logistic-regression)
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[Linear Regression](https://pytorch-lightning-bolts.readthedocs.io/en/latest/classic_ml.html#linear-regression)
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---
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## Testing Rigour
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All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).
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For every PR we test all combinations of:
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- PyTorch 1.3, 1.4, 1.5
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- Python 3.6, 3.7, 3.8
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- Linux, OSX, Windows
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- Multiple GPUs
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### Tutorials
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Check out our [introduction guide](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html) to get started.
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Or jump straight into [our tutorials](https://pytorch-lightning.readthedocs.io/en/latest/#tutorials).
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---
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## Does Lightning Slow my PyTorch
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No! Lightning is meant for research/production cases that require high-performance.
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## Community
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We have tests to ensure we get the EXACT same results in under 600 ms difference per epoch. In reality, lightning adds about a 300 ms overhead per epoch.
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[Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
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The lightning cimmunity is maintained by
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- [15 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.
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- 200+ community contributors.
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Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
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Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
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### Asking for help
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If you have any questions please:
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1. [read the docs](https://pytorch-lightning.rtfd.io/en/latest/).
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2. [Search through the issues](https://github.com/PytorchLightning/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question).
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3. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A).
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4. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning.
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### Funding
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Building open-source software with only a few part-time people is hard! We've secured funding to make sure we can
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hire a full-time staff, attend conferences, and move faster through implementing features you request.
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Our goal is to build an incredible research platform and a big supportive community. Many open-source projects
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have gone on to fund operations through things like support and special help for big corporations!
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If you are one of these corporations, please feel free to reach out to will@pytorchlightning.ai!
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---
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## FAQ
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**Starting a new project?**
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[Use our seed-project aimed at reproducibility!](https://github.com/PytorchLightning/pytorch-lightning-conference-seed)
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**Why lightning?**
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Although your research/production project might start simple, once you add things like GPU AND TPU training, 16-bit precision, etc, you end up spending more time engineering than researching. Lightning automates AND rigorously tests those parts for you.
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Lightning has 3 goals in mind:
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1. Maximal flexibility while abstracting out the common boilerplate across research projects.
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2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.
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3. Democratizing PyTorch power-user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.
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**Who is Lightning for?**
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- Professional researchers
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- Ph.D. students
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- Corporate production teams
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If you're just getting into deep learning, we recommend you learn PyTorch first! Once you've implemented a few models, come back and use all the advanced features of Lightning :)
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**What does lightning control for me?**
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Everything in Blue!
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This is how lightning separates the science (red) from engineering (blue).
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**How much effort is it to convert?**
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If your code is not a huge mess you should be able to organize it into a LightningModule in less than 1 hour.
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If your code IS a mess, then you needed to clean up anyhow ;)
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[Check out this step-by-step guide](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09).
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[Or watch this video](https://www.youtube.com/watch?v=QHww1JH7IDU).
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**How flexible is it?**
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## How flexible is it?
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As you see, you're just organizing your PyTorch code - there's no abstraction.
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And for the stuff that the Trainer abstracts out, you can [override any part](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#extensibility) you want to do things like implement your own distributed training, 16-bit precision, or even a custom backward pass.
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```python
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class LitModel(LightningModule):
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def optimizer_zero_grad(self, current_epoch, batch_idx, optimizer, opt_idx):
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optimizer.zero_grad()
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```
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For anything else you might need, we have an extensive [callback system](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html#callbacks) you can use to add arbitrary functionality not implemented by our team in the Trainer.
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## Who is Lightning for?
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- Professional researchers
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- Ph.D. students
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- Corporate production teams
|
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**What types of research works?**
|
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If you're just getting into deep learning, we recommend you learn PyTorch first! Once you've implemented a few models, come back and use all the advanced features of Lightning :)
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|
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## What does lightning control for me?
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Everything in Blue!
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This is how lightning separates the science (red) from engineering (blue).
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|
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## How much effort is it to convert?
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If your code is not a huge mess you should be able to organize it into a LightningModule in less than 1 hour.
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If your code IS a mess, then you needed to clean up anyhow ;)
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[Check out this step-by-step guide](https://towardsdatascience.com/from-pytorch-to-pytorch-lightning-a-gentle-introduction-b371b7caaf09).
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[Or watch this video](https://www.youtube.com/watch?v=QHww1JH7IDU).
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|
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## Starting a new project?
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[Use our seed-project aimed at reproducibility!](https://github.com/PytorchLightning/pytorch-lightning-conference-seed)
|
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|
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## Why do I want to use lightning?
|
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Although your research/production project might start simple, once you add things like GPU AND TPU training, 16-bit precision, etc, you end up spending more time engineering than researching. Lightning automates AND rigorously tests those parts for you.
|
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|
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## Support
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- [8 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.
|
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- 100+ community contributors.
|
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|
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Lightning is also part of the [PyTorch ecosystem](https://pytorch.org/ecosystem/) which requires projects to have solid testing, documentation and support.
|
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---
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## What types of research works?
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Anything! Remember, that this is just organized PyTorch code.
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The Training step defines the core complexity found in the training loop.
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#### Could be as complex as a seq2seq
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##### Could be as complex as a seq2seq
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```python
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# define what happens for training here
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@ -320,7 +373,7 @@ def training_step(self, batch, batch_idx):
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return {'loss': loss}
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```
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#### Or as basic as CNN image classification
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##### Or as basic as CNN image classification
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```python
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# define what happens for validation here
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@ -333,163 +386,39 @@ def validation_step(self, batch, batch_idx):
|
|||
return {'loss': loss}
|
||||
```
|
||||
|
||||
And without changing a single line of code, you could run on CPUs
|
||||
```python
|
||||
trainer = Trainer(max_epochs=1)
|
||||
```
|
||||
**Does Lightning Slow my PyTorch?**
|
||||
|
||||
No! Lightning is meant for research/production cases that require high-performance.
|
||||
|
||||
Or GPUs
|
||||
```python
|
||||
# 8 GPUs
|
||||
trainer = Trainer(max_epochs=1, gpus=8)
|
||||
We have tests to ensure we get the EXACT same results in under 600 ms difference per epoch. In reality, lightning adds about a 300 ms overhead per epoch.
|
||||
[Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
|
||||
|
||||
# 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])
|
||||
```
|
||||
|
||||
When you're done training, run the test accuracy
|
||||
```python
|
||||
trainer.test()
|
||||
```
|
||||
|
||||
## Visualization
|
||||
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/)
|
||||
- ...
|
||||
|
||||
|
||||
|
||||
|
||||
## 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)
|
||||
|
||||
## Tutorials
|
||||
Check out our [introduction guide](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html) to get started.
|
||||
Or jump straight into [our tutorials](https://pytorch-lightning.readthedocs.io/en/latest/#tutorials).
|
||||
|
||||
---
|
||||
|
||||
## Asking for help
|
||||
Welcome to the Lightning community!
|
||||
|
||||
If you have any questions, feel free to:
|
||||
1. [read the docs](https://pytorch-lightning.rtfd.io/en/latest/).
|
||||
2. [Search through the issues](https://github.com/PytorchLightning/pytorch-lightning/issues?utf8=%E2%9C%93&q=my++question).
|
||||
3. [Ask on stackoverflow](https://stackoverflow.com/questions/ask?guided=false) with the tag pytorch-lightning.
|
||||
4. [Join our slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A).
|
||||
|
||||
---
|
||||
|
||||
## FAQ
|
||||
**How do I use Lightning for rapid research?**
|
||||
|
||||
[Here's a walk-through](https://pytorch-lightning.readthedocs.io/en/latest/introduction_guide.html)
|
||||
|
||||
**Why was Lightning created?**
|
||||
|
||||
Lightning has 3 goals in mind:
|
||||
|
||||
1. Maximal flexibility while abstracting out the common boilerplate across research projects.
|
||||
2. Reproducibility. If all projects use the LightningModule template, it will be much much easier to understand what's going on and where to look! It will also mean every implementation follows a standard format.
|
||||
3. Democratizing PyTorch power-user features. Distributed training? 16-bit? know you need them but don't want to take the time to implement? All good... these come built into Lightning.
|
||||
Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
|
||||
|
||||
**How does Lightning compare with Ignite and fast.ai?**
|
||||
|
||||
[Here's a thorough comparison](https://medium.com/@_willfalcon/pytorch-lightning-vs-pytorch-ignite-vs-fast-ai-61dc7480ad8a).
|
||||
|
||||
**Is this another library I have to learn?**
|
||||
|
||||
Nope! We use pure Pytorch everywhere and don't add unnecessary abstractions!
|
||||
|
||||
**Are there plans to support Python 2?**
|
||||
|
||||
Nope.
|
||||
|
||||
**Are there plans to support virtualenv?**
|
||||
|
||||
Nope. Please use anaconda or miniconda.
|
||||
```bash
|
||||
conda activate my_env
|
||||
pip install pytorch-lightning
|
||||
```
|
||||
|
||||
## Custom installation
|
||||
|
||||
### Bleeding edge
|
||||
|
||||
If you can't wait for the next release, install the most up to date code with:
|
||||
* using GIT (locally clone whole repo with full history)
|
||||
```bash
|
||||
pip install git+https://github.com/PytorchLightning/pytorch-lightning.git@master --upgrade
|
||||
```
|
||||
* using instant zip (last state of the repo without git history)
|
||||
```bash
|
||||
pip install https://github.com/PytorchLightning/pytorch-lightning/archive/master.zip --upgrade
|
||||
```
|
||||
|
||||
### Any release installation
|
||||
|
||||
You can also install any past release `0.X.Y` from this repository:
|
||||
```bash
|
||||
pip install https://github.com/PytorchLightning/pytorch-lightning/archive/0.X.Y.zip --upgrade
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## Lightning team
|
||||
|
||||
#### Leads
|
||||
- William Falcon [(williamFalcon)](https://github.com/williamFalcon) (Lightning founder)
|
||||
- Jirka Borovec [(Borda)](https://github.com/Borda) (ghost :)
|
||||
- Ethan Harris [(ethanwharris)](https://github.com/ethanwharris) (Torchbearer founder)
|
||||
- Matthew Painter [(MattPainter01)](https://github.com/MattPainter01) (Torchbearer founder)
|
||||
- Justus Schock [(justusschock)](https://github.com/justusschock) (Former Core Member PyTorch Ignite)
|
||||
|
||||
#### Core Maintainers
|
||||
- Jeremy Jordan [(jeremyjordan)](https://github.com/jeremyjordan)
|
||||
- Tullie Murrell [(tullie)](https://github.com/tullie)
|
||||
- Adrian Wälchli [(awaelchli)](https://github.com/awaelchli)
|
||||
- Nicki Skafte [(skaftenicki)](https://github.com/SkafteNicki)
|
||||
- Peter Yu [(yukw777)](https://github.com/yukw777)
|
||||
- Rohit Gupta [(rohitgr7)](https://github.com/rohitgr7)
|
||||
- Nathan Raw[(nateraw)](https://github.com/nateraw)
|
||||
- Ananya Harsh Jha [(ananyahjha93)](https://github.com/ananyahjha93)
|
||||
- Teddy Koker [(teddykoker)](https://github.com/teddykoker)
|
||||
|
||||
#### Alumni
|
||||
- Nick Eggert [(neggert)](https://github.com/neggert)
|
||||
- Jeff Ling [(jeffling)](https://github.com/jeffling)
|
||||
|
||||
---
|
||||
|
||||
#### 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.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue