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lightning/notebooks/README.md

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add lightning colab tutorial notebooks (#3591) * :zap: add lightning colab tutorial notebooks * Update notebooks/01-mnist-hello-world.ipynb * Update notebooks/03-basic-gan.ipynb * Update notebooks/04-transformers-text-classification.ipynb * Update notebooks/02-datamodules.ipynb * add jupytext * :bug: comment ipynb related things in conf.py * :fire: remove .py files Co-authored-by: Jirka Borovec <jirka@pytorchlightning.ai>
2020-09-22 01:31:59 +00:00
# Lightning Notebooks ⚡
## Official Notebooks
You can easily run any of the official notebooks by clicking the 'Open in Colab' links in the table below :smile:
Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR (#4818) * Add a notebook example to reach a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning * Remove outputs * PR Feedback * some changes * some more changes Co-authored-by: chaton <thomas@grid.ai> Co-authored-by: rohitgr7 <rohitgr1998@gmail.com>
2020-12-10 10:56:18 +00:00
| Notebook | Description | Colab Link |
| :----------------------- | :----------------------------------------------------------------------------------- | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
| **MNIST Hello World** | Train your first Lightning Module on the classic MNIST Handwritten Digits Dataset. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/01-mnist-hello-world.ipynb) |
| **Datamodules** | Learn about DataModules and train a dataset-agnostic model on MNIST and CIFAR10. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/02-datamodules.ipynb) |
| **GAN** | Train a GAN on the MNIST Dataset. Learn how to use multiple optimizers in Lightning. | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/03-basic-gan.ipynb) |
| **BERT** | Fine-tune HuggingFace Transformers models on the GLUE Benchmark | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/04-transformers-text-classification.ipynb) |
| **Trainer Flags** | Overview of the available Lightning `Trainer` flags | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/05-trainer-flags-overview.ipynb) |
| **94% Baseline CIFAR10** | Establish a quick baseline of ~94% accuracy on CIFAR10 using Resnet in Lightning | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/06-cifar10-baseline.ipynb) |