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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
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"source": [
"<a href=\"https://colab.research.google.com/github/PytorchLightning/pytorch-lightning/blob/master/notebooks/01-mnist-hello-world.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "i7XbLCXGkll9"
},
"source": [
"# Introduction to Pytorch Lightning ⚡\n",
"\n",
"In this notebook, we'll go over the basics of lightning by preparing models to train on the [MNIST Handwritten Digits dataset](https://en.wikipedia.org/wiki/MNIST_database).\n",
"\n",
"---\n",
" - Give us a ⭐ [on Github](https://www.github.com/PytorchLightning/pytorch-lightning/)\n",
" - Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/latest/)\n",
" - Join us [on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "2LODD6w9ixlT"
},
"source": [
"### Setup \n",
"Lightning is easy to install. Simply ```pip install pytorch-lightning```"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "zK7-Gg69kMnG"
},
"outputs": [],
"source": [
"! pip install pytorch-lightning --quiet"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "w4_TYnt_keJi"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"import torch\n",
"from torch import nn\n",
"from torch.nn import functional as F\n",
"from torch.utils.data import DataLoader, random_split\n",
"from torchvision.datasets import MNIST\n",
"from torchvision import transforms\n",
"import pytorch_lightning as pl\n",
"from pytorch_lightning.metrics.functional import accuracy"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "EHpyMPKFkVbZ"
},
"source": [
"## Simplest example\n",
"\n",
"Here's the simplest most minimal example with just a training loop (no validation, no testing).\n",
"\n",
"**Keep in Mind** - A `LightningModule` *is* a PyTorch `nn.Module` - it just has a few more helpful features."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "V7ELesz1kVQo"
},
"outputs": [],
"source": [
"class MNISTModel(pl.LightningModule):\n",
"\n",
" def __init__(self):\n",
" super(MNISTModel, self).__init__()\n",
" self.l1 = torch.nn.Linear(28 * 28, 10)\n",
"\n",
" def forward(self, x):\n",
" return torch.relu(self.l1(x.view(x.size(0), -1)))\n",
"\n",
" def training_step(self, batch, batch_nb):\n",
" x, y = batch\n",
" loss = F.cross_entropy(self(x), y)\n",
" return loss\n",
"\n",
" def configure_optimizers(self):\n",
" return torch.optim.Adam(self.parameters(), lr=0.02)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "hIrtHg-Dv8TJ"
},
"source": [
"By using the `Trainer` you automatically get:\n",
"1. Tensorboard logging\n",
"2. Model checkpointing\n",
"3. Training and validation loop\n",
"4. early-stopping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4Dk6Ykv8lI7X"
},
"outputs": [],
"source": [
"# Init our model\n",
"mnist_model = MNISTModel()\n",
"\n",
"# Init DataLoader from MNIST Dataset\n",
"train_ds = MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor())\n",
"train_loader = DataLoader(train_ds, batch_size=32)\n",
"\n",
"# Initialize a trainer\n",
"trainer = pl.Trainer(gpus=1, max_epochs=3, progress_bar_refresh_rate=20)\n",
"\n",
"# Train the model ⚡\n",
"trainer.fit(mnist_model, train_loader)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "KNpOoBeIjscS"
},
"source": [
"## A more complete MNIST Lightning Module Example\n",
"\n",
"That wasn't so hard was it?\n",
"\n",
"Now that we've got our feet wet, let's dive in a bit deeper and write a more complete `LightningModule` for MNIST...\n",
"\n",
"This time, we'll bake in all the dataset specific pieces directly in the `LightningModule`. This way, we can avoid writing extra code at the beginning of our script every time we want to run it.\n",
"\n",
"---\n",
"\n",
"### Note what the following built-in functions are doing:\n",
"\n",
"1. [prepare_data()](https://pytorch-lightning.readthedocs.io/en/latest/api/pytorch_lightning.core.lightning.html#pytorch_lightning.core.lightning.LightningModule.prepare_data) 💾\n",
" - This is where we can download the dataset. We point to our desired dataset and ask torchvision's `MNIST` dataset class to download if the dataset isn't found there.\n",
" - **Note we do not make any state assignments in this function** (i.e. `self.something = ...`)\n",
"\n",
"2. [setup(stage)](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning-module.html#setup) ⚙️\n",
" - Loads in data from file and prepares PyTorch tensor datasets for each split (train, val, test). \n",
" - Setup expects a 'stage' arg which is used to separate logic for 'fit' and 'test'.\n",
" - If you don't mind loading all your datasets at once, you can set up a condition to allow for both 'fit' related setup and 'test' related setup to run whenever `None` is passed to `stage` (or ignore it altogether and exclude any conditionals).\n",
" - **Note this runs across all GPUs and it *is* safe to make state assignments here**\n",
"\n",
"3. [x_dataloader()](https://pytorch-lightning.readthedocs.io/en/latest/common/lightning-module.html#data-hooks) ♻️\n",
" - `train_dataloader()`, `val_dataloader()`, and `test_dataloader()` all return PyTorch `DataLoader` instances that are created by wrapping their respective datasets that we prepared in `setup()`"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "4DNItffri95Q"
},
"outputs": [],
"source": [
"class LitMNIST(pl.LightningModule):\n",
" \n",
" def __init__(self, data_dir='./', hidden_size=64, learning_rate=2e-4):\n",
"\n",
" super().__init__()\n",
"\n",
" # Set our init args as class attributes\n",
" self.data_dir = data_dir\n",
" self.hidden_size = hidden_size\n",
" self.learning_rate = learning_rate\n",
"\n",
" # Hardcode some dataset specific attributes\n",
" self.num_classes = 10\n",
" self.dims = (1, 28, 28)\n",
" channels, width, height = self.dims\n",
" self.transform = transforms.Compose([\n",
" transforms.ToTensor(),\n",
" transforms.Normalize((0.1307,), (0.3081,))\n",
" ])\n",
"\n",
" # Define PyTorch model\n",
" self.model = nn.Sequential(\n",
" nn.Flatten(),\n",
" nn.Linear(channels * width * height, hidden_size),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.1),\n",
" nn.Linear(hidden_size, hidden_size),\n",
" nn.ReLU(),\n",
" nn.Dropout(0.1),\n",
" nn.Linear(hidden_size, self.num_classes)\n",
" )\n",
"\n",
" def forward(self, x):\n",
" x = self.model(x)\n",
" return F.log_softmax(x, dim=1)\n",
"\n",
" def training_step(self, batch, batch_idx):\n",
" x, y = batch\n",
" logits = self(x)\n",
" loss = F.nll_loss(logits, y)\n",
" return loss\n",
"\n",
" def validation_step(self, batch, batch_idx):\n",
" x, y = batch\n",
" logits = self(x)\n",
" loss = F.nll_loss(logits, y)\n",
" preds = torch.argmax(logits, dim=1)\n",
" acc = accuracy(preds, y)\n",
"\n",
" # Calling self.log will surface up scalars for you in TensorBoard\n",
" self.log('val_loss', loss, prog_bar=True)\n",
" self.log('val_acc', acc, prog_bar=True)\n",
" return loss\n",
"\n",
" def test_step(self, batch, batch_idx):\n",
" # Here we just reuse the validation_step for testing\n",
" return self.validation_step(batch, batch_idx)\n",
"\n",
" def configure_optimizers(self):\n",
" optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n",
" return optimizer\n",
"\n",
" ####################\n",
" # DATA RELATED HOOKS\n",
" ####################\n",
"\n",
" def prepare_data(self):\n",
" # download\n",
" MNIST(self.data_dir, train=True, download=True)\n",
" MNIST(self.data_dir, train=False, download=True)\n",
"\n",
" def setup(self, stage=None):\n",
"\n",
" # Assign train/val datasets for use in dataloaders\n",
" if stage == 'fit' or stage is None:\n",
" mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)\n",
" self.mnist_train, self.mnist_val = random_split(mnist_full, [55000, 5000])\n",
"\n",
" # Assign test dataset for use in dataloader(s)\n",
" if stage == 'test' or stage is None:\n",
" self.mnist_test = MNIST(self.data_dir, train=False, transform=self.transform)\n",
"\n",
" def train_dataloader(self):\n",
" return DataLoader(self.mnist_train, batch_size=32)\n",
"\n",
" def val_dataloader(self):\n",
" return DataLoader(self.mnist_val, batch_size=32)\n",
"\n",
" def test_dataloader(self):\n",
" return DataLoader(self.mnist_test, batch_size=32)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "Mb0U5Rk2kLBy"
},
"outputs": [],
"source": [
"model = LitMNIST()\n",
"trainer = pl.Trainer(gpus=1, max_epochs=3, progress_bar_refresh_rate=20)\n",
"trainer.fit(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "nht8AvMptY6I"
},
"source": [
"### Testing\n",
"\n",
"To test a model, call `trainer.test(model)`.\n",
"\n",
"Or, if you've just trained a model, you can just call `trainer.test()` and Lightning will automatically test using the best saved checkpoint (conditioned on val_loss)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "PA151FkLtprO"
},
"outputs": [],
"source": [
"trainer.test()"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "T3-3lbbNtr5T"
},
"source": [
"### Bonus Tip\n",
"\n",
"You can keep calling `trainer.fit(model)` as many times as you'd like to continue training"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "IFBwCbLet2r6"
},
"outputs": [],
"source": [
"trainer.fit(model)"
]
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8TRyS5CCt3n9"
},
"source": [
"In Colab, you can use the TensorBoard magic function to view the logs that Lightning has created for you!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {},
"colab_type": "code",
"id": "wizS-QiLuAYo"
},
"outputs": [],
"source": [
"# Start tensorboard.\n",
"%load_ext tensorboard\n",
"%tensorboard --logdir lightning_logs/"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<code style=\"color:#792ee5;\">\n",
" <h1> <strong> Congratulations - Time to Join the Community! </strong> </h1>\n",
"</code>\n",
"\n",
"Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning movement, you can do so in the following ways!\n",
"\n",
"### Star [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) on GitHub\n",
"The easiest way to help our community is just by starring the GitHub repos! This helps raise awareness of the cool tools we're building.\n",
"\n",
"* Please, star [Lightning](https://github.com/PyTorchLightning/pytorch-lightning)\n",
"\n",
"### Join our [Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A)!\n",
"The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself and share your interests in `#general` channel\n",
"\n",
"### Interested by SOTA AI models ! Check out [Bolt](https://github.com/PyTorchLightning/pytorch-lightning-bolts)\n",
"Bolts has a collection of state-of-the-art models, all implemented in [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) and can be easily integrated within your own projects.\n",
"\n",
"* Please, star [Bolt](https://github.com/PyTorchLightning/pytorch-lightning-bolts)\n",
"\n",
"### Contributions !\n",
"The best way to contribute to our community is to become a code contributor! At any time you can go to [Lightning](https://github.com/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/pytorch-lightning-bolts) GitHub Issues page and filter for \"good first issue\". \n",
"\n",
"* [Lightning good first issue](https://github.com/PyTorchLightning/pytorch-lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n",
"* [Bolt good first issue](https://github.com/PyTorchLightning/pytorch-lightning-bolts/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n",
"* You can also contribute your own notebooks with useful examples !\n",
"\n",
"### Great thanks from the entire Pytorch Lightning Team for your interest !\n",
"\n",
"<img src=\"https://github.com/PyTorchLightning/pytorch-lightning/blob/master/docs/source/_static/images/logo.png?raw=true\" width=\"800\" height=\"200\" />"
]
}
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