{"cells": [{"cell_type": "markdown", "id": "b36d598d", "metadata": {"papermill": {"duration": 0.00325, "end_time": "2025-04-03T20:53:24.581994", "exception": false, "start_time": "2025-04-03T20:53:24.578744", "status": "completed"}, "tags": []}, "source": ["\n", "# Introduction to PyTorch Lightning\n", "\n", "* **Author:** Lightning.ai\n", "* **License:** CC BY-SA\n", "* **Generated:** 2025-04-03T20:53:18.167877\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", "Open in [{height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/lightning_examples/mnist-hello-world.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://lightning.ai/docs/)\n", "| Join us [on Discord](https://discord.com/invite/tfXFetEZxv)"]}, {"cell_type": "markdown", "id": "27a8ad74", "metadata": {"papermill": {"duration": 0.00234, "end_time": "2025-04-03T20:53:24.586994", "exception": false, "start_time": "2025-04-03T20:53:24.584654", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "0def7e9d", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2025-04-03T20:53:24.592596Z", "iopub.status.busy": "2025-04-03T20:53:24.592415Z", "iopub.status.idle": "2025-04-03T20:53:25.767242Z", "shell.execute_reply": "2025-04-03T20:53:25.765748Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 1.179969, "end_time": "2025-04-03T20:53:25.769318", "exception": false, "start_time": "2025-04-03T20:53:24.589349", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable.It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning.\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}, {"name": "stdout", "output_type": "stream", "text": ["\r\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m25.0.1\u001b[0m\r\n", "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpython -m pip install --upgrade pip\u001b[0m\r\n"]}], "source": ["! pip install --quiet \"torch>=1.8.1, <2.7\" \"matplotlib\" \"torchmetrics>=1.0, <1.8\" \"pandas\" \"torchmetrics >=0.11.0\" \"numpy <3.0\" \"pytorch-lightning >=2.0,<2.6\" \"torchvision\" \"seaborn\""]}, {"cell_type": "code", "execution_count": 2, "id": "9a148e57", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T20:53:25.776575Z", "iopub.status.busy": "2025-04-03T20:53:25.776033Z", "iopub.status.idle": "2025-04-03T20:53:29.333206Z", "shell.execute_reply": "2025-04-03T20:53:29.331870Z"}, "papermill": {"duration": 3.563765, "end_time": "2025-04-03T20:53:29.335766", "exception": false, "start_time": "2025-04-03T20:53:25.772001", "status": "completed"}, "tags": []}, "outputs": [], "source": ["\n", "# ------------------- Preliminaries ------------------- #\n", "import os\n", "from dataclasses import dataclass\n", "from typing import Tuple\n", "\n", "import pandas as pd\n", "import pytorch_lightning as pl\n", "import seaborn as sn\n", "import torch\n", "from IPython.display import display\n", "from pytorch_lightning.loggers import CSVLogger\n", "from torch import nn\n", "from torch.nn import functional as F\n", "from torch.utils.data import DataLoader, random_split\n", "from torchmetrics import Accuracy\n", "from torchvision import transforms\n", "from torchvision.datasets import MNIST\n", "\n", "# ------------------- Configuration ------------------- #\n", "\n", "\n", "@dataclass\n", "class Config:\n", " \"\"\"Configuration options for the Lightning MNIST example.\n", "\n", " Args:\n", " data_dir : The path to the directory where the MNIST dataset is stored. Defaults to the value of\n", " the 'PATH_DATASETS' environment variable or '.' if not set.\n", "\n", " save_dir : The path to the directory where the training logs will be saved. Defaults to 'logs/'.\n", "\n", " batch_size : The batch size to use during training. Defaults to 256 if a GPU is available,\n", " or 64 otherwise.\n", "\n", " max_epochs : The maximum number of epochs to train the model for. Defaults to 3.\n", "\n", " accelerator : The accelerator to use for training. Can be one of \"cpu\", \"gpu\", \"tpu\", \"ipu\", \"auto\".\n", "\n", " devices : The number of devices to use for training. Defaults to 1.\n", "\n", " Examples:\n", " This dataclass can be used to specify the configuration options for training a PyTorch Lightning model on the\n", " MNIST dataset. A new instance of this dataclass can be created as follows:\n", "\n", " >>> config = Config()\n", "\n", " The default values for each argument are shown in the documentation above. If desired, any of these values can be\n", " overridden when creating a new instance of the dataclass:\n", "\n", " >>> config = Config(batch_size=128, max_epochs=5)\n", "\n", " \"\"\"\n", "\n", " data_dir: str = os.environ.get(\"PATH_DATASETS\", \".\")\n", " save_dir: str = \"logs/\"\n", " batch_size: int = 256 if torch.cuda.is_available() else 64\n", " max_epochs: int = 3\n", " accelerator: str = \"auto\"\n", " devices: int = 1\n", "\n", "\n", "config = Config()"]}, {"cell_type": "markdown", "id": "b7fa54a6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.005546, "end_time": "2025-04-03T20:53:29.347310", "exception": false, "start_time": "2025-04-03T20:53:29.341764", "status": "completed"}, "tags": []}, "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, "id": "8e611193", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T20:53:29.360014Z", "iopub.status.busy": "2025-04-03T20:53:29.359609Z", "iopub.status.idle": "2025-04-03T20:53:29.371985Z", "shell.execute_reply": "2025-04-03T20:53:29.370820Z"}, "papermill": {"duration": 0.021379, "end_time": "2025-04-03T20:53:29.374211", "exception": false, "start_time": "2025-04-03T20:53:29.352832", "status": "completed"}, "tags": []}, "outputs": [], "source": ["\n", "\n", "class MNISTModel(pl.LightningModule):\n", " \"\"\"A PyTorch Lightning module for classifying images in the MNIST dataset.\n", "\n", " Attributes:\n", " l1 : A linear layer that maps input features to output features.\n", "\n", " Methods:\n", " forward(x):\n", " Performs a forward pass through the model.\n", "\n", " training_step(batch, batch_nb):\n", " Defines a single training step for the model.\n", "\n", " configure_optimizers():\n", " Configures the optimizer to use during training.\n", "\n", " Examples:\n", " The MNISTModel class can be used to create and train a PyTorch Lightning model for classifying images in the MNIST\n", " dataset. To create a new instance of the model, simply instantiate the class:\n", "\n", " >>> model = MNISTModel()\n", "\n", " The model can then be trained using a PyTorch Lightning trainer object:\n", "\n", " >>> trainer = pl.Trainer()\n", " >>> trainer.fit(model)\n", "\n", " \"\"\"\n", "\n", " def __init__(self):\n", " \"\"\"Initializes a new instance of the MNISTModel class.\"\"\"\n", " super().__init__()\n", " self.l1 = torch.nn.Linear(28 * 28, 10)\n", "\n", " def forward(self, x: torch.Tensor) -> torch.Tensor:\n", " \"\"\"Performs a forward pass through the model.\n", "\n", " Args:\n", " x : The input tensor to pass through the model.\n", "\n", " Returns:\n", " activated : The output tensor produced by the model.\n", "\n", " Examples:\n", " >>> model = MNISTModel()\n", " >>> x = torch.randn(1, 1, 28, 28)\n", " >>> output = model(x)\n", "\n", " \"\"\"\n", " flattened = x.view(x.size(0), -1)\n", " hidden = self.l1(flattened)\n", " activated = torch.relu(hidden)\n", "\n", " return activated\n", "\n", " def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_nb: int) -> torch.Tensor:\n", " \"\"\"Defines a single training step for the model.\n", "\n", " Args:\n", " batch: A tuple containing the input and target tensors for the batch.\n", " batch_nb: The batch number.\n", "\n", " Returns:\n", " torch.Tensor: The loss value for the current batch.\n", "\n", " Examples:\n", " >>> model = MNISTModel()\n", " >>> x = torch.randn(1, 1, 28, 28)\n", " >>> y = torch.tensor([1])\n", " >>> loss = model.training_step((x, y), 0)\n", "\n", " \"\"\"\n", " x, y = batch\n", " loss = F.cross_entropy(self(x), y)\n", " return loss\n", "\n", " def configure_optimizers(self) -> torch.optim.Optimizer:\n", " \"\"\"Configures the optimizer to use during training.\n", "\n", " Returns:\n", " torch.optim.Optimizer: The optimizer to use during training.\n", "\n", " Examples:\n", " >>> model = MNISTModel()\n", " >>> optimizer = model.configure_optimizers()\n", "\n", " \"\"\"\n", " return torch.optim.Adam(self.parameters(), lr=0.02)"]}, {"cell_type": "markdown", "id": "70f96910", "metadata": {"papermill": {"duration": 0.005677, "end_time": "2025-04-03T20:53:29.385581", "exception": false, "start_time": "2025-04-03T20:53:29.379904", "status": "completed"}, "tags": []}, "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": 4, "id": "36648472", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T20:53:29.399010Z", "iopub.status.busy": "2025-04-03T20:53:29.397956Z", "iopub.status.idle": "2025-04-03T20:53:48.559987Z", "shell.execute_reply": "2025-04-03T20:53:48.558794Z"}, "papermill": {"duration": 19.171314, "end_time": "2025-04-03T20:53:48.562520", "exception": false, "start_time": "2025-04-03T20:53:29.391206", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Failed to download (trying next):\n", "HTTP Error 404: Not Found\n", "\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n", "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to /__w/11/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", " 0%| | 0/9912422 [00:00 torch.Tensor:\n", " \"\"\"Performs a forward pass through the MLP.\n", "\n", " Args:\n", " x : The input data.\n", "\n", " Returns:\n", " torch.Tensor: The output of the MLP.\n", "\n", " \"\"\"\n", " x = self.model(x)\n", " return F.log_softmax(x, dim=1)\n", "\n", " def training_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_nb: int) -> torch.Tensor:\n", " \"\"\"Defines a single training step for the MLP.\n", "\n", " Args:\n", " batch: A tuple containing the input data and target labels.\n", "\n", " batch_idx: The index of the current batch.\n", "\n", " Returns:\n", " (torch.Tensor): The training loss.\n", "\n", " \"\"\"\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: Tuple[torch.Tensor, torch.Tensor], batch_nb: int) -> None:\n", " \"\"\"Defines a single validation step for the MLP.\n", "\n", " Args:\n", " batch : A tuple containing the input data and target labels.\n", " batch_idx : The index of the current batch.\n", "\n", " \"\"\"\n", " x, y = batch\n", " logits = self(x)\n", " loss = F.nll_loss(logits, y)\n", " preds = torch.argmax(logits, dim=1)\n", " self.val_accuracy.update(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\", self.val_accuracy, prog_bar=True)\n", "\n", " def test_step(self, batch: Tuple[torch.Tensor, torch.Tensor], batch_nb: int) -> None:\n", " \"\"\"Defines a single testing step for the MLP.\n", "\n", " Args:\n", " batch : A tuple containing the input data and target labels.\n", " batch_idx : The index of the current batch.\n", "\n", " \"\"\"\n", " x, y = batch\n", " logits = self(x)\n", " loss = F.nll_loss(logits, y)\n", " preds = torch.argmax(logits, dim=1)\n", " self.test_accuracy.update(preds, y)\n", "\n", " # Calling self.log will surface up scalars for you in TensorBoard\n", " self.log(\"test_loss\", loss, prog_bar=True)\n", " self.log(\"test_acc\", self.test_accuracy, prog_bar=True)\n", "\n", " def configure_optimizers(self) -> torch.optim.Optimizer:\n", " \"\"\"Configures the optimizer to use for training the MLP.\n", "\n", " Returns:\n", " torch.optim.Optimizer: The optimizer.\n", "\n", " \"\"\"\n", " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n", "\n", " return optimizer\n", "\n", " # ------------------------------------- #\n", " # DATA RELATED HOOKS\n", " # ------------------------------------- #\n", "\n", " def prepare_data(self) -> None:\n", " \"\"\"Downloads the MNIST dataset.\"\"\"\n", " MNIST(self.data_dir, train=True, download=True)\n", "\n", " MNIST(self.data_dir, train=False, download=True)\n", "\n", " def setup(self, stage: str = None) -> None:\n", " \"\"\"Splits the MNIST dataset into train, validation, and test sets.\n", "\n", " Args:\n", " stage : The current stage (either \"fit\" or \"test\"). Defaults to None.\n", "\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", "\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) -> DataLoader:\n", " \"\"\"Returns a DataLoader for the training set.\n", "\n", " Returns:\n", " DataLoader: The training DataLoader.\n", "\n", " \"\"\"\n", " return DataLoader(self.mnist_train, batch_size=config.batch_size)\n", "\n", " def val_dataloader(self) -> DataLoader:\n", " \"\"\"Returns a DataLoader for the validation set.\n", "\n", " Returns:\n", " DataLoader: The validation DataLoader.\n", "\n", " \"\"\"\n", " return DataLoader(self.mnist_val, batch_size=config.batch_size)\n", "\n", " def test_dataloader(self) -> DataLoader:\n", " \"\"\"Returns a DataLoader for the test set.\n", "\n", " Returns:\n", " DataLoader: The test DataLoader.\n", "\n", " \"\"\"\n", " return DataLoader(self.mnist_test, batch_size=config.batch_size)"]}, {"cell_type": "code", "execution_count": 6, "id": "dd757f00", "metadata": {"execution": {"iopub.execute_input": "2025-04-03T20:53:48.636025Z", "iopub.status.busy": "2025-04-03T20:53:48.635786Z", "iopub.status.idle": "2025-04-03T20:54:17.240844Z", "shell.execute_reply": "2025-04-03T20:54:17.239903Z"}, "papermill": {"duration": 28.613771, "end_time": "2025-04-03T20:54:17.243279", "exception": false, "start_time": "2025-04-03T20:53:48.629508", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True (cuda), used: True\n"]}, {"name": "stderr", "output_type": "stream", "text": ["TPU available: False, using: 0 TPU cores\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", " | Name | Type | Params | Mode \n", "-------------------------------------------------------------\n", "0 | model | Sequential | 55.1 K | train\n", "1 | val_accuracy | MulticlassAccuracy | 0 | train\n", "2 | test_accuracy | MulticlassAccuracy | 0 | train\n", "-------------------------------------------------------------\n", "55.1 K Trainable params\n", "0 Non-trainable params\n", "55.1 K Total params\n", "0.220 Total estimated model params size (MB)\n", "11 Modules in train mode\n", "0 Modules in eval mode\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b06e7dc4754045cab50324d13c347f42", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: | | 0/? [00:00\n", "\n", "\n", " \n", " \n", " test_acc \n", " test_loss \n", " val_acc \n", " val_loss \n", " \n", " \n", " epoch \n", " \n", " \n", " \n", " \n", " 0 \n", " NaN \n", " NaN \n", " 0.8790 \n", " 0.442527 \n", " \n", " \n", " 1 \n", " NaN \n", " NaN \n", " 0.9066 \n", " 0.326151 \n", " \n", " \n", " 2 \n", " NaN \n", " NaN \n", " 0.9190 \n", " 0.283037 \n", " \n", " \n", " 3 \n", " 0.9257 \n", " 0.255571 \n", " NaN \n", " NaN \n", " \n", " \n", "
\n", ""], "text/plain": [" test_acc test_loss val_acc val_loss\n", "epoch \n", "0 NaN NaN 0.8790 0.442527\n", "1 NaN NaN 0.9066 0.326151\n", "2 NaN NaN 0.9190 0.283037\n", "3 0.9257 0.255571 NaN NaN"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": [""]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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", "text/plain": [""]}, "metadata": {}, "output_type": "display_data"}], "source": ["\n", "# Read in the training metrics from the CSV file generated by the logger\n", "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n", "\n", "# Remove the \"step\" column, which is not needed for our analysis\n", "del metrics[\"step\"]\n", "\n", "# Set the epoch column as the index, for easier plotting\n", "metrics.set_index(\"epoch\", inplace=True)\n", "\n", "# Display the first few rows of the metrics table, excluding any columns with all NaN values\n", "display(metrics.dropna(axis=1, how=\"all\").head())\n", "\n", "# Create a line plot of the training metrics using Seaborn\n", "sn.relplot(data=metrics, kind=\"line\")"]}, {"cell_type": "markdown", "id": "bfb41ed6", "metadata": {"papermill": {"duration": 0.013403, "end_time": "2025-04-03T20:54:19.495910", "exception": false, "start_time": "2025-04-03T20:54:19.482507", "status": "completed"}, "tags": []}, "source": ["## Congratulations - Time to Join the Community!\n", "\n", "Congratulations on completing this notebook tutorial! If you enjoyed this and would like to join the Lightning\n", "movement, you can do so in the following ways!\n", "\n", "### Star [Lightning](https://github.com/Lightning-AI/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\n", "tools we're building.\n", "\n", "### Join our [Discord](https://discord.com/invite/tfXFetEZxv)!\n", "The best way to keep up to date on the latest advancements is to join our community! Make sure to introduce yourself\n", "and share your interests in `#general` channel\n", "\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\n", "[Lightning](https://github.com/Lightning-AI/lightning) or [Bolt](https://github.com/Lightning-AI/lightning-bolts)\n", "GitHub Issues page and filter for \"good first issue\".\n", "\n", "* [Lightning good first issue](https://github.com/Lightning-AI/lightning/issues?q=is%3Aopen+is%3Aissue+label%3A%22good+first+issue%22)\n", "* [Bolt good first issue](https://github.com/Lightning-AI/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", "[{height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", " :header: Introduction to PyTorch Lightning\n", " :card_description: 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", " :tags: Image,GPU/TPU,Lightning-Examples"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab,id,colab_type,-all", "formats": "ipynb,py:percent", "main_language": "python"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12"}, "papermill": {"default_parameters": {}, "duration": 58.220327, "end_time": "2025-04-03T20:54:21.967627", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/mnist-hello-world/hello-world.ipynb", "output_path": ".notebooks/lightning_examples/mnist-hello-world.ipynb", "parameters": {}, "start_time": "2025-04-03T20:53:23.747300", "version": "2.6.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"08fa5b890f52427cb815649322500771": 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