{"cells": [{"cell_type": "markdown", "id": "9214cb4b", "metadata": {"papermill": {"duration": 0.005, "end_time": "2023-03-15T10:40:36.106989", "exception": false, "start_time": "2023-03-15T10:40:36.101989", "status": "completed"}, "tags": []}, "source": ["\n", "# PyTorch Lightning DataModules\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2023-03-15T10:38:58.977380\n", "\n", "This notebook will walk you through how to start using Datamodules. With the release of `pytorch-lightning` version 0.9.0, we have included a new class called `LightningDataModule` to help you decouple data related hooks from your `LightningModule`. The most up-to-date documentation on datamodules can be found [here](https://lightning.ai/docs/pytorch/stable/data/datamodule.html).\n", "\n", "---\n", "Open in [{height=\"20px\" width=\"117px\"}](https://colab.research.google.com/github/PytorchLightning/lightning-tutorials/blob/publication/.notebooks/lightning_examples/datamodules.ipynb)\n", "\n", "Give us a \u2b50 [on Github](https://www.github.com/Lightning-AI/lightning/)\n", "| Check out [the documentation](https://pytorch-lightning.readthedocs.io/en/stable/)\n", "| Join us [on Slack](https://www.pytorchlightning.ai/community)"]}, {"cell_type": "markdown", "id": "52d6b0f8", "metadata": {"papermill": {"duration": 0.003497, "end_time": "2023-03-15T10:40:36.114445", "exception": false, "start_time": "2023-03-15T10:40:36.110948", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "4cda7c76", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2023-03-15T10:40:36.122514Z", "iopub.status.busy": "2023-03-15T10:40:36.122049Z", "iopub.status.idle": "2023-03-15T10:40:39.373356Z", "shell.execute_reply": "2023-03-15T10:40:39.372021Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.257442, "end_time": "2023-03-15T10:40:39.375326", "exception": false, "start_time": "2023-03-15T10:40:36.117884", "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. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n", "\u001b[0m"]}], "source": ["! pip install --quiet \"torchmetrics>=0.7, <0.12\" \"setuptools==67.4.0\" \"ipython[notebook]>=8.0.0, <8.12.0\" \"torch>=1.8.1, <1.14.0\" \"torchvision\" \"pytorch-lightning>=1.4, <2.0.0\""]}, {"cell_type": "markdown", "id": "6de813cb", "metadata": {"papermill": {"duration": 0.003626, "end_time": "2023-03-15T10:40:39.383174", "exception": false, "start_time": "2023-03-15T10:40:39.379548", "status": "completed"}, "tags": []}, "source": ["## Introduction\n", "\n", "First, we'll go over a regular `LightningModule` implementation without the use of a `LightningDataModule`"]}, {"cell_type": "code", "execution_count": 2, "id": "2179b1f1", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:40:39.392552Z", "iopub.status.busy": "2023-03-15T10:40:39.391720Z", "iopub.status.idle": "2023-03-15T10:40:41.953236Z", "shell.execute_reply": "2023-03-15T10:40:41.951759Z"}, "papermill": {"duration": 2.568674, "end_time": "2023-03-15T10:40:41.955375", "exception": false, "start_time": "2023-03-15T10:40:39.386701", "status": "completed"}, "tags": []}, "outputs": [], "source": ["import os\n", "\n", "import lightning as L\n", "import torch\n", "import torch.nn.functional as F\n", "from torch import nn\n", "from torch.utils.data import DataLoader, random_split\n", "from torchmetrics.functional import accuracy\n", "from torchvision import transforms\n", "\n", "# Note - you must have torchvision installed for this example\n", "from torchvision.datasets import CIFAR10, MNIST\n", "\n", "PATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n", "BATCH_SIZE = 256 if torch.cuda.is_available() else 64"]}, {"cell_type": "markdown", "id": "cac3d299", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.003759, "end_time": "2023-03-15T10:40:41.963128", "exception": false, "start_time": "2023-03-15T10:40:41.959369", "status": "completed"}, "tags": []}, "source": ["### Defining the LitMNISTModel\n", "\n", "Below, we reuse a `LightningModule` from our hello world tutorial that classifies MNIST Handwritten Digits.\n", "\n", "Unfortunately, we have hardcoded dataset-specific items within the model,\n", "forever limiting it to working with MNIST Data. \ud83d\ude22\n", "\n", "This is fine if you don't plan on training/evaluating your model on different datasets.\n", "However, in many cases, this can become bothersome when you want to try out your architecture with different datasets."]}, {"cell_type": "code", "execution_count": 3, "id": "709cf586", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:40:41.972713Z", "iopub.status.busy": "2023-03-15T10:40:41.971922Z", "iopub.status.idle": "2023-03-15T10:40:41.992949Z", "shell.execute_reply": "2023-03-15T10:40:41.992110Z"}, "papermill": {"duration": 0.027845, "end_time": "2023-03-15T10:40:41.994581", "exception": false, "start_time": "2023-03-15T10:40:41.966736", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitMNIST(L.LightningModule):\n", " def __init__(self, data_dir=PATH_DATASETS, hidden_size=64, learning_rate=2e-4):\n", " super().__init__()\n", "\n", " # We hardcode dataset specific stuff here.\n", " self.data_dir = data_dir\n", " self.num_classes = 10\n", " self.dims = (1, 28, 28)\n", " channels, width, height = self.dims\n", " self.transform = transforms.Compose(\n", " [\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,)),\n", " ]\n", " )\n", "\n", " self.hidden_size = hidden_size\n", " self.learning_rate = learning_rate\n", "\n", " # Build 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):\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, task=\"multiclass\", num_classes=10)\n", " self.log(\"val_loss\", loss, prog_bar=True)\n", " self.log(\"val_acc\", acc, prog_bar=True)\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", " # 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=128)\n", "\n", " def val_dataloader(self):\n", " return DataLoader(self.mnist_val, batch_size=128)\n", "\n", " def test_dataloader(self):\n", " return DataLoader(self.mnist_test, batch_size=128)"]}, {"cell_type": "markdown", "id": "4a655491", "metadata": {"papermill": {"duration": 0.003778, "end_time": "2023-03-15T10:40:42.002076", "exception": false, "start_time": "2023-03-15T10:40:41.998298", "status": "completed"}, "tags": []}, "source": ["### Training the ListMNIST Model"]}, {"cell_type": "code", "execution_count": 4, "id": "29812281", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:40:42.010531Z", "iopub.status.busy": "2023-03-15T10:40:42.010333Z", "iopub.status.idle": "2023-03-15T10:41:10.455990Z", "shell.execute_reply": "2023-03-15T10:41:10.454725Z"}, "papermill": {"duration": 28.452752, "end_time": "2023-03-15T10:41:10.458522", "exception": false, "start_time": "2023-03-15T10:40:42.005770", "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": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to /__w/6/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "bc45212939024bb4ba157142ddb0e9dc", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/9912422 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/train-images-idx3-ubyte.gz to /__w/6/s/.datasets/MNIST/raw\n"]}, {"name": "stdout", "output_type": "stream", "text": ["\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to /__w/6/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "fb2beed535524a66a377a602d49d9e1e", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/28881 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/train-labels-idx1-ubyte.gz to /__w/6/s/.datasets/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to /__w/6/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "568a0f5f3f3145428f6988907172e343", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/1648877 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/t10k-images-idx3-ubyte.gz to /__w/6/s/.datasets/MNIST/raw\n", "\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to /__w/6/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "07d23bf0647a469295abc1a9a8bf6152", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/4542 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting /__w/6/s/.datasets/MNIST/raw/t10k-labels-idx1-ubyte.gz to /__w/6/s/.datasets/MNIST/raw\n", "\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Missing logger folder: /__w/6/s/lightning_logs\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\n", "-------------------------------------\n", "0 | model | Sequential | 55.1 K\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"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "83713868abf04b14b7de5ed027fe13a8", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.9/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.9/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:224: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 64 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", " rank_zero_warn(\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "675bd95c1f49482c93aa92fb8bc9139e", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "74c0d5f0ea4d4bd7abf8310e5e6d9625", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "58b9b1d679e24ef7b1f616d1c47b4cc6", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=2` reached.\n"]}], "source": ["model = LitMNIST()\n", "trainer = L.Trainer(\n", " max_epochs=2,\n", " accelerator=\"auto\",\n", " devices=1,\n", ")\n", "trainer.fit(model)"]}, {"cell_type": "markdown", "id": "0c43b972", "metadata": {"papermill": {"duration": 0.077951, "end_time": "2023-03-15T10:41:10.549691", "exception": false, "start_time": "2023-03-15T10:41:10.471740", "status": "completed"}, "tags": []}, "source": ["## Using DataModules\n", "\n", "DataModules are a way of decoupling data-related hooks from the `LightningModule\n", "` so you can develop dataset agnostic models."]}, {"cell_type": "markdown", "id": "ccc911a7", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.005639, "end_time": "2023-03-15T10:41:10.561072", "exception": false, "start_time": "2023-03-15T10:41:10.555433", "status": "completed"}, "tags": []}, "source": ["### Defining The MNISTDataModule\n", "\n", "Let's go over each function in the class below and talk about what they're doing:\n", "\n", "1. ```__init__```\n", " - Takes in a `data_dir` arg that points to where you have downloaded/wish to download the MNIST dataset.\n", " - Defines a transform that will be applied across train, val, and test dataset splits.\n", " - Defines default `self.dims`.\n", "\n", "\n", "2. ```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", "3. ```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`.\n", " - **Note this runs across all GPUs and it *is* safe to make state assignments here**\n", "\n", "\n", "4. ```x_dataloader```\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, "id": "4b1dcd4f", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:41:10.574345Z", "iopub.status.busy": "2023-03-15T10:41:10.573776Z", "iopub.status.idle": "2023-03-15T10:41:10.586634Z", "shell.execute_reply": "2023-03-15T10:41:10.585811Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.02143, "end_time": "2023-03-15T10:41:10.588010", "exception": false, "start_time": "2023-03-15T10:41:10.566580", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class MNISTDataModule(L.LightningDataModule):\n", " def __init__(self, data_dir: str = PATH_DATASETS):\n", " super().__init__()\n", " self.data_dir = data_dir\n", " self.transform = transforms.Compose(\n", " [\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.1307,), (0.3081,)),\n", " ]\n", " )\n", "\n", " self.dims = (1, 28, 28)\n", " self.num_classes = 10\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", " # 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=BATCH_SIZE)\n", "\n", " def val_dataloader(self):\n", " return DataLoader(self.mnist_val, batch_size=BATCH_SIZE)\n", "\n", " def test_dataloader(self):\n", " return DataLoader(self.mnist_test, batch_size=BATCH_SIZE)"]}, {"cell_type": "markdown", "id": "519722af", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.005528, "end_time": "2023-03-15T10:41:10.599286", "exception": false, "start_time": "2023-03-15T10:41:10.593758", "status": "completed"}, "tags": []}, "source": ["### Defining the dataset agnostic `LitModel`\n", "\n", "Below, we define the same model as the `LitMNIST` model we made earlier.\n", "\n", "However, this time our model has the freedom to use any input data that we'd like \ud83d\udd25."]}, {"cell_type": "code", "execution_count": 6, "id": "6af84212", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:41:10.616736Z", "iopub.status.busy": "2023-03-15T10:41:10.615861Z", "iopub.status.idle": "2023-03-15T10:41:10.626285Z", "shell.execute_reply": "2023-03-15T10:41:10.625413Z"}, "papermill": {"duration": 0.018812, "end_time": "2023-03-15T10:41:10.627750", "exception": false, "start_time": "2023-03-15T10:41:10.608938", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitModel(L.LightningModule):\n", " def __init__(self, channels, width, height, num_classes, hidden_size=64, learning_rate=2e-4):\n", " super().__init__()\n", "\n", " # We take in input dimensions as parameters and use those to dynamically build model.\n", " self.channels = channels\n", " self.width = width\n", " self.height = height\n", " self.num_classes = num_classes\n", " self.hidden_size = hidden_size\n", " self.learning_rate = learning_rate\n", "\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, 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):\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, task=\"multiclass\", num_classes=10)\n", " self.log(\"val_loss\", loss, prog_bar=True)\n", " self.log(\"val_acc\", acc, prog_bar=True)\n", "\n", " def configure_optimizers(self):\n", " optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n", " return optimizer"]}, {"cell_type": "markdown", "id": "8c1c1dbd", "metadata": {"papermill": {"duration": 0.005577, "end_time": "2023-03-15T10:41:10.638939", "exception": false, "start_time": "2023-03-15T10:41:10.633362", "status": "completed"}, "tags": []}, "source": ["### Training the `LitModel` using the `MNISTDataModule`\n", "\n", "Now, we initialize and train the `LitModel` using the `MNISTDataModule`'s configuration settings and dataloaders."]}, {"cell_type": "code", "execution_count": 7, "id": "d185a5ce", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:41:10.651607Z", "iopub.status.busy": "2023-03-15T10:41:10.650932Z", "iopub.status.idle": "2023-03-15T10:41:43.596377Z", "shell.execute_reply": "2023-03-15T10:41:43.595445Z"}, "papermill": {"duration": 32.953635, "end_time": "2023-03-15T10:41:43.598179", "exception": false, "start_time": "2023-03-15T10:41:10.644544", "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": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\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\n", "-------------------------------------\n", "0 | model | Sequential | 55.1 K\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"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d7c5e7c3c30642c78d0422f1f10c4239", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a7dde305c1bd48b19d4bec2bcff8e147", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "e3daf92b09b14fc8b65cd4a3abd4e48c", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b10053daef9941509d48d9e324aa1a0b", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "97baa5bfb24b48f0b52bd7f16b279b22", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=3` reached.\n"]}], "source": ["# Init DataModule\n", "dm = MNISTDataModule()\n", "# Init model from datamodule's attributes\n", "model = LitModel(*dm.dims, dm.num_classes)\n", "# Init trainer\n", "trainer = L.Trainer(\n", " max_epochs=3,\n", " accelerator=\"auto\",\n", " devices=1,\n", ")\n", "# Pass the datamodule as arg to trainer.fit to override model hooks :)\n", "trainer.fit(model, dm)"]}, {"cell_type": "markdown", "id": "5fec36ea", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.006768, "end_time": "2023-03-15T10:41:43.614066", "exception": false, "start_time": "2023-03-15T10:41:43.607298", "status": "completed"}, "tags": []}, "source": ["### Defining the CIFAR10 DataModule\n", "\n", "Lets prove the `LitModel` we made earlier is dataset agnostic by defining a new datamodule for the CIFAR10 dataset."]}, {"cell_type": "code", "execution_count": 8, "id": "e52b720c", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:41:43.629307Z", "iopub.status.busy": "2023-03-15T10:41:43.628748Z", "iopub.status.idle": "2023-03-15T10:41:43.638499Z", "shell.execute_reply": "2023-03-15T10:41:43.637671Z"}, "papermill": {"duration": 0.01905, "end_time": "2023-03-15T10:41:43.639838", "exception": false, "start_time": "2023-03-15T10:41:43.620788", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class CIFAR10DataModule(L.LightningDataModule):\n", " def __init__(self, data_dir: str = \"./\"):\n", " super().__init__()\n", " self.data_dir = data_dir\n", " self.transform = transforms.Compose(\n", " [\n", " transforms.ToTensor(),\n", " transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n", " ]\n", " )\n", "\n", " self.dims = (3, 32, 32)\n", " self.num_classes = 10\n", "\n", " def prepare_data(self):\n", " # download\n", " CIFAR10(self.data_dir, train=True, download=True)\n", " CIFAR10(self.data_dir, train=False, download=True)\n", "\n", " def setup(self, stage=None):\n", " # Assign train/val datasets for use in dataloaders\n", " if stage == \"fit\" or stage is None:\n", " cifar_full = CIFAR10(self.data_dir, train=True, transform=self.transform)\n", " self.cifar_train, self.cifar_val = random_split(cifar_full, [45000, 5000])\n", "\n", " # Assign test dataset for use in dataloader(s)\n", " if stage == \"test\" or stage is None:\n", " self.cifar_test = CIFAR10(self.data_dir, train=False, transform=self.transform)\n", "\n", " def train_dataloader(self):\n", " return DataLoader(self.cifar_train, batch_size=BATCH_SIZE)\n", "\n", " def val_dataloader(self):\n", " return DataLoader(self.cifar_val, batch_size=BATCH_SIZE)\n", "\n", " def test_dataloader(self):\n", " return DataLoader(self.cifar_test, batch_size=BATCH_SIZE)"]}, {"cell_type": "markdown", "id": "eebc619a", "metadata": {"papermill": {"duration": 0.00679, "end_time": "2023-03-15T10:41:43.653374", "exception": false, "start_time": "2023-03-15T10:41:43.646584", "status": "completed"}, "tags": []}, "source": ["### Training the `LitModel` using the `CIFAR10DataModule`\n", "\n", "Our model isn't very good, so it will perform pretty badly on the CIFAR10 dataset.\n", "\n", "The point here is that we can see that our `LitModel` has no problem using a different datamodule as its input data."]}, {"cell_type": "code", "execution_count": 9, "id": "b81646d6", "metadata": {"execution": {"iopub.execute_input": "2023-03-15T10:41:43.667840Z", "iopub.status.busy": "2023-03-15T10:41:43.667646Z", "iopub.status.idle": "2023-03-15T10:43:03.022970Z", "shell.execute_reply": "2023-03-15T10:43:03.022394Z"}, "papermill": {"duration": 79.365095, "end_time": "2023-03-15T10:43:03.025267", "exception": false, "start_time": "2023-03-15T10:41:43.660172", "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": ["IPU available: False, using: 0 IPUs\n"]}, {"name": "stderr", "output_type": "stream", "text": ["HPU available: False, using: 0 HPUs\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to ./cifar-10-python.tar.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "c4848daacda04a81a20662b6187fe7ec", "version_major": 2, "version_minor": 0}, "text/plain": [" 0%| | 0/170498071 [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Extracting ./cifar-10-python.tar.gz to ./\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stderr", "output_type": "stream", "text": ["You are using a CUDA device ('NVIDIA GeForce RTX 3090') that has Tensor Cores. To properly utilize them, you should set `torch.set_float32_matmul_precision('medium' | 'high')` which will trade-off precision for performance. For more details, read https://pytorch.org/docs/stable/generated/torch.set_float32_matmul_precision.html#torch.set_float32_matmul_precision\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\n", "-------------------------------------\n", "0 | model | Sequential | 855 K \n", "-------------------------------------\n", "855 K Trainable params\n", "0 Non-trainable params\n", "855 K Total params\n", "3.420 Total estimated model params size (MB)\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "39775d0472d045a2a2f8514823e50aab", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "24aaaed092154f1dae0b2eb77671890c", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "14afc18095f744af999fd2e77c9cc345", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "555a5d61027649b2bd73807fd0dea675", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "4369f8aa0c754849b250395b6470d551", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "5b96c454a77f4cfdb221f59965845774", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "07423e7d29a44e3d814620c574393e5f", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=5` reached.\n"]}], "source": ["dm = CIFAR10DataModule()\n", "model = LitModel(*dm.dims, dm.num_classes, hidden_size=256)\n", "trainer = L.Trainer(\n", " max_epochs=5,\n", " accelerator=\"auto\",\n", " devices=1,\n", ")\n", "trainer.fit(model, dm)"]}, {"cell_type": "markdown", "id": "cde1df55", "metadata": {"papermill": {"duration": 0.009122, "end_time": "2023-03-15T10:43:03.045457", "exception": false, "start_time": "2023-03-15T10:43:03.036335", "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 [Slack](https://www.pytorchlightning.ai/community)!\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: PyTorch Lightning DataModules\n", " :card_description: This notebook will walk you through how to start using Datamodules. With the release of `pytorch-lightning` version 0.9.0, we have included a new class called...\n", " :tags: GPU/TPU,Lightning-Examples"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab_type,id,colab,-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": "3.9.16"}, "papermill": {"default_parameters": {}, "duration": 149.453658, "end_time": "2023-03-15T10:43:04.285135", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/datamodules/datamodules.ipynb", "output_path": ".notebooks/lightning_examples/datamodules.ipynb", "parameters": {}, "start_time": "2023-03-15T10:40:34.831477", "version": "2.4.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"012e7ea03cb646e8884fcccdb6978946": {"model_module": 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