{"cells": [{"cell_type": "markdown", "id": "da3bd453", "metadata": {"papermill": {"duration": 0.031743, "end_time": "2022-04-28T12:55:01.902365", "exception": false, "start_time": "2022-04-28T12:55:01.870622", "status": "completed"}, "tags": []}, "source": ["\n", "# Introduction to Pytorch Lightning\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2022-04-28T08:05:32.100192\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 [![Open In Colab](data:image/png;base64,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){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/PytorchLightning/pytorch-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": "8c5149be", "metadata": {"papermill": {"duration": 0.028475, "end_time": "2022-04-28T12:55:01.961385", "exception": false, "start_time": "2022-04-28T12:55:01.932910", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "dfb9657a", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2022-04-28T12:55:02.026114Z", "iopub.status.busy": "2022-04-28T12:55:02.025584Z", "iopub.status.idle": "2022-04-28T12:55:05.329954Z", "shell.execute_reply": "2022-04-28T12:55:05.329375Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.340052, "end_time": "2022-04-28T12:55:05.330108", "exception": false, "start_time": "2022-04-28T12:55:01.990056", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u001b[33mWARNING: You are using pip version 21.3.1; however, version 22.0.4 is available.\r\n", "You should consider upgrading via the '/usr/bin/python3.8 -m pip install --upgrade pip' command.\u001b[0m\r\n"]}], "source": ["! pip install --quiet \"seaborn\" \"pytorch-lightning>=1.4\" \"ipython[notebook]\" \"torch>=1.6, <1.9\" \"pandas\" \"torchvision\" \"torchmetrics>=0.6\""]}, {"cell_type": "code", "execution_count": 2, "id": "b6076c42", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:55:05.401729Z", "iopub.status.busy": "2022-04-28T12:55:05.401163Z", "iopub.status.idle": "2022-04-28T12:55:07.701858Z", "shell.execute_reply": "2022-04-28T12:55:07.702297Z"}, "papermill": {"duration": 2.339285, "end_time": "2022-04-28T12:55:07.702484", "exception": false, "start_time": "2022-04-28T12:55:05.363199", "status": "completed"}, "tags": []}, "outputs": [], "source": ["import os\n", "\n", "import pandas as pd\n", "import seaborn as sn\n", "import torch\n", "from IPython.core.display import display\n", "from pytorch_lightning import LightningModule, Trainer\n", "from pytorch_lightning.callbacks.progress import TQDMProgressBar\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", "PATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n", "BATCH_SIZE = 256 if torch.cuda.is_available() else 64"]}, {"cell_type": "markdown", "id": "56bda26b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.029813, "end_time": "2022-04-28T12:55:07.762240", "exception": false, "start_time": "2022-04-28T12:55:07.732427", "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": "2b9004c0", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:55:07.828553Z", "iopub.status.busy": "2022-04-28T12:55:07.827990Z", "iopub.status.idle": "2022-04-28T12:55:07.830545Z", "shell.execute_reply": "2022-04-28T12:55:07.830954Z"}, "papermill": {"duration": 0.037885, "end_time": "2022-04-28T12:55:07.831100", "exception": false, "start_time": "2022-04-28T12:55:07.793215", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class MNISTModel(LightningModule):\n", "    def __init__(self):\n", "        super().__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", "id": "d1051b01", "metadata": {"papermill": {"duration": 0.029818, "end_time": "2022-04-28T12:55:07.891613", "exception": false, "start_time": "2022-04-28T12:55:07.861795", "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": "e5854cb1", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:55:07.955938Z", "iopub.status.busy": "2022-04-28T12:55:07.955411Z", "iopub.status.idle": "2022-04-28T12:55:27.894253Z", "shell.execute_reply": "2022-04-28T12:55:27.893785Z"}, "papermill": {"duration": 19.973059, "end_time": "2022-04-28T12:55:27.894398", "exception": false, "start_time": "2022-04-28T12:55:07.921339", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True, 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": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\n", "  | Name | Type   | Params\n", "--------------------------------\n", "0 | l1   | Linear | 7.9 K \n", "--------------------------------\n", "7.9 K     Trainable params\n", "0         Non-trainable params\n", "7.9 K     Total params\n", "0.031     Total estimated model params size (MB)\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: 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 12 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": "7666e9e388ef4e8f8adcf93a08f2dafd", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["# Init our model\n", "mnist_model = MNISTModel()\n", "\n", "# Init DataLoader from MNIST Dataset\n", "train_ds = MNIST(PATH_DATASETS, train=True, download=True, transform=transforms.ToTensor())\n", "train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE)\n", "\n", "# Initialize a trainer\n", "trainer = Trainer(\n", "    accelerator=\"auto\",\n", "    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs\n", "    max_epochs=3,\n", "    callbacks=[TQDMProgressBar(refresh_rate=20)],\n", ")\n", "\n", "# Train the model \u26a1\n", "trainer.fit(mnist_model, train_loader)"]}, {"cell_type": "markdown", "id": "05c8b7d1", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.036078, "end_time": "2022-04-28T12:55:27.967422", "exception": false, "start_time": "2022-04-28T12:55:27.931344", "status": "completed"}, "tags": []}, "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`.\n", "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/stable/common/lightning_module.html#prepare-data) \ud83d\udcbe\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/stable/common/lightning_module.html#setup) \u2699\ufe0f\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/stable/api_references.html#core-api) \u267b\ufe0f\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": "f9fce56b", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:55:28.053056Z", "iopub.status.busy": "2022-04-28T12:55:28.042329Z", "iopub.status.idle": "2022-04-28T12:55:28.055187Z", "shell.execute_reply": "2022-04-28T12:55:28.055599Z"}, "papermill": {"duration": 0.052173, "end_time": "2022-04-28T12:55:28.055746", "exception": false, "start_time": "2022-04-28T12:55:28.003573", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitMNIST(LightningModule):\n", "    def __init__(self, data_dir=PATH_DATASETS, 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", "            [\n", "                transforms.ToTensor(),\n", "                transforms.Normalize((0.1307,), (0.3081,)),\n", "            ]\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", "        self.val_accuracy = Accuracy()\n", "        self.test_accuracy = Accuracy()\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", "        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, 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", "        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):\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=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": "code", "execution_count": 6, "id": "66816adb", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:55:28.141512Z", "iopub.status.busy": "2022-04-28T12:55:28.140982Z", "iopub.status.idle": "2022-04-28T12:56:00.878261Z", "shell.execute_reply": "2022-04-28T12:56:00.878683Z"}, "papermill": {"duration": 32.779357, "end_time": "2022-04-28T12:56:00.878860", "exception": false, "start_time": "2022-04-28T12:55:28.099503", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["GPU available: True, 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": ["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", "1 | val_accuracy  | Accuracy   | 0     \n", "2 | test_accuracy | Accuracy   | 0     \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": "c913d5cdf1a84bf6aeb3ce66641ba038", "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": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: 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 12 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": "d94d7beaed274e70b3f4ce83a035f40d", "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": "1b41411e37b44436adc2cdf8e9e56959", "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": "a851416ba0bd49f6bd4537685cfec517", "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": "b0f3fe4d77f945c7afb01e4f28a19fc8", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["model = LitMNIST()\n", "trainer = Trainer(\n", "    accelerator=\"auto\",\n", "    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs\n", "    max_epochs=3,\n", "    callbacks=[TQDMProgressBar(refresh_rate=20)],\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", ")\n", "trainer.fit(model)"]}, {"cell_type": "markdown", "id": "c021d8f5", "metadata": {"papermill": {"duration": 0.048069, "end_time": "2022-04-28T12:56:00.974852", "exception": false, "start_time": "2022-04-28T12:56:00.926783", "status": "completed"}, "tags": []}, "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\n", "test using the best saved checkpoint (conditioned on val_loss)."]}, {"cell_type": "code", "execution_count": 7, "id": "6f31bbf6", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:56:01.073612Z", "iopub.status.busy": "2022-04-28T12:56:01.072364Z", "iopub.status.idle": "2022-04-28T12:56:02.883287Z", "shell.execute_reply": "2022-04-28T12:56:02.883707Z"}, "papermill": {"duration": 1.861867, "end_time": "2022-04-28T12:56:02.883878", "exception": false, "start_time": "2022-04-28T12:56:01.022011", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1444: UserWarning: `.test(ckpt_path=None)` was called without a model. The best model of the previous `fit` call will be used. You can pass `test(ckpt_path='best')` to use and best model checkpoint and avoid this warning or `ckpt_path=trainer.checkpoint_callback.last_model_path` to use the last model.\n", "  rank_zero_warn(\n", "Restoring states from the checkpoint path at logs/lightning_logs/version_3/checkpoints/epoch=2-step=645.ckpt\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"name": "stderr", "output_type": "stream", "text": ["Loaded model weights from checkpoint at logs/lightning_logs/version_3/checkpoints/epoch=2-step=645.ckpt\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 12 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": "46147a5285ee4712a703d338c52be49e", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/html": ["<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n", "\u2503<span style=\"font-weight: bold\">        Test metric        </span>\u2503<span style=\"font-weight: bold\">       DataLoader 0        </span>\u2503\n", "\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n", "\u2502<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>\u2502<span style=\"color: #800080; text-decoration-color: #800080\">    0.9236999750137329     </span>\u2502\n", "\u2502<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>\u2502<span style=\"color: #800080; text-decoration-color: #800080\">    0.25315943360328674    </span>\u2502\n", "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n", "</pre>\n"], "text/plain": ["\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n", "\u2503\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m\u2503\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m\u2503\n", "\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n", "\u2502\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m   0.9236999750137329    \u001b[0m\u001b[35m \u001b[0m\u2502\n", "\u2502\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m\u2502\u001b[35m \u001b[0m\u001b[35m   0.25315943360328674   \u001b[0m\u001b[35m \u001b[0m\u2502\n", "\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["[{'test_loss': 0.25315943360328674, 'test_acc': 0.9236999750137329}]"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["trainer.test()"]}, {"cell_type": "markdown", "id": "7c7f9eda", "metadata": {"papermill": {"duration": 0.05379, "end_time": "2022-04-28T12:56:02.991785", "exception": false, "start_time": "2022-04-28T12:56:02.937995", "status": "completed"}, "tags": []}, "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": 8, "id": "6a6fbcd5", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:56:03.104409Z", "iopub.status.busy": "2022-04-28T12:56:03.103870Z", "iopub.status.idle": "2022-04-28T12:56:03.285768Z", "shell.execute_reply": "2022-04-28T12:56:03.286185Z"}, "papermill": {"duration": 0.240786, "end_time": "2022-04-28T12:56:03.286358", "exception": false, "start_time": "2022-04-28T12:56:03.045572", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/callbacks/model_checkpoint.py:611: UserWarning: Checkpoint directory logs/lightning_logs/version_3/checkpoints exists and is not empty.\n", "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n", "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", "1 | val_accuracy  | Accuracy   | 0     \n", "2 | test_accuracy | Accuracy   | 0     \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": "460f5141753046f9b54925aca37129ce", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}], "source": ["trainer.fit(model)"]}, {"cell_type": "markdown", "id": "ca97777f", "metadata": {"papermill": {"duration": 0.056861, "end_time": "2022-04-28T12:56:03.401171", "exception": false, "start_time": "2022-04-28T12:56:03.344310", "status": "completed"}, "tags": []}, "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": 9, "id": "248d37f8", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:56:03.519798Z", "iopub.status.busy": "2022-04-28T12:56:03.519287Z", "iopub.status.idle": "2022-04-28T12:56:03.863561Z", "shell.execute_reply": "2022-04-28T12:56:03.863975Z"}, "papermill": {"duration": 0.405875, "end_time": "2022-04-28T12:56:03.864175", "exception": false, "start_time": "2022-04-28T12:56:03.458300", "status": "completed"}, "tags": []}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>val_loss</th>\n", "      <th>val_acc</th>\n", "      <th>test_loss</th>\n", "      <th>test_acc</th>\n", "    </tr>\n", "    <tr>\n", "      <th>epoch</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>0.439232</td>\n", "      <td>0.8828</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>0.314095</td>\n", "      <td>0.9080</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>0.268804</td>\n", "      <td>0.9198</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>0.253159</td>\n", "      <td>0.9237</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["       val_loss  val_acc  test_loss  test_acc\n", "epoch                                        \n", "0      0.439232   0.8828        NaN       NaN\n", "1      0.314095   0.9080        NaN       NaN\n", "2      0.268804   0.9198        NaN       NaN\n", "2           NaN      NaN   0.253159    0.9237"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["<seaborn.axisgrid.FacetGrid at 0x7f0acc58dee0>"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 439.5x360 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["\n", "metrics = pd.read_csv(f\"{trainer.logger.log_dir}/metrics.csv\")\n", "del metrics[\"step\"]\n", "metrics.set_index(\"epoch\", inplace=True)\n", "display(metrics.dropna(axis=1, how=\"all\").head())\n", "sn.relplot(data=metrics, kind=\"line\")"]}, {"cell_type": "markdown", "id": "5d01bb2a", "metadata": {"papermill": {"duration": 0.060433, "end_time": "2022-04-28T12:56:03.987777", "exception": false, "start_time": "2022-04-28T12:56:03.927344", "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/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\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/PyTorchLightning/pytorch-lightning) or [Bolt](https://github.com/PyTorchLightning/lightning-bolts)\n", "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/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", "[![Pytorch 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