{"cells": [{"cell_type": "markdown", "id": "6a0120be", "metadata": {"papermill": {"duration": 0.003897, "end_time": "2025-05-01T11:04:39.327017", "exception": false, "start_time": "2025-05-01T11:04:39.323120", "status": "completed"}, "tags": []}, "source": ["\n", "# PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "\n", "* **Author:** Lightning.ai\n", "* **License:** CC BY-SA\n", "* **Generated:** 2025-05-01T11:04:33.308873\n", "\n", "Train a Resnet to 94% accuracy on Cifar10!\n", "\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/cifar10-baseline.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": "e2170c72", "metadata": {"papermill": {"duration": 0.003119, "end_time": "2025-05-01T11:04:39.333449", "exception": false, "start_time": "2025-05-01T11:04:39.330330", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "7796ba92", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2025-05-01T11:04:39.340946Z", "iopub.status.busy": "2025-05-01T11:04:39.340749Z", "iopub.status.idle": "2025-05-01T11:04:40.494965Z", "shell.execute_reply": "2025-05-01T11:04:40.493707Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 1.160361, "end_time": "2025-05-01T11:04:40.497084", "exception": false, "start_time": "2025-05-01T11:04:39.336723", "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;49m25.1\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 \"numpy <3.0\" \"torchvision\" \"pandas\" \"matplotlib\" \"pytorch-lightning >=2.0,<2.6\" \"seaborn\" \"torch>=1.8.1, <2.8\" \"torchmetrics>=1.0, <1.8\""]}, {"cell_type": "code", "execution_count": 2, "id": "c515d833", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:04:40.505544Z", "iopub.status.busy": "2025-05-01T11:04:40.505171Z", "iopub.status.idle": "2025-05-01T11:04:40.510115Z", "shell.execute_reply": "2025-05-01T11:04:40.509273Z"}, "papermill": {"duration": 0.011015, "end_time": "2025-05-01T11:04:40.511598", "exception": false, "start_time": "2025-05-01T11:04:40.500583", "status": "completed"}, "tags": []}, "outputs": [], "source": ["# Run this if you intend to use TPUs\n", "# !pip install cloud-tpu-client==0.10 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.8-cp37-cp37m-linux_x86_64.whl"]}, {"cell_type": "code", "execution_count": 3, "id": "a458421f", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:04:40.519530Z", "iopub.status.busy": "2025-05-01T11:04:40.519187Z", "iopub.status.idle": "2025-05-01T11:04:44.383395Z", "shell.execute_reply": "2025-05-01T11:04:44.382316Z"}, "papermill": {"duration": 3.871153, "end_time": "2025-05-01T11:04:44.386115", "exception": false, "start_time": "2025-05-01T11:04:40.514962", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Seed set to 7\n"]}], "source": ["import os\n", "\n", "import pandas as pd\n", "import pytorch_lightning as pl\n", "import seaborn as sn\n", "import torch\n", "import torch.nn as nn\n", "import torch.nn.functional as F\n", "import torchvision\n", "from IPython.display import display\n", "from pytorch_lightning.callbacks import LearningRateMonitor\n", "from pytorch_lightning.loggers import CSVLogger\n", "from torch.optim.lr_scheduler import OneCycleLR\n", "from torch.optim.swa_utils import AveragedModel\n", "from torch.utils.data import DataLoader, random_split\n", "from torchmetrics.functional import accuracy\n", "from torchvision.datasets import CIFAR10\n", "\n", "pl.seed_everything(7)\n", "\n", "PATH_DATASETS = os.environ.get(\"PATH_DATASETS\", \".\")\n", "BATCH_SIZE = 256 if torch.cuda.is_available() else 64\n", "NUM_WORKERS = int(os.cpu_count() / 2)"]}, {"cell_type": "markdown", "id": "e5ffa60c", "metadata": {"papermill": {"duration": 0.005873, "end_time": "2025-05-01T11:04:44.398440", "exception": false, "start_time": "2025-05-01T11:04:44.392567", "status": "completed"}, "tags": []}, "source": ["### CIFAR10 DataLoaders\n"]}, {"cell_type": "code", "execution_count": 4, "id": "e2bc6e8a", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:04:44.411700Z", "iopub.status.busy": "2025-05-01T11:04:44.411397Z", "iopub.status.idle": "2025-05-01T11:05:32.415658Z", "shell.execute_reply": "2025-05-01T11:05:32.414667Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 48.014391, "end_time": "2025-05-01T11:05:32.418649", "exception": false, "start_time": "2025-05-01T11:04:44.404258", "status": "completed"}, "tags": []}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /__w/11/s/.datasets/cifar-10-python.tar.gz\n"]}, {"name": "stderr", "output_type": "stream", "text": ["\r", "  0%|          | 0/170498071 [00:00<?, ?it/s]"]}, {"name": 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/__w/11/s/.datasets/cifar-10-python.tar.gz to /__w/11/s/.datasets\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}], "source": ["\n", "cifar10_normalization = torchvision.transforms.Normalize(\n", "    mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],\n", "    std=[x / 255.0 for x in [63.0, 62.1, 66.7]],\n", ")\n", "\n", "\n", "def split_dataset(dataset, val_split=0.2, train=True):\n", "    \"\"\"Splits the dataset into train and validation set.\"\"\"\n", "    len_dataset = len(dataset)\n", "    splits = get_splits(len_dataset, val_split)\n", "    dataset_train, dataset_val = random_split(dataset, splits, generator=torch.Generator().manual_seed(42))\n", "\n", "    if train:\n", "        return dataset_train\n", "    return dataset_val\n", "\n", "\n", "def get_splits(len_dataset, val_split):\n", "    \"\"\"Computes split lengths for train and validation set.\"\"\"\n", "    if isinstance(val_split, int):\n", "        train_len = len_dataset - val_split\n", "        splits = [train_len, val_split]\n", "    elif isinstance(val_split, float):\n", "        val_len = int(val_split * len_dataset)\n", "        train_len = len_dataset - val_len\n", "        splits = [train_len, val_len]\n", "    else:\n", "        raise ValueError(f\"Unsupported type {type(val_split)}\")\n", "\n", "    return splits\n", "\n", "\n", "train_transforms = torchvision.transforms.Compose(\n", "    [\n", "        torchvision.transforms.RandomCrop(32, padding=4),\n", "        torchvision.transforms.RandomHorizontalFlip(),\n", "        torchvision.transforms.ToTensor(),\n", "        cifar10_normalization,\n", "    ]\n", ")\n", "test_transforms = torchvision.transforms.Compose(\n", "    [\n", "        torchvision.transforms.ToTensor(),\n", "        cifar10_normalization,\n", "    ]\n", ")\n", "\n", "dataset_train = CIFAR10(PATH_DATASETS, train=True, download=True, transform=train_transforms)\n", "dataset_val = CIFAR10(PATH_DATASETS, train=True, download=True, transform=test_transforms)\n", "dataset_train = split_dataset(dataset_train)\n", "dataset_val = split_dataset(dataset_val, train=False)\n", "dataset_test = CIFAR10(PATH_DATASETS, train=False, download=True, transform=test_transforms)\n", "\n", "train_dataloader = DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, num_workers=NUM_WORKERS)\n", "val_dataloader = DataLoader(dataset_val, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS)\n", "test_dataloader = DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False, num_workers=NUM_WORKERS)"]}, {"cell_type": "markdown", "id": "e238046b", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.018703, "end_time": "2025-05-01T11:05:32.462364", "exception": false, "start_time": "2025-05-01T11:05:32.443661", "status": "completed"}, "tags": []}, "source": ["### Resnet\n", "Modify the pre-existing Resnet architecture from TorchVision. The pre-existing architecture is based on ImageNet\n", "images (224x224) as input. So we need to modify it for CIFAR10 images (32x32)."]}, {"cell_type": "code", "execution_count": 5, "id": "4259607c", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:05:32.500507Z", "iopub.status.busy": "2025-05-01T11:05:32.500248Z", "iopub.status.idle": "2025-05-01T11:05:32.504383Z", "shell.execute_reply": "2025-05-01T11:05:32.503781Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.02511, "end_time": "2025-05-01T11:05:32.505563", "exception": false, "start_time": "2025-05-01T11:05:32.480453", "status": "completed"}, "tags": []}, "outputs": [], "source": ["def create_model():\n", "    model = torchvision.models.resnet18(pretrained=False, num_classes=10)\n", "    model.conv1 = nn.Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", "    model.maxpool = nn.Identity()\n", "    return model"]}, {"cell_type": "markdown", "id": "9b5fd6cc", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.018053, "end_time": "2025-05-01T11:05:32.542220", "exception": false, "start_time": "2025-05-01T11:05:32.524167", "status": "completed"}, "tags": []}, "source": ["### Lightning Module\n", "Check out the [`configure_optimizers`](https://lightning.ai/docs/pytorch/stable/common/lightning_module.html#configure-optimizers)\n", "method to use custom Learning Rate schedulers. The OneCycleLR with SGD will get you to around 92-93% accuracy\n", "in 20-30 epochs and 93-94% accuracy in 40-50 epochs. Feel free to experiment with different\n", "LR schedules from https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate"]}, {"cell_type": "code", "execution_count": 6, "id": "0a0bebb9", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:05:32.579918Z", "iopub.status.busy": "2025-05-01T11:05:32.579485Z", "iopub.status.idle": "2025-05-01T11:05:32.587929Z", "shell.execute_reply": "2025-05-01T11:05:32.587234Z"}, "papermill": {"duration": 0.028585, "end_time": "2025-05-01T11:05:32.589156", "exception": false, "start_time": "2025-05-01T11:05:32.560571", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitResnet(pl.LightningModule):\n", "    def __init__(self, lr=0.05):\n", "        super().__init__()\n", "\n", "        self.save_hyperparameters()\n", "        self.model = create_model()\n", "\n", "    def forward(self, x):\n", "        out = self.model(x)\n", "        return F.log_softmax(out, 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", "        self.log(\"train_loss\", loss)\n", "        return loss\n", "\n", "    def evaluate(self, batch, stage=None):\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", "\n", "        if stage:\n", "            self.log(f\"{stage}_loss\", loss, prog_bar=True)\n", "            self.log(f\"{stage}_acc\", acc, prog_bar=True)\n", "\n", "    def validation_step(self, batch, batch_idx):\n", "        self.evaluate(batch, \"val\")\n", "\n", "    def test_step(self, batch, batch_idx):\n", "        self.evaluate(batch, \"test\")\n", "\n", "    def configure_optimizers(self):\n", "        optimizer = torch.optim.SGD(\n", "            self.parameters(),\n", "            lr=self.hparams.lr,\n", "            momentum=0.9,\n", "            weight_decay=5e-4,\n", "        )\n", "        steps_per_epoch = 45000 // BATCH_SIZE\n", "        scheduler_dict = {\n", "            \"scheduler\": OneCycleLR(\n", "                optimizer,\n", "                0.1,\n", "                epochs=self.trainer.max_epochs,\n", "                steps_per_epoch=steps_per_epoch,\n", "            ),\n", "            \"interval\": \"step\",\n", "        }\n", "        return {\"optimizer\": optimizer, \"lr_scheduler\": scheduler_dict}"]}, {"cell_type": "code", "execution_count": 7, "id": "4c976072", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:05:32.626961Z", "iopub.status.busy": "2025-05-01T11:05:32.626264Z", "iopub.status.idle": "2025-05-01T11:06:34.684614Z", "shell.execute_reply": "2025-05-01T11:06:34.683932Z"}, "papermill": {"duration": 62.078771, "end_time": "2025-05-01T11:06:34.686006", "exception": false, "start_time": "2025-05-01T11:05:32.607235", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", "  warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", "  warnings.warn(msg)\n", "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": ["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 | Mode \n", "-----------------------------------------\n", "0 | model | ResNet | 11.2 M | train\n", "-----------------------------------------\n", "11.2 M    Trainable params\n", "0         Non-trainable params\n", "11.2 M    Total params\n", "44.696    Total estimated model params size (MB)\n", "68        Modules in train mode\n", "0         Modules in eval mode\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "831d9b64c86f489d82e0f441a3d6cc19", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b666731194af408bb2dd1d91d86f8ed5", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "fb9b97972f7c4ad1b173b244eb2535bd", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "7bde50f7f3ac4723987f13c18bb18a05", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0bc6d50e9df94bbdac22bfd1aae28447", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ff7a873d1db54c0ba1ef5bd0c031f1db", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "ac49dfbbb0194b23bef5be054c1ec075", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=5` reached.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "8c93738238484baba870c6a47389b3b9", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["\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\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\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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "       Test metric             DataLoader 0\n", "\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\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\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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "        test_acc            0.8180000185966492\n", "        test_loss           0.5285996198654175\n", "\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\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\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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n"]}, {"data": {"text/plain": ["[{'test_loss': 0.5285996198654175, 'test_acc': 0.8180000185966492}]"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["model = LitResnet(lr=0.05)\n", "\n", "trainer = pl.Trainer(\n", "    max_epochs=5,\n", "    accelerator=\"auto\",\n", "    devices=1,\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", "    callbacks=[LearningRateMonitor(logging_interval=\"step\")],\n", ")\n", "\n", "trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)\n", "trainer.test(model, dataloaders=test_dataloader)"]}, {"cell_type": "code", "execution_count": 8, "id": "25903e3d", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:06:34.737647Z", "iopub.status.busy": "2025-05-01T11:06:34.736973Z", "iopub.status.idle": "2025-05-01T11:06:35.311312Z", "shell.execute_reply": "2025-05-01T11:06:35.310560Z"}, "papermill": {"duration": 0.606073, "end_time": "2025-05-01T11:06:35.313262", "exception": false, "start_time": "2025-05-01T11:06:34.707189", "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 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<td>1.856030</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.034132</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0.0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.684393</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.062440</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         lr-SGD  test_acc  test_loss  train_loss  val_acc  val_loss\n", "epoch                                                              \n", "NaN    0.012079       NaN        NaN         NaN      NaN       NaN\n", "0.0         NaN       NaN        NaN    1.856030      NaN       NaN\n", "NaN    0.034132       NaN        NaN         NaN      NaN       NaN\n", "0.0         NaN       NaN        NaN    1.684393      NaN       NaN\n", "NaN    0.062440       NaN        NaN         NaN      NaN       NaN"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["<seaborn.axisgrid.FacetGrid at 0x7fc10314c550>"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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", "text/plain": ["<Figure size 616.125x500 with 1 Axes>"]}, "metadata": {}, "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": "74f3cea6", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.029625, "end_time": "2025-05-01T11:06:35.388789", "exception": false, "start_time": "2025-05-01T11:06:35.359164", "status": "completed"}, "tags": []}, "source": ["### Bonus: Use [Stochastic Weight Averaging](https://arxiv.org/abs/1803.05407) to get a boost on performance\n", "\n", "Use SWA from torch.optim to get a quick performance boost. Also shows a couple of cool features from Lightning:\n", "- Use `training_epoch_end` to run code after the end of every epoch\n", "- Use a pretrained model directly with this wrapper for SWA"]}, {"cell_type": "code", "execution_count": 9, "id": "3ce49f19", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:06:35.461325Z", "iopub.status.busy": "2025-05-01T11:06:35.461158Z", "iopub.status.idle": "2025-05-01T11:06:35.467802Z", "shell.execute_reply": "2025-05-01T11:06:35.467005Z"}, "papermill": {"duration": 0.057893, "end_time": "2025-05-01T11:06:35.468998", "exception": false, "start_time": "2025-05-01T11:06:35.411105", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class SWAResnet(LitResnet):\n", "    def __init__(self, trained_model, lr=0.01):\n", "        super().__init__()\n", "\n", "        self.save_hyperparameters(\"lr\")\n", "        self.model = trained_model\n", "        self.swa_model = AveragedModel(self.model)\n", "\n", "    def forward(self, x):\n", "        out = self.swa_model(x)\n", "        return F.log_softmax(out, dim=1)\n", "\n", "    def on_train_epoch_end(self):\n", "        self.swa_model.update_parameters(self.model)\n", "\n", "    def validation_step(self, batch, batch_idx, stage=None):\n", "        x, y = batch\n", "        logits = F.log_softmax(self.model(x), dim=1)\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", "\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.SGD(self.model.parameters(), lr=self.hparams.lr, momentum=0.9, weight_decay=5e-4)\n", "        return optimizer\n", "\n", "    # def on_train_end(self):  # todo: failing as trainer has only dataloaders, not datamodules\n", "    #     update_bn(self.trainer.datamodule.train_dataloader(), self.swa_model, device=self.device)"]}, {"cell_type": "code", "execution_count": 10, "id": "afc2b14b", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:06:35.511253Z", "iopub.status.busy": "2025-05-01T11:06:35.511037Z", "iopub.status.idle": "2025-05-01T11:52:44.920645Z", "shell.execute_reply": "2025-05-01T11:52:44.919455Z"}, "papermill": {"duration": 2769.481934, "end_time": "2025-05-01T11:52:44.971704", "exception": false, "start_time": "2025-05-01T11:06:35.489770", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/usr/local/lib/python3.10/dist-packages/pytorch_lightning/utilities/parsing.py:208: Attribute 'trained_model' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['trained_model'])`.\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", "  warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=None`.\n", "  warnings.warn(msg)\n", "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     | ResNet        | 11.2 M | train\n", "1 | swa_model | AveragedModel | 11.2 M | train\n", "----------------------------------------------------\n", "22.3 M    Trainable params\n", "0         Non-trainable params\n", "22.3 M    Total params\n", "89.392    Total estimated model params size (MB)\n", "137       Modules in train mode\n", "0         Modules in eval mode\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "0ac69fe7a38745139218c61c171c3f45", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity Checking: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d29279e5510e40c08f269cdcecd82e72", "version_major": 2, "version_minor": 0}, "text/plain": ["Training: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "91bf04d2b9644efdb81cfaab2ca229e5", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "2a42f3c792bf4db8b64dd9776371f1f9", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "740b066e05f04c1d9ebf2a3a0dd79e88", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "346ecbda42ac4ce7b45f1d8264c2e2f9", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "10608a4f14bc4fc39eea83ab641a3360", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stderr", "output_type": "stream", "text": ["`Trainer.fit` stopped: `max_epochs=5` reached.\n"]}, {"name": "stderr", "output_type": "stream", "text": ["LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0,1]\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "b9c854ed463641759e36d4c9b0ab36ec", "version_major": 2, "version_minor": 0}, "text/plain": ["Testing: |          | 0/? [00:00<?, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["\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\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\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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "       Test metric             DataLoader 0\n", "\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\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\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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n", "        test_acc            0.8180000185966492\n", "        test_loss           0.5285996198654175\n", "\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\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\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\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\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n"]}, {"data": {"text/plain": ["[{'test_loss': 0.5285996198654175, 'test_acc': 0.8180000185966492}]"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["swa_model = SWAResnet(model.model, lr=0.01)\n", "\n", "swa_trainer = pl.Trainer(\n", "    max_epochs=5,\n", "    accelerator=\"auto\",\n", "    devices=1,\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", ")\n", "\n", "swa_trainer.fit(swa_model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)\n", "swa_trainer.test(swa_model, dataloaders=test_dataloader)"]}, {"cell_type": "code", "execution_count": 11, "id": "5e66b42d", "metadata": {"execution": {"iopub.execute_input": "2025-05-01T11:52:45.025569Z", "iopub.status.busy": "2025-05-01T11:52:45.025300Z", "iopub.status.idle": "2025-05-01T11:52:45.404037Z", "shell.execute_reply": "2025-05-01T11:52:45.403137Z"}, "papermill": {"duration": 0.404818, "end_time": "2025-05-01T11:52:45.406285", "exception": false, "start_time": "2025-05-01T11:52:45.001467", "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: 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<td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.034132</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>0.0</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>1.684393</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>NaN</th>\n", "      <td>0.062440</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["         lr-SGD  test_acc  test_loss  train_loss  val_acc  val_loss\n", "epoch                                                              \n", "NaN    0.012079       NaN        NaN         NaN      NaN       NaN\n", "0.0         NaN       NaN        NaN    1.856030      NaN       NaN\n", "NaN    0.034132       NaN        NaN         NaN      NaN       NaN\n", "0.0         NaN       NaN        NaN    1.684393      NaN       NaN\n", "NaN    0.062440       NaN        NaN         NaN      NaN       NaN"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["<seaborn.axisgrid.FacetGrid at 0x7fbf9d256e00>"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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", "text/plain": ["<Figure size 616.125x500 with 1 Axes>"]}, "metadata": {}, "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": "011616dd", "metadata": {"papermill": {"duration": 0.030886, "end_time": "2025-05-01T11:52:45.486372", "exception": false, "start_time": "2025-05-01T11:52:45.455486", "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", "[![Pytorch Lightning](data:image/png;base64,NDA0OiBOb3QgRm91bmQ=){height=\"60px\" width=\"240px\"}](https://pytorchlightning.ai)"]}, {"cell_type": "raw", "metadata": {"raw_mimetype": "text/restructuredtext"}, "source": [".. customcarditem::\n", "   :header: PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "   :card_description: Train a Resnet to 94% accuracy on Cifar10!\n", "   :tags: Image,GPU/TPU,Lightning-Examples"]}], "metadata": {"jupytext": {"cell_metadata_filter": "colab_type,colab,id,-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": 2888.438279, "end_time": "2025-05-01T11:52:46.933531", "environment_variables": {}, "exception": null, "input_path": "lightning_examples/cifar10-baseline/baseline.ipynb", "output_path": ".notebooks/lightning_examples/cifar10-baseline.ipynb", "parameters": {}, "start_time": "2025-05-01T11:04:38.495252", "version": "2.6.0"}, "widgets": {"application/vnd.jupyter.widget-state+json": {"state": {"005ac4e0b184462d8c20e3ffd86a4c24": {"model_module": "@jupyter-widgets/controls", 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