{"cells": [{"cell_type": "markdown", "id": "7e66f54a", "metadata": {"papermill": {"duration": 0.033319, "end_time": "2022-04-28T08:16:20.137906", "exception": false, "start_time": "2022-04-28T08:16:20.104587", "status": "completed"}, "tags": []}, "source": ["\n", "# PyTorch Lightning CIFAR10 ~94% Baseline Tutorial\n", "\n", "* **Author:** PL team\n", "* **License:** CC BY-SA\n", "* **Generated:** 2022-04-28T08:05:29.967173\n", "\n", "Train a Resnet to 94% accuracy on Cifar10!\n", "\n", "\n", "---\n", "Open in [![Open In Colab](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAHUAAAAUCAYAAACzrHJDAAAIuUlEQVRoQ+1ZaVRURxb+qhdolmbTUVSURpZgmLhHbQVFZIlGQBEXcMvJhKiTEzfigjQg7oNEJ9GMGidnjnNMBs2czIzajksEFRE1xklCTKJiQLRFsUGkoUWw+82pamn79etGYoKek1B/4NW99/tu3e/dquJBAGD27NkHALxKf39WY39gyrOi+i3xqGtUoePJrFmznrmgtModorbTu8YRNZk5cybXTvCtwh7o6NR2KzuZMWNGh6jtVt7nA0ymT5/eJlF9POrh7PAQl6s8bGYa3PUum//htmebVtLRqW0q01M5keTk5FZFzU0oRle3+zxwg5Hgtb+PZiL/ZVohxCI+hL5JgjmfjPxZ26+33BG3dA+ealHPM4gQAo5rU59gsI8bRvl54t3Ca62mvHyUAhtOlLd5WSQpKcluBjumnoCLs1EARkVd9E8l3p9y2i7RbQ1B6pFwu/YDgW8KbHJHMTQrwnjz2oZm9M4pavOCfo5jWrgCaaMVcMs6/pNhDr0+AMN93XlxV7R6DNpyzi7W/OE+yIrsjU6rTrbKV5cd/pNyItOmTbMp6sbBB+EqaYJY4cWE3VUciNt1TpgfcRFv71Fi54xT5kSoyLvOBEJMOMxWXkFlBeBSX4u6Zkcs+3KszYRtiapbNRqF31UgetVuc8z9vBXIv1qD+F1f83B6uDlCUyfsZGepGPpmg01OB7EITQbhS9ribKy+DmP1DUiClLz4bnIHVOqa7BY+Z1wg5g3zgUvyehiNpnJKxSLc/ts76LKm0BzX3c0RNy1yXjDcB5lWoro4iNHQxM+f1kWeWQARAWQS++trISJTp061Kep25X/MycwtjuctSC5rxo7ppi7VNUox5+PhPHtrsS2O1qJ6yx1QujQUzm9sh6hbkBlvvGcN8hYnwjUjH6kjfZEd5c/jitz5Jc5U3ENnFynKl4eB7nyEgP2UZ+Yz3/rVEbyYr27qELrtC4FIC0J7sc7xWnmccdHfRRTs0VB+cA4lt+oFcRR/wUeH8FG5w2Mbx8FQ8TXEvv1xYf4wBP3O2WyL3/UVjpXWgIqaFeUPr+wTmDvUB7njH6/bOv+HRg4SqioAg5GDe1aB3ZeMTJkyRSBqkLsWqSEm0fZVBEN94zEZnYvrdx1JL5cxe+a+AbhSJecRRHW/ikTFRTa38dtQlNZ5CRKwFvUtZU/kvBoEF9Uxni/XqIM+dwKbTw3rhcxIf7gmr2M+H6SMwx8iBzJbw5oxeG3Lv5FX9B3AGaHPS8e8z77H7v9VMpvPG5ug1enh7eGK8h0LBTwUb+GInqzInlRUK65DmTPQu4c3+uQKjwKK77zwUxBX4Tq7yR1RuiwUsqlrABCM6esHdXoy47fk4+prYKy8ZF574x4V5BnHQBuf4g9Z9ld8U36L2aktZNNplNfw7zotwWTy5MkCUft4aLEopJj5/OPHl1BQqeAVOnHgNSQOqmBzq9V9cfEm/yx5ubMGKS9cYPZ3vx2OS/c6PVHUuUO7Y1Pci3BO/1zgq18byebfGemLtNF+6JRtOvMk926ibussZqM+1mNz4TWkH7rCbM5phwGRGDAaoF8fY5OHFnlldAA8sgoEXKnDukA1NgSeNjqkJT9brbN4pC9WRweYXyLugR73c+MYvyWfu0yC6+mjzN1Isfw3FKJS98CU/zI1IHFkFPR52cHL2FJk0sB6kMTERIGo9GzcPkLNfA0cwdwi/hfEYO86ZMd9w+y1egfM2T2Eh/vesMNwljSzuZRT420SW3eqy8N6aHMmwmnFUZ7/PGVPbIoNZvNU1BURdHs0bT2+HjL8sDSM2e6vi4Lj5NW8WOLVA6RTT2azxLV+bglaFNqLieqemS/gWkw7NyoAHo+2dEsiivengjKsPFoqWOvbSh/kxPaxyW/JRzH2Fl3EzD9/xjAefJqB3usKUFn/0Gb+S/d/jy3FN2yLOmnSJJtn6oehByEiHPSeXnDxFGPRnoFoaBJjcdQlbDwcjL1zTNuQpoxD7R0OG0uUTMi0fkVwdzBdYIwcwZunxrVJVLplNm54BZp7jfDfYLoNyqQi1K6KxIdHzmN+QQ2WjFIwUT2zTGdlRXo4NFXVUO4sgX5dFC7f0aP/ZlNeUjFBuL8Xjl6uRuP6aMjSjpjzsH62FDU7JhBuGccEXIvDfJFFBc/gHw80dklfCVYnRaDfpiJcutPA4F7qJsfJeUPQI+1fqMlNhFx1FM0GDqkjFVg7NojlQ0Vt4aM5ReSqcbpaCg8nCW5lRsBvbT4T1TLfFptsfh7gItzuKTdJSEiwKSrt1vcmnEXXrsLbYnWDA1bu+z2WKy9Arq+1KRqdfKsoBo0GcdtEpS/B1bO4v0cFiUhkjskvKcMrWwtAPHuwQq8Z+4LZ1vTQANfXt4J0DwZX9gWa9qh4XDM/voC9JXfwYEMMHJcfNtusn82ihvliVUwg5KrPGVf6GH94ZJpEZBen6EC4qYTHA1dXhW0JIex8txzv//c8lhzXIi/BFxOH9jGbQhZsRalTIBZZ8KkGyZAxeRQvXkFF1TWz/Hm46jNYUnjPbt3JxIkT7f6dSj8qfJJyVvBxgaIlblOyjtysNHWN9fjjqWi7glJfW3/S0Hlj2XnA8PhKT9w6g3Qx3XiXhvuxQsuT1proxBKI/AaZqY1Xz5muvY8G8XkRRCaHsfQsRAFDH/tZPbcYuHotOG0FRIqB4HR3wNVoIPLtz8ycTguu+jpEigE218vd1YCr5m+HpHMvEI9u4LTXwNWaLjl0iPwGAmIpeHx1VeCqTJdPs1/vweweQPO3HC24NhOhnTphwoQnfv6QSY2ICbkNmdSA4h87oaLaiYfn5diIEd4att2erOwJXbPUHp953p6orQVSUVWRAXBT8c/dJ5L9xhzaJGp71GR/wFP8P5V2z10NSC9T93QM2xUg8fHxT+zU9ijeU4naHon8CjFJXFzc8/kn+dN06q9QgF98SYSo2Xen2NjYZy5sR6f+4nLSK5Iam2PH/x87a1YN/t5sBgAAAABJRU5ErkJggg==){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/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": "059220b3", "metadata": {"papermill": {"duration": 0.029846, "end_time": "2022-04-28T08:16:20.199491", "exception": false, "start_time": "2022-04-28T08:16:20.169645", "status": "completed"}, "tags": []}, "source": ["## Setup\n", "This notebook requires some packages besides pytorch-lightning."]}, {"cell_type": "code", "execution_count": 1, "id": "42a86c03", "metadata": {"colab": {}, "colab_type": "code", "execution": {"iopub.execute_input": "2022-04-28T08:16:20.266048Z", "iopub.status.busy": "2022-04-28T08:16:20.265498Z", "iopub.status.idle": "2022-04-28T08:16:23.514484Z", "shell.execute_reply": "2022-04-28T08:16:23.513899Z"}, "id": "LfrJLKPFyhsK", "lines_to_next_cell": 0, "papermill": {"duration": 3.285737, "end_time": "2022-04-28T08:16:23.514639", "exception": false, "start_time": "2022-04-28T08:16:20.228902", "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 \"pandas\" \"torch>=1.6, <1.9\" \"torchvision\" \"ipython[notebook]\" \"seaborn\" \"pytorch-lightning>=1.4\" \"torchmetrics>=0.6\" \"lightning-bolts\""]}, {"cell_type": "code", "execution_count": 2, "id": "941ef33a", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:16:23.582678Z", "iopub.status.busy": "2022-04-28T08:16:23.581761Z", "iopub.status.idle": "2022-04-28T08:16:23.584535Z", "shell.execute_reply": "2022-04-28T08:16:23.584950Z"}, "papermill": {"duration": 0.036745, "end_time": "2022-04-28T08:16:23.585085", "exception": false, "start_time": "2022-04-28T08:16:23.548340", "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": "de76b3ab", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:16:23.654358Z", "iopub.status.busy": "2022-04-28T08:16:23.653823Z", "iopub.status.idle": "2022-04-28T08:16:25.949844Z", "shell.execute_reply": "2022-04-28T08:16:25.950283Z"}, "papermill": {"duration": 2.333018, "end_time": "2022-04-28T08:16:25.950458", "exception": false, "start_time": "2022-04-28T08:16:23.617440", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["Global seed set to 7\n"]}], "source": ["import os\n", "\n", "import pandas as pd\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.core.display import display\n", "from pl_bolts.datamodules import CIFAR10DataModule\n", "from pl_bolts.transforms.dataset_normalizations import cifar10_normalization\n", "from pytorch_lightning import LightningModule, Trainer, seed_everything\n", "from pytorch_lightning.callbacks import LearningRateMonitor\n", "from pytorch_lightning.callbacks.progress import TQDMProgressBar\n", "from pytorch_lightning.loggers import CSVLogger\n", "from torch.optim.lr_scheduler import OneCycleLR\n", "from torch.optim.swa_utils import AveragedModel, update_bn\n", "from torchmetrics.functional import accuracy\n", "\n", "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": "34f13886", "metadata": {"papermill": {"duration": 0.066591, "end_time": "2022-04-28T08:16:26.050000", "exception": false, "start_time": "2022-04-28T08:16:25.983409", "status": "completed"}, "tags": []}, "source": ["### CIFAR10 Data Module\n", "\n", "Import the existing data module from `bolts` and modify the train and test transforms."]}, {"cell_type": "code", "execution_count": 4, "id": "f6a1c0e6", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:16:26.125632Z", "iopub.status.busy": "2022-04-28T08:16:26.125100Z", "iopub.status.idle": "2022-04-28T08:16:26.128348Z", "shell.execute_reply": "2022-04-28T08:16:26.127879Z"}, "papermill": {"duration": 0.04236, "end_time": "2022-04-28T08:16:26.128471", "exception": false, "start_time": "2022-04-28T08:16:26.086111", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:60: LightningDeprecationWarning: DataModule property `train_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\n", "/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:64: LightningDeprecationWarning: DataModule property `val_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\n", "/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:68: LightningDeprecationWarning: DataModule property `test_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\n"]}], "source": ["\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", "\n", "test_transforms = torchvision.transforms.Compose(\n", "    [\n", "        torchvision.transforms.ToTensor(),\n", "        cifar10_normalization(),\n", "    ]\n", ")\n", "\n", "cifar10_dm = CIFAR10DataModule(\n", "    data_dir=PATH_DATASETS,\n", "    batch_size=BATCH_SIZE,\n", "    num_workers=NUM_WORKERS,\n", "    train_transforms=train_transforms,\n", "    test_transforms=test_transforms,\n", "    val_transforms=test_transforms,\n", ")"]}, {"cell_type": "markdown", "id": "3ebc0eaa", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.035124, "end_time": "2022-04-28T08:16:26.195622", "exception": false, "start_time": "2022-04-28T08:16:26.160498", "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": "96ff098b", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:16:26.265523Z", "iopub.status.busy": "2022-04-28T08:16:26.265004Z", "iopub.status.idle": "2022-04-28T08:16:26.267046Z", "shell.execute_reply": "2022-04-28T08:16:26.267460Z"}, "lines_to_next_cell": 2, "papermill": {"duration": 0.039053, "end_time": "2022-04-28T08:16:26.267599", "exception": false, "start_time": "2022-04-28T08:16:26.228546", "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": "b1a35008", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.036155, "end_time": "2022-04-28T08:16:26.337499", "exception": false, "start_time": "2022-04-28T08:16:26.301344", "status": "completed"}, "tags": []}, "source": ["### Lightning Module\n", "Check out the [`configure_optimizers`](https://pytorch-lightning.readthedocs.io/en/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": "2dad8bee", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:16:26.416028Z", "iopub.status.busy": "2022-04-28T08:16:26.415462Z", "iopub.status.idle": "2022-04-28T08:16:26.417317Z", "shell.execute_reply": "2022-04-28T08:16:26.417741Z"}, "papermill": {"duration": 0.045442, "end_time": "2022-04-28T08:16:26.417903", "exception": false, "start_time": "2022-04-28T08:16:26.372461", "status": "completed"}, "tags": []}, "outputs": [], "source": ["class LitResnet(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)\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": "75af1ce6", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:16:26.488007Z", "iopub.status.busy": "2022-04-28T08:16:26.487474Z", "iopub.status.idle": "2022-04-28T08:21:55.895718Z", "shell.execute_reply": "2022-04-28T08:21:55.896255Z"}, "papermill": {"duration": 329.445947, "end_time": "2022-04-28T08:21:55.896421", "exception": false, "start_time": "2022-04-28T08:16:26.450474", "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": "stdout", "output_type": "stream", "text": ["Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /__w/1/s/.datasets/cifar-10-python.tar.gz\n"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "601f7f2f19734f35b81d452928e7b664", "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 /__w/1/s/.datasets/cifar-10-python.tar.gz to /__w/1/s/.datasets\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:88: LightningDeprecationWarning: DataModule property `train_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\n", "/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:107: LightningDeprecationWarning: DataModule property `val_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\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 | ResNet | 11.2 M\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"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "bddb7b6f7d3045b58c684d286860aca2", "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": "aee2f55863634bd489ee6a93f590ab1d", "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": "fc801be703164975836710a9088a3e2e", "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": "2fd0ebdad1cb4cdd8078e88d634a8e94", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, 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{"application/vnd.jupyter.widget-view+json": {"model_id": "8fc44743902b44b8bf643cfa2d3d5567", "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": "37c2f9e8f4544cb189ad9debf84344e8", "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": "c872d37d34dc4e00b72a782170d7a88e", "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": "0ae3d35d13ed4f25bd0b09e7154c92df", "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": "630e1930b9f14b5794ccecac534541a1", "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": "b6e410ec2ba14f8f86a3904e4cca9c5a", "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": "53849ee2ab8f41d0814af75ee735556a", "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": "42fc6f7641ea45f89ac3a52f7e0f8730", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:126: LightningDeprecationWarning: DataModule property `test_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\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": "8d2311d8b74f4f6f9fd32346ad7d4a42", "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\">    0.9193999767303467     </span>\u2502\n", "\u2502<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>\u2502<span style=\"color: #800080; text-decoration-color: #800080\">    0.28191840648651123    </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   0.9193999767303467    \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   0.28191840648651123   \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': 0.28191840648651123, 'test_acc': 0.9193999767303467}]"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["model = LitResnet(lr=0.05)\n", "\n", "trainer = Trainer(\n", "    max_epochs=30,\n", "    accelerator=\"auto\",\n", "    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", "    callbacks=[LearningRateMonitor(logging_interval=\"step\"), TQDMProgressBar(refresh_rate=10)],\n", ")\n", "\n", "trainer.fit(model, cifar10_dm)\n", "trainer.test(model, datamodule=cifar10_dm)"]}, {"cell_type": "code", "execution_count": 8, "id": "d922588d", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:21:56.074375Z", "iopub.status.busy": "2022-04-28T08:21:56.073844Z", "iopub.status.idle": "2022-04-28T08:21:57.094047Z", "shell.execute_reply": "2022-04-28T08:21:57.094462Z"}, "papermill": {"duration": 1.110399, "end_time": "2022-04-28T08:21:57.094616", "exception": false, "start_time": "2022-04-28T08:21:55.984217", "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", 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\n", "text/plain": ["<Figure size 443.75x360 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": "b01d4dda", "metadata": {"lines_to_next_cell": 2, "papermill": {"duration": 0.091004, "end_time": "2022-04-28T08:21:57.282939", "exception": false, "start_time": "2022-04-28T08:21:57.191935", "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": "c16c73b7", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:21:57.474351Z", "iopub.status.busy": "2022-04-28T08:21:57.473833Z", "iopub.status.idle": "2022-04-28T08:21:57.475521Z", "shell.execute_reply": "2022-04-28T08:21:57.475954Z"}, "papermill": {"duration": 0.100985, "end_time": "2022-04-28T08:21:57.476095", "exception": false, "start_time": "2022-04-28T08:21:57.375110", "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 training_epoch_end(self, training_step_outputs):\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)\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):\n", "        update_bn(self.trainer.datamodule.train_dataloader(), self.swa_model, device=self.device)"]}, {"cell_type": "code", "execution_count": 10, "id": "6752880d", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T08:21:57.673500Z", "iopub.status.busy": "2022-04-28T08:21:57.672979Z", "iopub.status.idle": "2022-04-28T12:54:51.075767Z", "shell.execute_reply": "2022-04-28T12:54:51.076220Z"}, "papermill": {"duration": 16373.508754, "end_time": "2022-04-28T12:54:51.076388", "exception": false, "start_time": "2022-04-28T08:21:57.567634", "status": "completed"}, "tags": []}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:261: UserWarning: 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", "  rank_zero_warn(\n"]}, {"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": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:88: LightningDeprecationWarning: DataModule property `train_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\n", "/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:107: LightningDeprecationWarning: DataModule property `val_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\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     | ResNet        | 11.2 M\n", "1 | swa_model | AveragedModel | 11.2 M\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"]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "d12f3eae2d1b42b88bbf0c60dc3174ca", "version_major": 2, "version_minor": 0}, "text/plain": ["Sanity 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{"application/vnd.jupyter.widget-view+json": {"model_id": "db696b12ec4349118973ee62a16726f7", "version_major": 2, "version_minor": 0}, "text/plain": ["Validation: 0it [00:00, ?it/s]"]}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stdout", "output_type": "stream", "text": ["Files already downloaded and verified\n"]}, {"name": "stderr", "output_type": "stream", "text": ["/home/AzDevOps_azpcontainer/.local/lib/python3.8/site-packages/pytorch_lightning/core/datamodule.py:126: LightningDeprecationWarning: DataModule property `test_transforms` was deprecated in v1.5 and will be removed in v1.7.\n", "  rank_zero_deprecation(\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": "f0229fff007c43aa90a336559ed78c4f", "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\">    0.9204999804496765     </span>\u2502\n", "\u2502<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>\u2502<span style=\"color: #800080; text-decoration-color: #800080\">    0.25821828842163086    </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   0.9204999804496765    \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   0.25821828842163086   \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': 0.25821828842163086, 'test_acc': 0.9204999804496765}]"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["swa_model = SWAResnet(model.model, lr=0.01)\n", "swa_model.datamodule = cifar10_dm\n", "\n", "swa_trainer = Trainer(\n", "    max_epochs=20,\n", "    accelerator=\"auto\",\n", "    devices=1 if torch.cuda.is_available() else None,  # limiting got iPython runs\n", "    callbacks=[TQDMProgressBar(refresh_rate=20)],\n", "    logger=CSVLogger(save_dir=\"logs/\"),\n", ")\n", "\n", "swa_trainer.fit(swa_model, cifar10_dm)\n", "swa_trainer.test(swa_model, datamodule=cifar10_dm)"]}, {"cell_type": "code", "execution_count": 11, "id": "537e60d9", "metadata": {"execution": {"iopub.execute_input": "2022-04-28T12:54:51.457816Z", "iopub.status.busy": "2022-04-28T12:54:51.457298Z", "iopub.status.idle": "2022-04-28T12:54:52.480722Z", "shell.execute_reply": "2022-04-28T12:54:52.481139Z"}, "papermill": {"duration": 1.255544, "end_time": "2022-04-28T12:54:52.481304", "exception": false, "start_time": "2022-04-28T12:54:51.225760", "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", " 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NaN"]}, "metadata": {}, "output_type": "display_data"}, {"data": {"text/plain": ["<seaborn.axisgrid.FacetGrid at 0x7f1ac8283e20>"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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\n", "text/plain": ["<Figure size 443.75x360 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": "3ab2d27e", "metadata": {"papermill": {"duration": 0.654577, "end_time": "2022-04-28T12:54:53.274782", "exception": false, "start_time": "2022-04-28T12:54:52.620205", "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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