Comet_Logging_with_Lightning.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TirendazAcademy/PyTorch-Lightning-Tutorials/blob/main/Comet_Logging_with_Lightning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing lightning"
      ],
      "metadata": {
        "id": "qfQWw1IBwIR3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8Sm-_kbbVWUK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa2778ff-d629-47a5-b27b-c07ea3396435"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m21.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m501.8/501.8 kB\u001b[0m \u001b[31m32.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m201.7/201.7 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m506.5/506.5 kB\u001b[0m \u001b[31m45.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.3/3.3 MB\u001b[0m \u001b[31m92.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.3/54.3 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install lightning -q \n",
        "!pip install comet-ml -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing required libraries"
      ],
      "metadata": {
        "id": "Zzz_NWpkWrQk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import comet_ml\n",
        "import torch\n",
        "import torch.nn.functional as F\n",
        "import torchvision.datasets as datasets\n",
        "import torchvision.transforms as transforms\n",
        "from torch import nn, optim\n",
        "from torch.utils.data import DataLoader\n",
        "from torch.utils.data import random_split\n",
        "import pytorch_lightning as pl\n",
        "import torchmetrics\n",
        "from torchmetrics import Metric\n",
        "from pytorch_lightning.callbacks import EarlyStopping, Callback\n",
        "from torchvision.transforms import RandomHorizontalFlip, RandomVerticalFlip\n",
        "import torchvision\n",
        "from pytorch_lightning.loggers import TensorBoardLogger\n",
        "from lightning.pytorch.loggers import CometLogger"
      ],
      "metadata": {
        "id": "Jxq9vFaaW0kh"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Torch version:\", torch.__version__)\n",
        "print(\"Pytorch ligthening version:\", pl.__version__)\n",
        "print(\"Comet-ML version:\", comet_ml.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bA4g7Se8W5uA",
        "outputId": "18994d8e-61f7-42db-e5c4-9a8f3912d21e"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Torch version: 2.0.0+cu118\n",
            "Pytorch ligthening version: 2.0.2\n",
            "Comet-ML version: 3.32.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading the dataset"
      ],
      "metadata": {
        "id": "GNKyf6NOW-Ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MnistDataModule(pl.LightningDataModule):\n",
        "    def __init__(self, data_dir, batch_size, num_workers):\n",
        "        super().__init__()\n",
        "        self.data_dir = data_dir\n",
        "        self.batch_size = batch_size\n",
        "        self.num_workers = num_workers\n",
        "\n",
        "    def prepare_data(self):\n",
        "        datasets.MNIST(self.data_dir, train=True, download=True)\n",
        "        datasets.MNIST(self.data_dir, train=False, download=True)\n",
        "\n",
        "    def setup(self, stage=None):\n",
        "        # Assign train/val datasets for use in dataloaders\n",
        "        if stage == \"fit\" or stage is None:\n",
        "            entire_dataset = datasets.MNIST(\n",
        "                root=self.data_dir,  \n",
        "                train=True,\n",
        "                transform=transforms.Compose([\n",
        "                  transforms.RandomVerticalFlip(),\n",
        "                  transforms.RandomHorizontalFlip(),\n",
        "                  transforms.ToTensor()\n",
        "                ]),\n",
        "                download=False,\n",
        "            )\n",
        "            self.train_ds, self.val_ds = random_split(entire_dataset, [50000, 10000])       \n",
        "\n",
        "        # Assign test dataset for use in dataloader(s)     \n",
        "        if stage == \"test\" or stage is None:            \n",
        "            self.test_ds = datasets.MNIST(\n",
        "                root=self.data_dir,\n",
        "                train=False,\n",
        "                transform=transforms.ToTensor(),\n",
        "                download=False,\n",
        "            )\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(\n",
        "            self.train_ds,\n",
        "            batch_size=self.batch_size,\n",
        "            num_workers=self.num_workers,\n",
        "            shuffle=True,\n",
        "        )\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(\n",
        "            self.val_ds,\n",
        "            batch_size=self.batch_size,\n",
        "            num_workers=self.num_workers,\n",
        "            shuffle=False,\n",
        "        )\n",
        "\n",
        "    def test_dataloader(self):\n",
        "        return DataLoader(\n",
        "            self.test_ds,\n",
        "            batch_size=self.batch_size,\n",
        "            num_workers=self.num_workers,\n",
        "            shuffle=False,\n",
        "        )"
      ],
      "metadata": {
        "id": "UhbouvUjW-HE"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building the Model"
      ],
      "metadata": {
        "id": "J4ZJ052mYZcW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NN(pl.LightningModule):\n",
        "    def __init__(self, input_size, learning_rate, num_classes):\n",
        "        super().__init__()\n",
        "        \n",
        "        self.save_hyperparameters()\n",
        "        self.lr = learning_rate\n",
        "        self.loss_fn = nn.CrossEntropyLoss()\n",
        "        self.accuracy = torchmetrics.Accuracy(task=\"multiclass\", num_classes=num_classes)\n",
        "        self.f1_score = torchmetrics.F1Score(task=\"multiclass\", num_classes=num_classes)\n",
        "\n",
        "        # Neural Network architecture\n",
        "        self.layer1 = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "        self.layer2 = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=2, stride=2),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "        self.layer3 = torch.nn.Sequential(\n",
        "            torch.nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),\n",
        "            torch.nn.ReLU(),\n",
        "            torch.nn.MaxPool2d(kernel_size=2, stride=2, padding=1),\n",
        "            torch.nn.Dropout(p=0.5))\n",
        "        \n",
        "        self.fc1 = torch.nn.Linear(4 * 4 * 128, 625, bias=True)\n",
        "        torch.nn.init.xavier_uniform_(self.fc1.weight)\n",
        "        self.dense1_bn = torch.nn.BatchNorm1d(625)\n",
        "        self.fc2 = torch.nn.Linear(625, 10, bias=True)\n",
        "        torch.nn.init.xavier_uniform_(self.fc2.weight)\n",
        "        self.dense2_bn = torch.nn.BatchNorm1d(10)\n",
        "\n",
        "    def forward(self,x):\n",
        "        x = self.layer1(x)\n",
        "        x = self.layer2(x)\n",
        "        x = self.layer3(x)\n",
        "        x = x.view(x.size(0), -1)\n",
        "        x = self.dense1_bn(self.fc1(x))\n",
        "        x = self.dense2_bn(self.fc2(x))\n",
        "        x = F.softmax(x, dim=1)\n",
        "        return x\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        loss, scores, y = self._common_step(batch, batch_idx)\n",
        "        accuracy = self.accuracy(scores, y)\n",
        "        f1_score = self.f1_score(scores, y)   \n",
        "        self.log_dict(\n",
        "            {\n",
        "                \"train_loss\": loss,\n",
        "                \"train_accuracy\": accuracy,\n",
        "                \"train_f1_score\": f1_score,\n",
        "            },\n",
        "            on_step=False,\n",
        "            on_epoch=True,\n",
        "            prog_bar=True,\n",
        "        )      \n",
        "        return {\"loss\": loss, \"scores\": scores, \"y\": y}\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        loss, scores, y = self._common_step(batch, batch_idx)\n",
        "        self.log(\"val_loss\", loss)\n",
        "        return loss\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        loss, scores, y = self._common_step(batch, batch_idx)\n",
        "        self.log(\"test_loss\", loss)\n",
        "        return loss\n",
        "\n",
        "    def _common_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        scores = self.forward(x)\n",
        "        loss = self.loss_fn(scores, y)\n",
        "        return loss, scores, y\n",
        "\n",
        "    def predict_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        scores = self.forward(x)\n",
        "        preds = torch.argmax(scores, dim=1)\n",
        "        return preds\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return optim.Adam(self.parameters(), lr=self.lr)"
      ],
      "metadata": {
        "id": "YjGDmsuLYbob"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Callbacks"
      ],
      "metadata": {
        "id": "acTbkLzkydnv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class MyPrintingCallback(Callback):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "\n",
        "    def on_train_start(self, trainer, pl_module):\n",
        "        print(\"Starting to train!\")\n",
        "\n",
        "    def on_train_end(self, trainer, pl_module):\n",
        "        print(\"Training is done.\")"
      ],
      "metadata": {
        "id": "WALggEO5ycww"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "comet_logger = CometLogger(\n",
        "    api_key=os.environ.get(\"api_key\"),\n",
        "    workspace=os.environ.get(\"workspace\"),\n",
        "    project_name=os.environ.get(\"project_name\"),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Sn2dLU9bkSp",
        "outputId": "1fa7188d-a8c7-4825-ed38-f932733121ee"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: CometLogger will be initialized in online mode\n",
            "INFO:lightning.pytorch.loggers.comet:CometLogger will be initialized in online mode\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting the hyperparameters"
      ],
      "metadata": {
        "id": "Dpy8c0sehA1Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "hyper_params = {\n",
        "    # Training hyperparameters\n",
        "    \"INPUT_SIZE\": 784, \n",
        "    \"NUM_CLASSES\": 10, \n",
        "    \"LEARNING_RATE\": 0.001, \n",
        "    \"BATCH_SIZE\": 64,\n",
        "    \"NUM_EPOCHS\": 10,\n",
        "    # Dataset\n",
        "    \"DATA_DIR\": \"dataset/\",\n",
        "    \"NUM_WORKERS\": os.cpu_count(),\n",
        "    # Compute related\n",
        "    \"ACCELERATOR\": \"auto\",\n",
        "    \"DEVICES\": \"auto\",\n",
        "    }\n",
        "\n",
        "# Logging hyperparamters\n",
        "comet_logger.experiment.log_parameters(hyper_params)"
      ],
      "metadata": {
        "id": "TllALSUshB9Z",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4dfae3f7-8e57-4fb6-cff0-a0fb6491156a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/tirendaz-test/lightning-demo/fbab4d10b0a1494585c16a9a96c3d620\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model"
      ],
      "metadata": {
        "id": "pjslqXW_hIww"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = NN(\n",
        "    input_size=hyper_params[\"INPUT_SIZE\"],\n",
        "    learning_rate=hyper_params[\"LEARNING_RATE\"],\n",
        "    num_classes=hyper_params[\"NUM_CLASSES\"]\n",
        ")\n",
        "dm = MnistDataModule(\n",
        "    data_dir=hyper_params[\"DATA_DIR\"],\n",
        "    batch_size=hyper_params[\"BATCH_SIZE\"],\n",
        "    num_workers=hyper_params[\"NUM_WORKERS\"],\n",
        ")\n",
        "trainer = pl.Trainer(\n",
        "    logger=comet_logger,\n",
        "    accelerator=hyper_params[\"ACCELERATOR\"],\n",
        "    devices=hyper_params[\"DEVICES\"],\n",
        "    min_epochs=1,\n",
        "    max_epochs=hyper_params[\"NUM_EPOCHS\"],\n",
        "    callbacks=[MyPrintingCallback(), EarlyStopping(monitor=\"val_loss\")]\n",
        ")\n",
        "\n",
        "trainer.fit(model, dm)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "jPvqlcechE0r",
        "outputId": "25ee1440-7cd9-42d3-afd9-f963545bbd38"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: GPU available: True (cuda), used: True\n",
            "INFO:lightning.pytorch.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO: TPU available: False, using: 0 TPU cores\n",
            "INFO:lightning.pytorch.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO: IPU available: False, using: 0 IPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO: HPU available: False, using: 0 HPUs\n",
            "INFO:lightning.pytorch.utilities.rank_zero:HPU available: False, using: 0 HPUs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to dataset/MNIST/raw/train-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 9912422/9912422 [00:00<00:00, 325023540.79it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting dataset/MNIST/raw/train-images-idx3-ubyte.gz to dataset/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to dataset/MNIST/raw/train-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 28881/28881 [00:00<00:00, 43201032.03it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting dataset/MNIST/raw/train-labels-idx1-ubyte.gz to dataset/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to dataset/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 1648877/1648877 [00:00<00:00, 165527186.92it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting dataset/MNIST/raw/t10k-images-idx3-ubyte.gz to dataset/MNIST/raw\n",
            "\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
            "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n",
            "100%|██████████| 4542/4542 [00:00<00:00, 7669295.00it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Extracting dataset/MNIST/raw/t10k-labels-idx1-ubyte.gz to dataset/MNIST/raw\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n",
            "INFO:pytorch_lightning.callbacks.model_summary:\n",
            "  | Name      | Type               | Params\n",
            "-------------------------------------------------\n",
            "0 | loss_fn   | CrossEntropyLoss   | 0     \n",
            "1 | accuracy  | MulticlassAccuracy | 0     \n",
            "2 | f1_score  | MulticlassF1Score  | 0     \n",
            "3 | layer1    | Sequential         | 320   \n",
            "4 | layer2    | Sequential         | 18.5 K\n",
            "5 | layer3    | Sequential         | 73.9 K\n",
            "6 | fc1       | Linear             | 1.3 M \n",
            "7 | dense1_bn | BatchNorm1d        | 1.2 K \n",
            "8 | fc2       | Linear             | 6.3 K \n",
            "9 | dense2_bn | BatchNorm1d        | 20    \n",
            "-------------------------------------------------\n",
            "1.4 M     Trainable params\n",
            "0         Non-trainable params\n",
            "1.4 M     Total params\n",
            "5.523     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "76c8b2c8cbe048fbbf2f5b13789034db"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Starting to train!\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Training: 0it [00:00, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f52968d465af45a8a16426ae96c41957"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Validation: 0it [00:00, ?it/s]"
            ],
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        {
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          "data": {
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              "Validation: 0it [00:00, ?it/s]"
            ],
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              "model_id": "5131a2a134184b799d888ecfdaed3c4c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO: `Trainer.fit` stopped: `max_epochs=10` reached.\n",
            "INFO:lightning.pytorch.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=10` reached.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training is done.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml Experiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/tirendaz-test/lightning-demo/fbab4d10b0a1494585c16a9a96c3d620\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Metrics [count] (min, max):\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     loss [782]          : (1.5125749111175537, 2.3072807788848877)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_accuracy [10] : (0.6470000147819519, 0.9036399722099304)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_f1_score [10] : (0.6470000147819519, 0.9036399722099304)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     train_loss [10]     : (1.5792583227157593, 1.9541436433792114)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     val_loss [10]       : (1.520200490951538, 1.742313265800476)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Parameters:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     ACCELERATOR   : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     BATCH_SIZE    : 64\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DATA_DIR      : dataset/\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     DEVICES       : auto\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     INPUT_SIZE    : 784\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     LEARNING_RATE : 0.001\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     NUM_CLASSES   : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     NUM_EPOCHS    : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     NUM_WORKERS   : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     input_size    : 784\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     learning_rate : 0.001\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     num_classes   : 10\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     environment details : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     filename            : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     installed packages  : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model graph         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     notebook            : 2\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     os packages         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     source_code         : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Please wait for metadata to finish uploading (timeout is 3600 seconds)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the model \n",
        "my_model = NN.load_from_checkpoint(\n",
        "    \"/content/lightning-demo/fbab4d10b0a1494585c16a9a96c3d620/checkpoints/epoch=9-step=7820.ckpt\")"
      ],
      "metadata": {
        "id": "MjtUMJvTuU8R"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving Model in Comet-ML\n",
        "from comet_ml.integration.pytorch import log_model\n",
        "log_model(comet_logger.experiment, my_model, model_name=\"my_pl_model\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBYHCKvw7Qh1",
        "outputId": "e8149174-1592-414a-b1e4-d6c8f0a6a85a"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;214mCOMET WARNING:\u001b[0m As you are running in a Jupyter environment, you will need to call `experiment.end()` when finished to ensure all metrics and code are logged before exiting.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Couldn't find a Git repository in '/content' nor in any parent directory. Set `COMET_GIT_DIRECTORY` if your Git Repository is elsewhere.\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Experiment is live on comet.com https://www.comet.com/tirendaz-test/lightning-demo/fbab4d10b0a1494585c16a9a96c3d620\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Ending our experiment\n",
        "comet_logger.experiment.end()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bHzS8ylh8ZzP",
        "outputId": "4737a39a-fff4-41d1-e8be-f40a7d1fce05"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Comet.ml ExistingExperiment Summary\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m ---------------------------------------------------------------------------------------\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Data:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     display_summary_level : 1\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     url                   : https://www.comet.com/tirendaz-test/lightning-demo/fbab4d10b0a1494585c16a9a96c3d620\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Others:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     Created from : pytorch-lightning\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m   Uploads:\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m     model-element : 2 (5.28 MB)\n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m \n",
            "\u001b[1;38;5;39mCOMET INFO:\u001b[0m Uploading 1 metrics, params and output messages\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resource\n",
        "\n",
        "- [CometLogger](https://lightning.ai/docs/pytorch/latest/extensions/generated/lightning.pytorch.loggers.CometLogger.html#lightning.pytorch.loggers.comet.CometLogger.log_hyperparams)\n",
        "\n",
        "\n",
        "🔗 Let's connect [YouTube](http://youtube.com/tirendazacademy) | [Medium](http://tirendazacademy.medium.com) | [Twitter](http://twitter.com/tirendazacademy) | [Instagram](https://www.instagram.com/tirendazacademy) | [GitHub](http://github.com/tirendazacademy) | [Linkedin](https://www.linkedin.com/in/tirendaz-academy) | [Kaggle](https://www.kaggle.com/tirendazacademy) 😎"
      ],
      "metadata": {
        "id": "BKCUwTRMvSIv"
      }
    }
  ]
}