MNIST_with_PyTorch_Lightning_1.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/MNIST_with_PyTorch_Lightning_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installing the lightning"
      ],
      "metadata": {
        "id": "giyjuPu9S8dw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install lightning -q   "
      ],
      "metadata": {
        "id": "HCemyiiPEoUl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the necessary libraries"
      ],
      "metadata": {
        "id": "XQvD6jKUTBPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "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"
      ],
      "metadata": {
        "id": "Dvg5z0ADE1r_"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"torch version:\",torch.__version__)\n",
        "print(\"pytorch ligthening version:\",pl.__version__)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ydYEbS6vExz6",
        "outputId": "c29274e0-c5ea-4344-8458-1510f4380c61"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "torch version: 2.0.0+cu118\n",
            "pytorch ligthening version: 2.0.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Preparing the dataset with DataModelu"
      ],
      "metadata": {
        "id": "PKx9YIhtTI15"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "ptZNOfgGDo3R"
      },
      "outputs": [],
      "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):\n",
        "        entire_dataset = datasets.MNIST(\n",
        "            root=self.data_dir,\n",
        "            train=True,\n",
        "            transform=transforms.ToTensor(),\n",
        "            download=False,\n",
        "        )\n",
        "        self.train_ds, self.val_ds = random_split(entire_dataset, [50000, 10000])\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",
        "        )"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Building the model"
      ],
      "metadata": {
        "id": "wRs6Rx22TQXF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class NN(pl.LightningModule):\n",
        "  def __init__(self, input_size, learning_rate, num_classes):\n",
        "    super().__init__()\n",
        "    self.lr = learning_rate\n",
        "    self.fc1 = nn.Linear(input_size, 50)\n",
        "    self.fc2 = nn.Linear(50, num_classes)\n",
        "    self.loss_fn = nn.CrossEntropyLoss()\n",
        "    self.accuracy = torchmetrics.Accuracy(\n",
        "        task=\"multiclass\", num_classes=num_classes\n",
        "    )\n",
        "    self.f1_score = torchmetrics.F1Score(\n",
        "        task=\"multiclass\", num_classes=num_classes\n",
        "    )\n",
        "\n",
        "  def forward(self, x):\n",
        "    x = F.relu(self.fc1(x))\n",
        "    x = self.fc2(x)\n",
        "    return x\n",
        "\n",
        "  def training_step(self, batch, batch_idx):\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",
        "        )\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",
        "        x = x.reshape(x.size(0), -1)\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",
        "        x = x.reshape(x.size(0), -1)\n",
        "        scores = self.forward(x)\n",
        "        preds = torch.argmax(scores, dim=1)\n",
        "        return \n",
        "\n",
        "  def configure_optimizers(self):\n",
        "        return optim.Adam(self.parameters(), lr=self.lr)"
      ],
      "metadata": {
        "id": "d61wC1geLSHZ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setting configs"
      ],
      "metadata": {
        "id": "XvekC1DXPRh0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Training hyperparameters\n",
        "INPUT_SIZE = 784\n",
        "NUM_CLASSES = 10\n",
        "LEARNING_RATE = 0.001\n",
        "BATCH_SIZE = 64\n",
        "NUM_EPOCHS = 3\n",
        "\n",
        "# Dataset\n",
        "DATA_DIR = \"dataset/\"\n",
        "NUM_WORKERS = os.cpu_count()\n",
        "\n",
        "# Compute related\n",
        "ACCELERATOR = \"auto\"\n",
        "DEVICES = \"auto\"\n",
        "PRECISION = 16"
      ],
      "metadata": {
        "id": "DPM1K9Z1PDH7"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model"
      ],
      "metadata": {
        "id": "vbxdEzpdPKpC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = NN(\n",
        "    input_size=INPUT_SIZE,\n",
        "    learning_rate=LEARNING_RATE,\n",
        "    num_classes=NUM_CLASSES,\n",
        ")\n",
        "\n",
        "dm = MnistDataModule(\n",
        "    data_dir=DATA_DIR,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    num_workers=NUM_WORKERS,\n",
        ")\n",
        "\n",
        "trainer = pl.Trainer(\n",
        "    devices=ACCELERATOR,\n",
        "    accelerator=DEVICES, \n",
        "    min_epochs=1,\n",
        "    max_epochs=3,\n",
        "    precision=PRECISION,\n",
        ")\n",
        "\n",
        "trainer.fit(model, dm)\n",
        "trainer.validate(model, dm)\n",
        "trainer.test(model, dm)"
      ],
      "metadata": {
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        },
        "id": "5iAzFNSZPH4N",
        "outputId": "556b3b73-acba-4100-fa3d-8cccca4593c5"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/lightning_fabric/connector.py:562: UserWarning: 16 is supported for historical reasons but its usage is discouraged. Please set your precision to 16-mixed instead!\n",
            "  rank_zero_warn(\n",
            "INFO:pytorch_lightning.utilities.rank_zero:Using 16bit Automatic Mixed Precision (AMP)\n",
            "INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n",
            "INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n",
            "INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n",
            "INFO:pytorch_lightning.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, 70958242.01it/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, 96063198.91it/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, 24942084.83it/s]\n"
          ]
        },
        {
          "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": [
            "100%|██████████| 4542/4542 [00:00<00:00, 19579166.26it/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": [
            "WARNING:pytorch_lightning.loggers.tensorboard:Missing logger folder: /content/lightning_logs\n",
            "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 | fc1      | Linear             | 39.2 K\n",
            "1 | fc2      | Linear             | 510   \n",
            "2 | loss_fn  | CrossEntropyLoss   | 0     \n",
            "3 | accuracy | MulticlassAccuracy | 0     \n",
            "4 | f1_score | MulticlassF1Score  | 0     \n",
            "------------------------------------------------\n",
            "39.8 K    Trainable params\n",
            "0         Non-trainable params\n",
            "39.8 K    Total params\n",
            "0.159     Total estimated model params size (MB)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Sanity Checking: 0it [00:00, ?it/s]"
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        {
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          "text": [
            "INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=3` reached.\n",
            "INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n"
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              "┃\u001b[1m \u001b[0m\u001b[1m     Validate metric     \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[36m \u001b[0m\u001b[36m        val_loss         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.14731153845787048   \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
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              "┃<span style=\"font-weight: bold\">      Validate metric      </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         val_loss          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.14731153845787048    </span>│\n",
              "└───────────────────────────┴───────────────────────────┘\n",
              "</pre>\n"
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              "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│\u001b[36m \u001b[0m\u001b[36m        test_loss        \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.1555553376674652    \u001b[0m\u001b[35m \u001b[0m│\n",
              "└───────────────────────────┴───────────────────────────┘\n"
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              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_loss         </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.1555553376674652     </span>│\n",
              "└───────────────────────────┴───────────────────────────┘\n",
              "</pre>\n"
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        {
          "output_type": "execute_result",
          "data": {
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              "[{'test_loss': 0.1555553376674652}]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Resource\n",
        "- [Aladdin Persson GitHub ](https://github.com/aladdinpersson/Machine-Learning-Collection/tree/master/ML/Pytorch/pytorch_lightning)\n",
        "\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": "ds6S_UwyTbKH"
      }
    }
  ]
}