Intro_to_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/Intro_to_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 the lightning"
      ],
      "metadata": {
        "id": "TrUYA9xn6Tv9"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XMLFmlMS5eU3"
      },
      "outputs": [],
      "source": [
        "!pip install lightning -q"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the necessary libraries"
      ],
      "metadata": {
        "id": "c5szG_W-6fD2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from torch import nn\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision.datasets import CIFAR10\n",
        "from torchvision import transforms\n",
        "import pytorch_lightning as pl"
      ],
      "metadata": {
        "id": "XPFDpYtt59fe"
      },
      "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": "sIgXox9G57as",
        "outputId": "5d53a921-f9bd-47c8-c8d4-9868ceb0d4f1"
      },
      "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": [
        "# Bulding the model"
      ],
      "metadata": {
        "id": "0yDIS6aQ6mOj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(pl.LightningModule):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "\n",
        "        self.conv1 = nn.Conv2d(3, 6, 5)\n",
        "        self.pool = nn.MaxPool2d(2, 2)\n",
        "        self.conv2 = nn.Conv2d(6, 16, 5)\n",
        "        self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
        "        self.fc2 = nn.Linear(120, 84)\n",
        "        self.fc3 = nn.Linear(84, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = self.pool(nn.functional.relu(self.conv1(x)))\n",
        "        x = self.pool(nn.functional.relu(self.conv2(x)))\n",
        "        x = torch.flatten(x, 1)\n",
        "        x = nn.functional.relu(self.fc1(x))\n",
        "        x = nn.functional.relu(self.fc2(x))\n",
        "        x = self.fc3(x)\n",
        "        return x\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        logits = self(x)\n",
        "        loss = nn.functional.cross_entropy(logits, y)\n",
        "        self.log(\"train_loss\", loss)\n",
        "        return loss\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        x, y = batch\n",
        "        logits = self(x)\n",
        "        loss = nn.functional.cross_entropy(logits, y)\n",
        "        return loss\n",
        "        \n",
        "    def configure_optimizers(self):\n",
        "        optimizer = torch.optim.SGD(self.parameters(), lr=0.001, momentum=0.9)\n",
        "        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=7, gamma=0.1)\n",
        "        return [optimizer], [scheduler]"
      ],
      "metadata": {
        "id": "y_p3HPBm6FYj"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Loading the dataset"
      ],
      "metadata": {
        "id": "xPuUEAQt75zu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class CIFAR10DataModule(pl.LightningDataModule):\n",
        "    def __init__(self, batch_size=64):\n",
        "        super().__init__()\n",
        "        self.batch_size = batch_size\n",
        "        self.transform = transforms.Compose(\n",
        "            [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]\n",
        "        )\n",
        "\n",
        "    def prepare_data(self):\n",
        "        CIFAR10(root=\"./data\", train=True, download=True)\n",
        "        CIFAR10(root=\"./data\", train=False, download=True)\n",
        "\n",
        "    def setup(self, stage=None):\n",
        "        if stage == \"fit\" or stage is None:\n",
        "            self.train_dataset = CIFAR10(root=\"./data\", train=True, transform=self.transform)\n",
        "\n",
        "            # Split the train dataset into train and validation\n",
        "            self.train_dataset, self.val_dataset = torch.utils.data.random_split(\n",
        "                self.train_dataset, [45000, 5000]\n",
        "            )\n",
        "\n",
        "    def train_dataloader(self):\n",
        "        return DataLoader(self.train_dataset, batch_size=self.batch_size, num_workers=2, shuffle=True)\n",
        "\n",
        "    def val_dataloader(self):\n",
        "        return DataLoader(self.val_dataset, batch_size=self.batch_size, num_workers=2)"
      ],
      "metadata": {
        "id": "llO8pyhj722a"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training the model"
      ],
      "metadata": {
        "id": "nGCCTIn68aHH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = Net()\n",
        "data_module = CIFAR10DataModule(batch_size=64)\n",
        "\n",
        "trainer = pl.Trainer(devices=1, max_epochs=20)\n",
        "trainer.fit(model, data_module)\n",
        "# Evaluate on the validation set\n",
        "trainer.validate(model, data_module.val_dataloader())"
      ],
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        "outputId": "faa75456-9f7f-457b-8dfd-9dfcf13182eb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "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": [
            "Files already downloaded and verified\n",
            "Files already downloaded and verified\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 | conv1 | Conv2d    | 456   \n",
            "1 | pool  | MaxPool2d | 0     \n",
            "2 | conv2 | Conv2d    | 2.4 K \n",
            "3 | fc1   | Linear    | 48.1 K\n",
            "4 | fc2   | Linear    | 10.2 K\n",
            "5 | fc3   | Linear    | 850   \n",
            "------------------------------------\n",
            "62.0 K    Trainable params\n",
            "0         Non-trainable params\n",
            "62.0 K    Total params\n",
            "0.248     Total estimated model params size (MB)\n"
          ]
        },
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              "Sanity Checking: 0it [00:00, ?it/s]"
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            "Files already downloaded and verified\n",
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              "[{}]"
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    {
      "cell_type": "markdown",
      "source": [
        "## Resource\n",
        "\n",
        "- [PyTorch Lightning: An Introduction to the Lightning-Fast Deep Learning Framework](https://pub.towardsai.net/pytorch-lightning-an-introduction-to-the-lightning-fast-deep-learning-framework-14325519cbaf)\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": "csGx9aznBR85"
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}