{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "bug_report_model.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rR4_BAUYs3Mb"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i7XbLCXGkll9"
      },
      "source": [
        "# The Boring Model\n",
        "Replicate a bug you experience, using this model.\n",
        "\n",
        "[Remember! we're always available for support on Slack](https://join.slack.com/t/pytorch-lightning/shared_invite/zt-f6bl2l0l-JYMK3tbAgAmGRrlNr00f1A)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2LODD6w9ixlT"
      },
      "source": [
        "---\n",
        "## Setup env"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zK7-Gg69kMnG"
      },
      "source": [
        "%%capture\n",
        "! pip install -qU pytorch-lightning"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WvuSN5jEbY8P"
      },
      "source": [
        "---\n",
        "## Deps"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w4_TYnt_keJi"
      },
      "source": [
        "import os\n",
        "\n",
        "import torch\n",
        "from torch.utils.data import DataLoader, Dataset\n",
        "\n",
        "from pytorch_lightning import LightningModule, Trainer"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrJDukwPtUnS"
      },
      "source": [
        "---\n",
        "## Data\n",
        "Random data is best for debugging. If you needs special tensor shapes or batch compositions or dataloaders, modify as needed"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvgTiaZpkvwS"
      },
      "source": [
        "class RandomDataset(Dataset):\n",
        "    def __init__(self, size, num_samples):\n",
        "        self.len = num_samples\n",
        "        self.data = torch.randn(num_samples, size)\n",
        "\n",
        "    def __getitem__(self, index):\n",
        "        return self.data[index]\n",
        "\n",
        "    def __len__(self):\n",
        "        return self.len"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sxVlWjGhl02D"
      },
      "source": [
        "num_samples = 10000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V7ELesz1kVQo"
      },
      "source": [
        "class BoringModel(LightningModule):\n",
        "    def __init__(self):\n",
        "        super().__init__()\n",
        "        self.layer = torch.nn.Linear(32, 2)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.layer(x)\n",
        "\n",
        "    def training_step(self, batch, batch_idx):\n",
        "        loss = self(batch).sum()\n",
        "        self.log(\"train_loss\", loss)\n",
        "        return {\"loss\": loss}\n",
        "\n",
        "    def validation_step(self, batch, batch_idx):\n",
        "        loss = self(batch).sum()\n",
        "        self.log(\"valid_loss\", loss)\n",
        "\n",
        "    def test_step(self, batch, batch_idx):\n",
        "        loss = self(batch).sum()\n",
        "        self.log(\"test_loss\", loss)\n",
        "\n",
        "    def configure_optimizers(self):\n",
        "        return torch.optim.SGD(self.layer.parameters(), lr=0.1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ubvW3LGSupmt"
      },
      "source": [
        "---\n",
        "## Define the test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Dk6Ykv8lI7X"
      },
      "source": [
        "def run():\n",
        "    train_data = DataLoader(RandomDataset(32, 64), batch_size=2)\n",
        "    val_data = DataLoader(RandomDataset(32, 64), batch_size=2)\n",
        "    test_data = DataLoader(RandomDataset(32, 64), batch_size=2)\n",
        "\n",
        "    model = BoringModel()\n",
        "    trainer = Trainer(\n",
        "        default_root_dir=os.getcwd(),\n",
        "        limit_train_batches=1,\n",
        "        limit_val_batches=1,\n",
        "        limit_test_batches=1,\n",
        "        num_sanity_val_steps=0,\n",
        "        max_epochs=1,\n",
        "        enable_model_summary=False,\n",
        "    )\n",
        "    trainer.fit(model, train_dataloaders=train_data, val_dataloaders=val_data)\n",
        "    trainer.test(model, dataloaders=test_data)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4dPfTZVgmgxz"
      },
      "source": [
        "---\n",
        "## Run Test"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AAtq1hwSmjKe"
      },
      "source": [
        "run()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Flyi--SpvsJN"
      },
      "source": [
        "---\n",
        "## Environment\n",
        "Run this to get the environment details"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0-yvGFRoaDSi"
      },
      "source": [
        "%%capture\n",
        "! wget https://raw.githubusercontent.com/PyTorchLightning/pytorch-lightning/master/requirements/collect_env_details.py"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "quj4LUDgmFvj"
      },
      "source": [
        "! python collect_env_details.py"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}