lightning/pl_examples/bug_report/bug_report_model.ipynb

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        "![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": []
    }
  ]
}