integrations/pytorch-lightning/showcase/Neptune-PyTorch-Lightning-basic.ipynb

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   "source": [
    "# PyTorch Lightning 1.x + Neptune [Basic Example]"
   ]
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   "source": [
    "Neptune helps you keep track of your machine learning experiments and if you are using PyTorch Lightning you can add tracking very easily.\n",
    "\n",
    "This quickstart will show you how to log PyTorch Lightning experiments to Neptune using ``NeptuneLogger`` (part of the pytorch-lightning library)."
   ]
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   "source": [
    "## Before you start"
   ]
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   "metadata": {
    "tags": [
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   "source": [
    "### Install dependencies"
   ]
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   "source": [
    "! pip install pytorch-lightning==1.0.0 neptune-client==0.4.123 torch==1.6.0 torchvision==0.7.0 -f https://download.pytorch.org/whl/torch_stable.html"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "header"
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   },
   "source": [
    "## Step 1: Import Libraries"
   ]
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  {
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   "metadata": {
    "tags": [
     "code"
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   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import DataLoader\n",
    "from torchvision.datasets import MNIST\n",
    "from torchvision import transforms\n",
    "\n",
    "import pytorch_lightning as pl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "header"
    ]
   },
   "source": [
    "## Step 2: Define Hyper-Parameters"
   ]
  },
  {
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   "metadata": {
    "tags": [
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   "source": [
    "PARAMS = {'max_epochs': 3,\n",
    "          'learning_rate': 0.005,\n",
    "          'batch_size': 32}"
   ]
  },
  {
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   "metadata": {
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    "id": "rf3FmHL11R0r",
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   },
   "source": [
    "## Step 3: Define LightningModule and DataLoader"
   ]
  },
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   "source": [
    "# pl.LightningModule\n",
    "class LitModel(pl.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.l1 = torch.nn.Linear(28 * 28, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        return torch.relu(self.l1(x.view(x.size(0), -1)))\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, y = batch\n",
    "        y_hat = self(x)\n",
    "        loss = F.cross_entropy(y_hat, y)\n",
    "        self.log('train_loss', loss)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=PARAMS['learning_rate'])\n",
    "\n",
    "# DataLoader\n",
    "train_loader = DataLoader(MNIST(os.getcwd(), download=True, transform=transforms.ToTensor()),\n",
    "                          batch_size=PARAMS['batch_size'])"
   ]
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   "source": [
    "## Step 4: Create NeptuneLogger"
   ]
  },
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   "source": [
    "from pytorch_lightning.loggers.neptune import NeptuneLogger\n",
    "\n",
    "neptune_logger = NeptuneLogger(\n",
    "    api_key=\"ANONYMOUS\",\n",
    "    project_name=\"shared/pytorch-lightning-integration\",\n",
    "    params=PARAMS)"
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   "source": [
    "## Step 5: Pass NeptuneLogger to the Trainer"
   ]
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    "trainer = pl.Trainer(max_epochs=PARAMS['max_epochs'],\n",
    "                     logger=neptune_logger)"
   ]
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   "source": [
    "## Step 6: Run experiment"
   ]
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    "model = LitModel()\n",
    "\n",
    "trainer.fit(model, train_loader)"
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    "colab_type": "text",
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   "source": [
    "## Explore Results"
   ]
  },
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   "metadata": {
    "tags": [
     "comment"
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   "source": [
    "You just learned how to start logging PyTorch Lightning experiments to Neptune, by using Neptune logger which is part of the lightning library.\n",
    "\n",
    "Above training is logged to Neptune in near real-time. Click on the link that was outputted to the console or [go here](https://ui.neptune.ai/o/shared/org/pytorch-lightning-integration/e/PYTOR-137883/charts) to explore an experiment similar to yours. In particular check:\n",
    "\n",
    "* [train metrics](https://ui.neptune.ai/o/shared/org/pytorch-lightning-integration/e/PYTOR-137883/charts),\n",
    "* [parameters](https://ui.neptune.ai/o/shared/org/pytorch-lightning-integration/e/PYTOR-137883/parameters),\n",
    "* [hardware](https://ui.neptune.ai/o/shared/org/pytorch-lightning-integration/e/PYTOR-137883/monitoring),\n",
    "* [metadata](https://ui.neptune.ai/o/shared/org/pytorch-lightning-integration/e/PYTOR-137883/details) including git summary info."
   ]
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