mlflow-optuna-pytorch.ipynb

{
 "cells": [
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   "cell_type": "markdown",
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     "name": "#%% md\n"
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   },
   "source": [
    "# PyTorch: Using MLflow and Optuna for experiment tracking and hyperparameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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     "name": "stderr",
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     "text": [
      "/Users/sstoppel/miniconda3/envs/test-opt/lib/python3.8/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
      "  and should_run_async(code)\n"
     ]
    }
   ],
   "source": [
    "import optuna\n",
    "import mlflow\n",
    "import torch\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "\n",
    "from torchvision import datasets, transforms\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from mlflow import pytorch\n",
    "from pprint import pformat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Network "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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     "is_executing": false,
     "name": "#%%\n"
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   "outputs": [],
   "source": [
    "class Net(nn.Module):\n",
    "    def __init__(self, dropout=0.0):\n",
    "        super(Net, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(1, 32, 3, 1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, 1)\n",
    "        self.dropout1 = nn.Dropout2d(dropout)\n",
    "        self.dropout2 = nn.Dropout2d(dropout)\n",
    "        self.fc1 = nn.Linear(9216, 128)\n",
    "        self.fc2 = nn.Linear(128, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.conv1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.conv2(x)\n",
    "        x = F.relu(x)\n",
    "        x = F.max_pool2d(x, 2)\n",
    "        x = self.dropout1(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc1(x)\n",
    "        x = F.relu(x)\n",
    "        x = self.dropout2(x)\n",
    "        x = self.fc2(x)\n",
    "        output = F.log_softmax(x, dim=1)\n",
    "        return output\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Train and validation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
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   "outputs": [],
   "source": [
    "# Training loop\n",
    "def train(model, device, train_loader, optimizer, epoch):\n",
    "    model.train()\n",
    "    train_set_size = len(train_loader.dataset)\n",
    "    num_batches = len(train_loader)\n",
    "    train_loss = 0.0\n",
    "    for batch_idx, (data, target) in enumerate(train_loader):\n",
    "        data, target = data.to(device), target.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        output = model(data)\n",
    "        loss = F.nll_loss(output, target)\n",
    "        train_loss += loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        if batch_idx % 10 == 0:\n",
    "            batch_size = len(data)\n",
    "            print(f\"Train Epoch: {epoch} [{batch_idx * batch_size}/{train_set_size} \"\n",
    "                  f\"({100. * batch_idx / num_batches:.0f}%)]\\tLoss: {loss.item():.6f}\")\n",
    "    avg_train_loss = train_loss / num_batches\n",
    "    return avg_train_loss\n",
    "\n",
    "# Testing loop\n",
    "def validate(model, device, val_loader):\n",
    "    model.eval()\n",
    "    val_set_size = len(val_loader.dataset)\n",
    "    val_loss = 0\n",
    "    correct = 0\n",
    "    with torch.no_grad():\n",
    "        for data, target in val_loader:\n",
    "            data, target = data.to(device), target.to(device)\n",
    "            output = model(data)\n",
    "            val_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss\n",
    "            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability\n",
    "            correct += pred.eq(target.view_as(pred)).sum().item()\n",
    "\n",
    "    val_loss /= val_set_size\n",
    "\n",
    "    print(f\"Test set: Average loss: {val_loss:.4f}, Accuracy: {correct}/{val_set_size} \"\n",
    "          f\"({100. * correct / val_set_size:.0f}%)\\n\")\n",
    "    return val_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Main"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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   },
   "outputs": [],
   "source": [
    "\n",
    "# Obtain hyperparameters for this trial\n",
    "def suggest_hyperparameters(trial):\n",
    "    # Obtain the learning rate on a logarithmic scale\n",
    "    lr = trial.suggest_float(\"lr\", 1e-4, 1e-1, log=True)\n",
    "    # Obtain the dropout ratio in a range from 0.0 to 0.9 with step size 0.1\n",
    "    dropout = trial.suggest_float(\"dropout\", 0.0, 0.9, step=0.1)\n",
    "    # Obtain the optimizer to use by name\n",
    "    optimizer_name = trial.suggest_categorical(\"optimizer_name\", [\"Adam\", \"Adadelta\"])\n",
    "\n",
    "    print(f\"Suggested hyperparameters: \\n{pformat(trial.params)}\")\n",
    "    return lr, dropout, optimizer_name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "def get_mnist_dataloaders(batch_size=8):\n",
    "    # Load the MNIST train and test datasets and save them to ./data\n",
    "    mnist_train = datasets.MNIST('./data', train=True, download=True, transform=transforms.Compose([\n",
    "                                               transforms.ToTensor(),\n",
    "                                               transforms.Normalize((0.1307,), (0.3081,))\n",
    "                                           ]))\n",
    "    train_loader = torch.utils.data.DataLoader(mnist_train,\n",
    "                                                       batch_size=batch_size,\n",
    "                                                       shuffle=True)\n",
    "    mnist_test = datasets.MNIST('./data', train=False, download=True, transform=transforms.Compose([\n",
    "                                               transforms.ToTensor(),\n",
    "                                               transforms.Normalize((0.1307,), (0.3081,))\n",
    "                                           ]))\n",
    "    val_loader = torch.utils.data.DataLoader(mnist_test,\n",
    "                                                      batch_size=1000,\n",
    "                                                      shuffle=True)\n",
    "    return train_loader, val_loader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "is_executing": false,
     "name": "#%%\n"
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   "source": [
    "def objective(trial):\n",
    "    print(\"\\n********************************\\n\")\n",
    "    best_val_loss = float('Inf')\n",
    "    \n",
    "    # Start a new mlflow run\n",
    "    with mlflow.start_run():\n",
    "        # Get hyperparameter suggestions created by optuna and log them as params using mlflow\n",
    "        lr, dropout, optimizer_name = suggest_hyperparameters(trial)\n",
    "        mlflow.log_params(trial.params)\n",
    "\n",
    "        # Use CUDA if GPU is available and log device as param using mlflow\n",
    "        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "        mlflow.log_param(\"device\", device)\n",
    "\n",
    "        # Initialize network\n",
    "        model = Net(dropout=dropout).to(device)\n",
    "\n",
    "        # Pick an optimizer based on optuna's parameter suggestion\n",
    "        if optimizer_name == \"Adam\":\n",
    "            optimizer = optim.Adam(model.parameters(), lr=lr)\n",
    "        if optimizer_name == \"Adadelta\":\n",
    "            optimizer = optim.Adadelta(model.parameters(), lr=lr)\n",
    "        scheduler = StepLR(optimizer, step_size=1, gamma=0.7)\n",
    "        \n",
    "        # Get DataLoaders for MNIST train and validation set\n",
    "        train_loader, val_loader = get_mnist_dataloaders()\n",
    "        \n",
    "        # Network training & validation loop\n",
    "        for epoch in range(0, 5):\n",
    "            avg_train_loss = train(model, device, train_loader, optimizer, epoch)\n",
    "            avg_val_loss = validate(model, device, val_loader)\n",
    "            \n",
    "            if avg_val_loss <= best_val_loss:\n",
    "                best_val_loss = avg_val_loss\n",
    "\n",
    "            # Log average train and validation set loss metrics for the current epoch using mlflow\n",
    "            mlflow.log_metric(\"avg_train_losses\", avg_train_loss, step=epoch)\n",
    "            mlflow.log_metric(\"avg_val_loss\", avg_val_loss, step=epoch)\n",
    "            \n",
    "            scheduler.step()\n",
    "\n",
    "    # Return the best validation loss achieved by the network.\n",
    "    # This is needed as Optuna needs to know how the suggested hyperparameters are influencing the network loss.\n",
    "    return best_val_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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     "name": "#%%\n"
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   "source": [
    "def main():\n",
    "    # Create the optuna study which shares the experiment name\n",
    "    study = optuna.create_study(study_name=\"pytorch-mlflow-optuna\", direction=\"minimize\")\n",
    "    study.optimize(objective, n_trials=5)\n",
    "\n",
    "    # Print optuna study statistics\n",
    "    print(\"\\n++++++++++++++++++++++++++++++++++\\n\")\n",
    "    print(\"Study statistics: \")\n",
    "    print(\"  Number of finished trials: \", len(study.trials))\n",
    "\n",
    "    print(\"Best trial:\")\n",
    "    trial = study.best_trial\n",
    "\n",
    "    print(\"  Trial number: \", trial.number)\n",
    "    print(\"  Loss (trial value): \", trial.value)\n",
    "\n",
    "    print(\"  Params: \")\n",
    "    for key, value in trial.params.items():\n",
    "        print(\"    {}: {}\".format(key, value))\n"
   ]
  },
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   "execution_count": null,
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2020-11-16 12:11:06,622]\u001b[0m A new study created in memory with name: pytorch-mlflow-optuna\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "********************************\n",
      "\n",
      "Suggested hyperparameters: \n",
      "{'dropout': 0.1, 'lr': 0.001615819736728719, 'optimizer_name': 'Adadelta'}\n",
      "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.260692\n",
      "Train Epoch: 0 [80/60000 (0%)]\tLoss: 2.231736\n",
      "Train Epoch: 0 [160/60000 (0%)]\tLoss: 2.332082\n",
      "Train Epoch: 0 [240/60000 (0%)]\tLoss: 2.354262\n",
      "Train Epoch: 0 [320/60000 (1%)]\tLoss: 2.359829\n",
      "Train Epoch: 0 [400/60000 (1%)]\tLoss: 2.338420\n",
      "Train Epoch: 0 [480/60000 (1%)]\tLoss: 2.279206\n",
      "Train Epoch: 0 [560/60000 (1%)]\tLoss: 2.268904\n",
      "Train Epoch: 0 [640/60000 (1%)]\tLoss: 2.345856\n",
      "Train Epoch: 0 [720/60000 (1%)]\tLoss: 2.248383\n",
      "Train Epoch: 0 [800/60000 (1%)]\tLoss: 2.278030\n",
      "Train Epoch: 0 [880/60000 (1%)]\tLoss: 2.224254\n",
      "Train Epoch: 0 [960/60000 (2%)]\tLoss: 2.302792\n",
      "Train Epoch: 0 [1040/60000 (2%)]\tLoss: 2.215610\n",
      "Train Epoch: 0 [1120/60000 (2%)]\tLoss: 2.259586\n",
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      "Train Epoch: 0 [1280/60000 (2%)]\tLoss: 2.220117\n",
      "Train Epoch: 0 [1360/60000 (2%)]\tLoss: 2.184291\n",
      "Train Epoch: 0 [1440/60000 (2%)]\tLoss: 2.195797\n",
      "Train Epoch: 0 [1520/60000 (3%)]\tLoss: 2.190829\n",
      "Train Epoch: 0 [1600/60000 (3%)]\tLoss: 2.218215\n",
      "Train Epoch: 0 [1680/60000 (3%)]\tLoss: 2.174963\n",
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      "Test set: Average loss: 0.3285, Accuracy: 9089/10000 (91%)\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Test set: Average loss: 0.2770, Accuracy: 9198/10000 (92%)\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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      "Test set: Average loss: 0.2544, Accuracy: 9269/10000 (93%)\n",
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Test set: Average loss: 0.2421, Accuracy: 9281/10000 (93%)\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[32m[I 2020-11-16 12:31:18,363]\u001b[0m Trial 0 finished with value: 0.2334354248046875 and parameters: {'lr': 0.001615819736728719, 'dropout': 0.1, 'optimizer_name': 'Adadelta'}. Best is trial 0 with value: 0.2334354248046875.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set: Average loss: 0.2334, Accuracy: 9301/10000 (93%)\n",
      "\n",
      "\n",
      "********************************\n",
      "\n",
      "Suggested hyperparameters: \n",
      "{'dropout': 0.5, 'lr': 0.022053063057492, 'optimizer_name': 'Adadelta'}\n",
      "Train Epoch: 0 [0/60000 (0%)]\tLoss: 2.367640\n",
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      "Train Epoch: 0 [320/60000 (1%)]\tLoss: 2.083424\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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    {
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      "Train Epoch: 0 [57440/60000 (96%)]\tLoss: 0.110107\n",
      "Train Epoch: 0 [57520/60000 (96%)]\tLoss: 0.480557\n",
      "Train Epoch: 0 [57600/60000 (96%)]\tLoss: 0.261581\n",
      "Train Epoch: 0 [57680/60000 (96%)]\tLoss: 0.382418\n",
      "Train Epoch: 0 [57760/60000 (96%)]\tLoss: 0.133055\n",
      "Train Epoch: 0 [57840/60000 (96%)]\tLoss: 0.052646\n",
      "Train Epoch: 0 [57920/60000 (97%)]\tLoss: 0.018617\n",
      "Train Epoch: 0 [58000/60000 (97%)]\tLoss: 0.167688\n"
     ]
    }
   ],
   "source": [
    "main()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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