01_intro_starter.ipynb

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  {
   "cell_type": "markdown",
   "id": "dbfb9335",
   "metadata": {},
   "source": [
    "# Introduction to W&B\n",
    "\n",
    "<!--- @wandbcode{dlai_01} -->\n",
    "\n",
    "We will add `wandb` to sprite classification model training, so that we can track and visualize important metrics, gain insights into our model's behavior and make informed decisions for model improvements. We will also see how to compare and analyze different experiments, collaborate with team members, and reproduce results effectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e9ba792c-2baa-4c19-a132-2ed82a759e79",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "from pathlib import Path\n",
    "from types import SimpleNamespace\n",
    "from tqdm.auto import tqdm\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import Adam\n",
    "from utilities import get_dataloaders\n",
    "\n",
    "import wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e0bfcc9",
   "metadata": {},
   "source": [
    "### Sprite classification\n",
    "\n",
    "We will build a simple model to classify sprites. You can see some examples of sprites and corresponding classes in the image below.\n",
    "\n",
    "<img src=\"sprite_sample.png\" alt=\"Alt Text\" width=\"700\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d51a9f7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "INPUT_SIZE = 3 * 16 * 16\n",
    "OUTPUT_SIZE = 5\n",
    "HIDDEN_SIZE = 256\n",
    "NUM_WORKERS = 2\n",
    "CLASSES = [\"hero\", \"non-hero\", \"food\", \"spell\", \"side-facing\"]\n",
    "DATA_DIR = Path('./data/')\n",
    "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available()  else \"cpu\")\n",
    "\n",
    "def get_model(dropout):\n",
    "    \"Simple MLP with Dropout\"\n",
    "    return nn.Sequential(\n",
    "        nn.Flatten(),\n",
    "        nn.Linear(INPUT_SIZE, HIDDEN_SIZE),\n",
    "        nn.BatchNorm1d(HIDDEN_SIZE),\n",
    "        nn.ReLU(),\n",
    "        nn.Dropout(dropout),\n",
    "        nn.Linear(HIDDEN_SIZE, OUTPUT_SIZE)\n",
    "    ).to(DEVICE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f33f739c-d7ef-4954-ae87-d5bdd6bf25ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's define a config object to store our hyperparameters\n",
    "config = SimpleNamespace(\n",
    "    epochs = 2,\n",
    "    batch_size = 128,\n",
    "    lr = 1e-5,\n",
    "    dropout = 0.5,\n",
    "    slice_size = 10_000,\n",
    "    valid_pct = 0.2,\n",
    ")"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5492ebb-2dfa-44ce-af6c-24655e45a2ed",
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   "source": [
    "def train_model(config):\n",
    "    \"Train a model with a given config\"\n",
    "    \n",
    "    wandb.init(\n",
    "        project=\"dlai_intro\",\n",
    "        config=config,\n",
    "    )\n",
    "\n",
    "    # Get the data\n",
    "    train_dl, valid_dl = get_dataloaders(DATA_DIR, \n",
    "                                         config.batch_size, \n",
    "                                         config.slice_size, \n",
    "                                         config.valid_pct)\n",
    "    n_steps_per_epoch = math.ceil(len(train_dl.dataset) / config.batch_size)\n",
    "\n",
    "    # A simple MLP model\n",
    "    model = get_model(config.dropout)\n",
    "\n",
    "    # Make the loss and optimizer\n",
    "    loss_func = nn.CrossEntropyLoss()\n",
    "    optimizer = Adam(model.parameters(), lr=config.lr)\n",
    "\n",
    "    example_ct = 0\n",
    "\n",
    "    for epoch in tqdm(range(config.epochs), total=config.epochs):\n",
    "        model.train()\n",
    "\n",
    "        for step, (images, labels) in enumerate(train_dl):\n",
    "            images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
    "\n",
    "            outputs = model(images)\n",
    "            train_loss = loss_func(outputs, labels)\n",
    "            optimizer.zero_grad()\n",
    "            train_loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            example_ct += len(images)\n",
    "            metrics = {\n",
    "                \"train/train_loss\": train_loss,\n",
    "                \"train/epoch\": epoch + 1,\n",
    "                \"train/example_ct\": example_ct\n",
    "            }\n",
    "            wandb.log(metrics)\n",
    "            \n",
    "        # Compute validation metrics, log images on last epoch\n",
    "        val_loss, accuracy = validate_model(model, valid_dl, loss_func)\n",
    "        # Compute train and validation metrics\n",
    "        val_metrics = {\n",
    "            \"val/val_loss\": val_loss,\n",
    "            \"val/val_accuracy\": accuracy\n",
    "        }\n",
    "        wandb.log(val_metrics)\n",
    "    \n",
    "    wandb.finish()\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8401cf96",
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   "source": [
    "def validate_model(model, valid_dl, loss_func):\n",
    "    \"Compute the performance of the model on the validation dataset\"\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    correct = 0\n",
    "\n",
    "    with torch.inference_mode():\n",
    "        for i, (images, labels) in enumerate(valid_dl):\n",
    "            images, labels = images.to(DEVICE), labels.to(DEVICE)\n",
    "\n",
    "            # Forward pass\n",
    "            outputs = model(images)\n",
    "            val_loss += loss_func(outputs, labels) * labels.size(0)\n",
    "\n",
    "            # Compute accuracy and accumulate\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "            \n",
    "    return val_loss / len(valid_dl.dataset), correct / len(valid_dl.dataset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4cac7d2",
   "metadata": {},
   "source": [
    "### W&B account\n",
    "[Sign up](https://wandb.ai/site) for a free account at https://wandb.ai/site and then login to your wandb account to store the results of your experiments and use advanced W&B features. You can also continue to learn in anonymous mode. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "803c37e2-7ff5-46a6-afb7-b80cb69f7501",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: (1) Private W&B dashboard, no account required\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: (2) Use an existing W&B account\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "wandb: Enter your choice: 2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: You chose 'Use an existing W&B account'\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ········\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: C:\\Users\\kawam/.netrc\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.login(anonymous=\"allow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3df2485",
   "metadata": {},
   "source": [
    "### Train model\n",
    "Let's train the model with default config and check how it's doing in W&B. "
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "id": "9423c964-f7e3-4d3b-8a24-e70f7f4414c6",
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    {
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     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mkawamura-ryota-94\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁████████████████████</td></tr><tr><td>train/example_ct</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/train_loss</td><td>▇█▆▇▇▆▇▅▆▅▆▄▆▅▆▅▄▄▄▃▃▃▃▄▄▃▃▂▂▂▃▁▂▂▂▃▂▁▁▂</td></tr><tr><td>val/val_accuracy</td><td>▁█</td></tr><tr><td>val/val_loss</td><td>█▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>2</td></tr><tr><td>train/example_ct</td><td>16000</td></tr><tr><td>train/train_loss</td><td>1.42892</td></tr><tr><td>val/val_accuracy</td><td>0.5635</td></tr><tr><td>val/val_loss</td><td>1.29923</td></tr></table><br/></div></div>"
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    "train_model(config)"
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       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁████████████████████</td></tr><tr><td>train/example_ct</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/train_loss</td><td>█▇▇▆▆▅▅▅▄▄▃▄▃▃▃▃▂▃▂▂▂▂▂▂▂▂▂▂▁▁▁▁▁▁▁▁▁▂▁▁</td></tr><tr><td>val/val_accuracy</td><td>▁█</td></tr><tr><td>val/val_loss</td><td>█▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>2</td></tr><tr><td>train/example_ct</td><td>16000</td></tr><tr><td>train/train_loss</td><td>0.3946</td></tr><tr><td>val/val_accuracy</td><td>0.9415</td></tr><tr><td>val/val_loss</td><td>0.32857</td></tr></table><br/></div></div>"
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    "# So let's change the learning rate to a 1e-3 \n",
    "# and see how this affects our results.\n",
    "config.lr = 1e-4\n",
    "train_model(config)"
   ]
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   "cell_type": "code",
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   "id": "7837e71e-1e26-496b-b5e2-edabcf3fc676",
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       "Run data is saved locally in <code>C:\\Users\\kawam\\DeepLearning.AI\\Evaluating-and-Debugging-Generative-AI\\wandb\\run-20230802_221639-j1wj0w32</code>"
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       " View project at <a href='https://wandb.ai/kawamura-ryota-94/dlai_intro' target=\"_blank\">https://wandb.ai/kawamura-ryota-94/dlai_intro</a>"
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     "data": {
      "text/html": [
       "Waiting for W&B process to finish... <strong style=\"color:green\">(success).</strong>"
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    {
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       "<style>\n",
       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁████████████████████</td></tr><tr><td>train/example_ct</td><td>▁▁▁▂▂▂▂▂▂▃▃▃▃▃▃▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇▇███</td></tr><tr><td>train/train_loss</td><td>█▇▆▆▆▅▅▄▅▄▄▃▃▃▃▂▃▃▂▂▂▂▂▂▂▁▂▂▂▁▁▂▂▁▁▁▁▁▁▁</td></tr><tr><td>val/val_accuracy</td><td>▁█</td></tr><tr><td>val/val_loss</td><td>█▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>2</td></tr><tr><td>train/example_ct</td><td>16000</td></tr><tr><td>train/train_loss</td><td>0.43456</td></tr><tr><td>val/val_accuracy</td><td>0.953</td></tr><tr><td>val/val_loss</td><td>0.3223</td></tr></table><br/></div></div>"
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    {
     "data": {
      "text/html": [
       " View run <strong style=\"color:#cdcd00\">sleek-star-3</strong> at: <a href='https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/j1wj0w32' target=\"_blank\">https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/j1wj0w32</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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     },
     "metadata": {},
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    },
    {
     "data": {
      "text/html": [
       "Find logs at: <code>.\\wandb\\run-20230802_221639-j1wj0w32\\logs</code>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "metadata": {},
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   ],
   "source": [
    "config.lr = 1e-4\n",
    "train_model(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "065e3961-a9b7-4ab0-94dd-a07ef17f9217",
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    },
    {
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       "Tracking run with wandb version 0.15.8"
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       "Run data is saved locally in <code>C:\\Users\\kawam\\DeepLearning.AI\\Evaluating-and-Debugging-Generative-AI\\wandb\\run-20230802_221654-x5jmnco4</code>"
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       "Syncing run <strong><a href='https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/x5jmnco4' target=\"_blank\">silvery-gorge-4</a></strong> to <a href='https://wandb.ai/kawamura-ryota-94/dlai_intro' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
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       "    table.wandb td:nth-child(1) { padding: 0 10px; text-align: left ; width: auto;} td:nth-child(2) {text-align: left ; width: 100%}\n",
       "    .wandb-row { display: flex; flex-direction: row; flex-wrap: wrap; justify-content: flex-start; width: 100% }\n",
       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/example_ct</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>train/train_loss</td><td>███▇▆▆▆▅▅▅▄▄▄▄▃▃▃▃▃▃▃▃▂▂▂▂▂▂▂▂▂▂▁▁▂▂▁▂▁▂</td></tr><tr><td>val/val_accuracy</td><td>▁</td></tr><tr><td>val/val_loss</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>1</td></tr><tr><td>train/example_ct</td><td>8000</td></tr><tr><td>train/train_loss</td><td>0.57452</td></tr><tr><td>val/val_accuracy</td><td>0.899</td></tr><tr><td>val/val_loss</td><td>0.53215</td></tr></table><br/></div></div>"
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    {
     "data": {
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       " View run <strong style=\"color:#cdcd00\">silvery-gorge-4</strong> at: <a href='https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/x5jmnco4' target=\"_blank\">https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/x5jmnco4</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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    {
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    "config.dropout = 0.1\n",
    "config.epochs = 1\n",
    "train_model(config)"
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  },
  {
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   "execution_count": 11,
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       "    .wandb-col { display: flex; flex-direction: column; flex-basis: 100%; flex: 1; padding: 10px; }\n",
       "    </style>\n",
       "<div class=\"wandb-row\"><div class=\"wandb-col\"><h3>Run history:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>train/example_ct</td><td>▁▁▁▁▂▂▂▂▂▃▃▃▃▃▄▄▄▄▄▄▅▅▅▅▅▅▆▆▆▆▆▇▇▇▇▇████</td></tr><tr><td>train/train_loss</td><td>█▆▄▄▃▃▃▃▂▂▃▂▂▂▂▂▂▂▁▁▁▁▁▂▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁▁</td></tr><tr><td>val/val_accuracy</td><td>▁</td></tr><tr><td>val/val_loss</td><td>▁</td></tr></table><br/></div><div class=\"wandb-col\"><h3>Run summary:</h3><br/><table class=\"wandb\"><tr><td>train/epoch</td><td>1</td></tr><tr><td>train/example_ct</td><td>8000</td></tr><tr><td>train/train_loss</td><td>0.04837</td></tr><tr><td>val/val_accuracy</td><td>0.989</td></tr><tr><td>val/val_loss</td><td>0.0604</td></tr></table><br/></div></div>"
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       " View run <strong style=\"color:#cdcd00\">jolly-water-5</strong> at: <a href='https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/qsg03nf1' target=\"_blank\">https://wandb.ai/kawamura-ryota-94/dlai_intro/runs/qsg03nf1</a><br/>Synced 4 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)"
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    {
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   "source": [
    "config.lr = 1e-3\n",
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  },
  {
   "cell_type": "code",
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   "id": "a772569d-5bc3-44db-8837-feae41c75b67",
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