unit06-dl-tips/6.1-checkpointing/6.1-part3-checkpointing.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "97e9fe7b-ebc2-4166-aa97-12e1e12954b4",
   "metadata": {},
   "source": [
    "# Unit 6. Essential Deep Learning Tips & Tricks\n",
    "\n",
    "## 6.1 Model Checkpointing and Early Stopping\n",
    "\n",
    "## Part 3 -- ModelCheckpoint Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eec68953",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch    : 1.13.1\n",
      "lightning: 2.0.0rc1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -p torch,lightning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e80fef60",
   "metadata": {},
   "outputs": [],
   "source": [
    "import lightning as L\n",
    "import torch\n",
    "from lightning.pytorch.loggers import CSVLogger\n",
    "\n",
    "from shared_utilities import CustomDataModule, LightningModel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4dc80397-26f2-41de-80fc-572e240fca9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "class PyTorchMLP(torch.nn.Module):\n",
    "    def __init__(self, num_features, num_classes):\n",
    "        super().__init__()\n",
    "\n",
    "        self.all_layers = torch.nn.Sequential(\n",
    "            # 1st hidden layer\n",
    "            torch.nn.Linear(num_features, 100),\n",
    "            torch.nn.ReLU(),\n",
    "            # 2nd hidden layer\n",
    "            torch.nn.Linear(100, 50),\n",
    "            torch.nn.ReLU(),\n",
    "            # output layer\n",
    "            torch.nn.Linear(50, num_classes),\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = torch.flatten(x, start_dim=1)\n",
    "        logits = self.all_layers(x)\n",
    "        return logits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "aa7bcf14-8046-4213-accf-544b67e74883",
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.manual_seed(123)\n",
    "\n",
    "dm = CustomDataModule()\n",
    "\n",
    "pytorch_model = PyTorchMLP(num_features=100, num_classes=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "049d3bf9-a537-41d0-902f-35f4e79c94ba",
   "metadata": {},
   "source": [
    "# NEW: ModelCheckpoint Callback"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6180d181-263d-4495-b03c-1c8fde4a42b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightning.pytorch.callbacks import ModelCheckpoint\n",
    "\n",
    "callbacks = [\n",
    "    ModelCheckpoint(save_top_k=1, mode=\"max\", monitor=\"val_acc\", save_last=True)\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eaf13b54",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: True (mps), used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n",
      "HPU available: False, using: 0 HPUs\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/setup.py:201: UserWarning: MPS available but not used. Set `accelerator` and `devices` using `Trainer(accelerator='mps', devices=1)`.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/callbacks/model_checkpoint.py:612: UserWarning: Checkpoint directory logs/my-model/version_0/checkpoints exists and is not empty.\n",
      "  rank_zero_warn(f\"Checkpoint directory {dirpath} exists and is not empty.\")\n",
      "\n",
      "  | Name      | Type               | Params\n",
      "-------------------------------------------------\n",
      "0 | model     | PyTorchMLP         | 15.3 K\n",
      "1 | train_acc | MulticlassAccuracy | 0     \n",
      "2 | val_acc   | MulticlassAccuracy | 0     \n",
      "3 | test_acc  | MulticlassAccuracy | 0     \n",
      "-------------------------------------------------\n",
      "15.3 K    Trainable params\n",
      "0         Non-trainable params\n",
      "15.3 K    Total params\n",
      "0.061     Total estimated model params size (MB)\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/fabric/loggers/csv_logs.py:188: UserWarning: Experiment logs directory logs/my-model/version_0 exists and is not empty. Previous log files in this directory will be deleted when the new ones are saved!\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
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       "model_id": "f90ad1e481ec492bb4852b7907e9bde9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Sanity Checking: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n",
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
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       "model_id": "71da933d13814769a663c7edfb35deb9",
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      "text/plain": [
       "Training: 0it [00:00, ?it/s]"
      ]
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       "Validation: 0it [00:00, ?it/s]"
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       "Validation: 0it [00:00, ?it/s]"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`Trainer.fit` stopped: `max_epochs=10` reached.\n"
     ]
    }
   ],
   "source": [
    "lightning_model = LightningModel(model=pytorch_model, learning_rate=0.05)\n",
    "\n",
    "trainer = L.Trainer(\n",
    "    callbacks=callbacks,  # New!!!\n",
    "    max_epochs=10,\n",
    "    accelerator=\"cpu\",\n",
    "    devices=\"auto\",\n",
    "    logger=CSVLogger(save_dir=\"logs/\", name=\"my-model\"),\n",
    "    deterministic=True,\n",
    ")\n",
    "\n",
    "trainer.fit(model=lightning_model, datamodule=dm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "897d5146",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from shared_utilities import plot_csv_logger\n",
    "\n",
    "plot_csv_logger(csv_path=f\"{trainer.logger.log_dir}/metrics.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c92115b-7012-4f8f-94ba-0b19c972323a",
   "metadata": {},
   "source": [
    "# Use the Best Checkpoint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9f2cacd-674d-401e-adc5-e697547f3c07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sebastianraschka/Desktop/lightning-master/src/lightning/pytorch/trainer/connectors/data_connector.py:208: PossibleUserWarning: The dataloader, test_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 10 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n",
      "  rank_zero_warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0ca67880de644b16a308def16a38b5ea",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Testing: 0it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\">        Test metric        </span>┃<span style=\"font-weight: bold\">       DataLoader 0        </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│<span style=\"color: #008080; text-decoration-color: #008080\">         test_acc          </span>│<span style=\"color: #800080; text-decoration-color: #800080\">    0.8557500243186951     </span>│\n",
       "└───────────────────────────┴───────────────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8557500243186951    \u001b[0m\u001b[35m \u001b[0m│\n",
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     "text": [
      "Restoring states from the checkpoint path at logs/my-model/version_0/checkpoints/epoch=8-step=4050-v4.ckpt\n",
      "Lightning automatically upgraded your loaded checkpoint from v2.0.0rc1 to v2.0.0rc1. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_0/checkpoints/epoch=8-step=4050-v4.ckpt`\n",
      "Loaded model weights from the checkpoint at logs/my-model/version_0/checkpoints/epoch=8-step=4050-v4.ckpt\n"
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       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8622499704360962    \u001b[0m\u001b[35m \u001b[0m│\n",
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     "text": [
      "Restoring states from the checkpoint path at /Users/sebastianraschka/Desktop/6.1-checkpointing/logs/my-model/version_0/checkpoints/last-v4.ckpt\n",
      "Lightning automatically upgraded your loaded checkpoint from v2.0.0rc1 to v2.0.0rc1. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_0/checkpoints/last-v4.ckpt`\n",
      "Loaded model weights from the checkpoint at /Users/sebastianraschka/Desktop/6.1-checkpointing/logs/my-model/version_0/checkpoints/last-v4.ckpt\n"
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       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
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       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8557500243186951    \u001b[0m\u001b[35m \u001b[0m│\n",
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    {
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      "Lightning automatically upgraded your loaded checkpoint from v2.0.0rc1 to v2.0.0rc1. To apply the upgrade to your files permanently, run `python -m lightning.pytorch.utilities.upgrade_checkpoint --file logs/my-model/version_0/checkpoints/epoch=8-step=4050-v4.ckpt`\n"
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       "┃\u001b[1m \u001b[0m\u001b[1m       Test metric       \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      DataLoader 0       \u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
       "│\u001b[36m \u001b[0m\u001b[36m        test_acc         \u001b[0m\u001b[36m \u001b[0m│\u001b[35m \u001b[0m\u001b[35m   0.8622499704360962    \u001b[0m\u001b[35m \u001b[0m│\n",
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     "execution_count": 12,
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   "source": [
    "path = trainer.checkpoint_callback.best_model_path\n",
    "\n",
    "lightning_model = LightningModel.load_from_checkpoint(\n",
    "    path, learning_rate=0.1, model=pytorch_model\n",
    ")\n",
    "lightning_model.eval();\n",
    "\n",
    "trainer.test(model=lightning_model, datamodule=dm)"
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