PyLiughtShort.ipynb

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  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0d3efd0",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install --quiet \"pytorch-lightning>=1.3\" \"torchvision\" \"torchmetrics>=0.3\" \"torch>=1.6, <1.9\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "29eafd96",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import torch\n",
    "from pytorch_lightning import LightningModule, Trainer\n",
    "from pytorch_lightning.metrics.functional import accuracy\n",
    "from torch import nn\n",
    "from torch.nn import functional as F\n",
    "from torch.utils.data import DataLoader, random_split\n",
    "from torchvision import transforms\n",
    "from torchvision.datasets import MNIST\n",
    "\n",
    "PATH_DATASETS = os.environ.get('PATH_DATASETS', '.')\n",
    "AVAIL_GPUS = min(1, torch.cuda.device_count())\n",
    "BATCH_SIZE = 256 if AVAIL_GPUS else 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bcee96b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MNISTModel(LightningModule):\n",
    "\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_nb):\n",
    "        x, y = batch\n",
    "        loss = F.cross_entropy(self(x), y)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        return torch.optim.Adam(self.parameters(), lr=0.02)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bba96a2d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 503: Service Unavailable\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./MNIST/raw/train-images-idx3-ubyte.gz\n"
     ]
    },
    {
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       "model_id": "47f7aac993aa478a91b51cf890277784",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9912422 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/train-images-idx3-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
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       "model_id": "b31ea977d8a845fd9757ae63051bded0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28881 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/train-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 503: Service Unavailable\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./MNIST/raw/t10k-images-idx3-ubyte.gz\n"
     ]
    },
    {
     "data": {
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       "model_id": "deaa96f92916417aaed746569181255e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1648877 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "data": {
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       "model_id": "3ea405871cc842438bbab747aea6ad73",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4542 [00:00<?, ?it/s]"
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     },
     "metadata": {},
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n",
      "  return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n",
      "GPU available: False, used: False\n",
      "TPU available: False, using: 0 TPU cores\n",
      "IPU available: False, using: 0 IPUs\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "\n",
      "Processing...\n",
      "Done!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  | Name | Type   | Params\n",
      "--------------------------------\n",
      "0 | l1   | Linear | 7.9 K \n",
      "--------------------------------\n",
      "7.9 K     Trainable params\n",
      "0         Non-trainable params\n",
      "7.9 K     Total params\n",
      "0.031     Total estimated model params size (MB)\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0e6b7643f4a49d89811af7dcd859178",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Training: -1it [00:00, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mnist_model = MNISTModel()\n",
    "\n",
    "# Init DataLoader from MNIST Dataset\n",
    "train_ds = MNIST(PATH_DATASETS, train=True, download=True, transform=transforms.ToTensor())\n",
    "train_loader = DataLoader(train_ds, batch_size=BATCH_SIZE)\n",
    "\n",
    "# Initialize a trainer\n",
    "trainer = Trainer(\n",
    "    gpus=AVAIL_GPUS,\n",
    "    max_epochs=3,\n",
    "    progress_bar_refresh_rate=20,\n",
    ")\n",
    "\n",
    "# Train the model ⚡\n",
    "trainer.fit(mnist_model, train_loader)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e163e3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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