hello-lightning.ipynb

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    {
     "name": "stdout",
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     "text": [
      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 9912422/9912422 [00:00<00:00, 133264027.32it/s]\n"
     ]
    },
    {
     "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",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./MNIST/raw/train-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 28881/28881 [00:00<00:00, 21743976.63it/s]\n"
     ]
    },
    {
     "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",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\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"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 1648877/1648877 [00:00<00:00, 136478103.10it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-images-idx3-ubyte.gz to ./MNIST/raw\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Failed to download (trying next):\n",
      "HTTP Error 403: Forbidden\n",
      "\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 4542/4542 [00:00<00:00, 3051013.58it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting ./MNIST/raw/t10k-labels-idx1-ubyte.gz to ./MNIST/raw\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "GPU available: False, 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",
      "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/lightning/pytorch/loops/utilities.py:73: `max_epochs` was not set. Setting it to 1000 epochs. To train without an epoch limit, set `max_epochs=-1`.\n",
      "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/lightning/pytorch/trainer/configuration_validator.py:72: You passed in a `val_dataloader` but have no `validation_step`. Skipping val loop.\n",
      "Missing logger folder: /teamspace/studios/this_studio/lightning_logs\n",
      "\n",
      "  | Name    | Type       | Params\n",
      "---------------------------------------\n",
      "0 | encoder | Sequential | 100 K \n",
      "1 | decoder | Sequential | 101 K \n",
      "---------------------------------------\n",
      "202 K     Trainable params\n",
      "0         Non-trainable params\n",
      "202 K     Total params\n",
      "0.810     Total estimated model params size (MB)\n",
      "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=3` in the `DataLoader` to improve performance.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7f6f0415bd08481baea611ebbcc355e6",
       "version_major": 2,
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      "text/plain": [
       "Training: |          | 0/? [00:00<?, ?it/s]"
      ]
     },
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     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/zeus/miniconda3/envs/cloudspace/lib/python3.10/site-packages/lightning/pytorch/trainer/call.py:54: Detected KeyboardInterrupt, attempting graceful shutdown...\n"
     ]
    }
   ],
   "source": [
    "import torch, torch.nn as nn, torch.utils.data as data, torchvision as tv, torch.nn.functional as F\n",
    "import lightning as L\n",
    "\n",
    "class LitAutoEncoder(L.LightningModule):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        self.encoder = nn.Sequential(nn.Linear(28 * 28, 128), nn.ReLU(), nn.Linear(128, 3))\n",
    "        self.decoder = nn.Sequential(nn.Linear(3, 128), nn.ReLU(), nn.Linear(128, 28 * 28))\n",
    "\n",
    "    def forward(self, x):\n",
    "        embedding = self.encoder(x)\n",
    "        return embedding\n",
    "\n",
    "    def training_step(self, batch, batch_idx):\n",
    "        x, _ = batch\n",
    "        x = x.view(x.size(0), -1)\n",
    "        z = self.encoder(x)\n",
    "        x_hat = self.decoder(z)\n",
    "        loss = F.mse_loss(x_hat, x)\n",
    "        self.log(\"train_loss\", loss)\n",
    "        return loss\n",
    "\n",
    "    def configure_optimizers(self):\n",
    "        optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)\n",
    "        return optimizer\n",
    "\n",
    "dataset = tv.datasets.MNIST(\".\", download=True, transform=tv.transforms.ToTensor())\n",
    "train, val = data.random_split(dataset, [55000, 5000])\n",
    "\n",
    "autoencoder = LitAutoEncoder()\n",
    "trainer = L.Trainer()\n",
    "trainer.fit(autoencoder, data.DataLoader(train), data.DataLoader(val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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