pytorch_ipynb/mechanics/model-pipeline-vgg16.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.\n",
    "- Author: Sebastian Raschka\n",
    "- GitHub Repository: https://github.com/rasbt/deeplearning-models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Author: Sebastian Raschka\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.8.8\n",
      "IPython version      : 7.21.0\n",
      "\n",
      "torch: 1.8.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -a 'Sebastian Raschka' -v -p torch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Zoo -- Distribute a Model Across Multiple GPUs with Pipeline Parallelism (VGG-16 Example)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demos pipeline parallelism added to PyTorch 1.8 using VGG-16 as an example. For more details, see https://pytorch.org/docs/1.8.0/pipeline.html?highlight=pipeline#."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1) Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import torch\n",
    "\n",
    "sys.path.insert(0, \"..\") # to include ../helper_evaluate.py etc.\n",
    "\n",
    "from helper_utils import set_all_seeds, set_deterministic\n",
    "from helper_evaluate import compute_accuracy\n",
    "from helper_data import get_dataloaders_cifar10\n",
    "from helper_train import train_classifier_simple_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### SETTINGS\n",
    "##########################\n",
    "\n",
    "# Data settings\n",
    "num_classes = 10\n",
    "\n",
    "# Hyperparameters\n",
    "random_seed = 1\n",
    "learning_rate = 0.0001\n",
    "batch_size = 128\n",
    "num_epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "set_all_seeds(random_seed)\n",
    "#set_deterministic()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Files already downloaded and verified\n"
     ]
    }
   ],
   "source": [
    "##########################\n",
    "### Dataset\n",
    "##########################\n",
    "\n",
    "train_loader, valid_loader, test_loader = get_dataloaders_cifar10(\n",
    "    batch_size, \n",
    "    num_workers=2, \n",
    "    validation_fraction=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2) Regular (1-GPU) Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section implements the VGG-16 network in the conventional manne as a reference. The next section replicates this using pipeline parallelism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "##########################\n",
    "### Model\n",
    "##########################\n",
    "\n",
    "\n",
    "class VGG16(torch.nn.Module):\n",
    "\n",
    "    def __init__(self, num_classes):\n",
    "        super().__init__()\n",
    "        \n",
    "        # calculate same padding:\n",
    "        # (w - k + 2*p)/s + 1 = o\n",
    "        # => p = (s(o-1) - w + k)/2\n",
    "        \n",
    "        self.block_1 = torch.nn.Sequential(\n",
    "                torch.nn.Conv2d(in_channels=3,\n",
    "                          out_channels=64,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          # (1(32-1)- 32 + 3)/2 = 1\n",
    "                          padding=1), \n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.Conv2d(in_channels=64,\n",
    "                          out_channels=64,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                             stride=(2, 2))\n",
    "        )\n",
    "        \n",
    "        self.block_2 = torch.nn.Sequential(\n",
    "                torch.nn.Conv2d(in_channels=64,\n",
    "                          out_channels=128,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.Conv2d(in_channels=128,\n",
    "                          out_channels=128,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                             stride=(2, 2))\n",
    "        )\n",
    "        \n",
    "        self.block_3 = torch.nn.Sequential(        \n",
    "                torch.nn.Conv2d(in_channels=128,\n",
    "                          out_channels=256,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.Conv2d(in_channels=256,\n",
    "                          out_channels=256,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),        \n",
    "                torch.nn.Conv2d(in_channels=256,\n",
    "                          out_channels=256,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),\n",
    "                torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                             stride=(2, 2))\n",
    "        )\n",
    "        \n",
    "          \n",
    "        self.block_4 = torch.nn.Sequential(   \n",
    "                torch.nn.Conv2d(in_channels=256,\n",
    "                          out_channels=512,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),        \n",
    "                torch.nn.Conv2d(in_channels=512,\n",
    "                          out_channels=512,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),        \n",
    "                torch.nn.Conv2d(in_channels=512,\n",
    "                          out_channels=512,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),            \n",
    "                torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                             stride=(2, 2))\n",
    "        )\n",
    "        \n",
    "        self.block_5 = torch.nn.Sequential(\n",
    "                torch.nn.Conv2d(in_channels=512,\n",
    "                          out_channels=512,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),            \n",
    "                torch.nn.Conv2d(in_channels=512,\n",
    "                          out_channels=512,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),            \n",
    "                torch.nn.Conv2d(in_channels=512,\n",
    "                          out_channels=512,\n",
    "                          kernel_size=(3, 3),\n",
    "                          stride=(1, 1),\n",
    "                          padding=1),\n",
    "                torch.nn.ReLU(),    \n",
    "                torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                             stride=(2, 2))             \n",
    "        )\n",
    "            \n",
    "        self.classifier = torch.nn.Sequential(\n",
    "            torch.nn.Flatten(),\n",
    "            torch.nn.Linear(512, 4096),\n",
    "            torch.nn.ReLU(True),\n",
    "            #torch.nn.Dropout(p=0.5),\n",
    "            torch.nn.Linear(4096, 4096),\n",
    "            torch.nn.ReLU(True),\n",
    "            #torch.nn.Dropout(p=0.5),\n",
    "            torch.nn.Linear(4096, num_classes),\n",
    "        )\n",
    "        \n",
    "        \n",
    "    def forward(self, x):\n",
    "        x = self.block_1(x)\n",
    "        x = self.block_2(x)\n",
    "        x = self.block_3(x)\n",
    "        x = self.block_4(x)\n",
    "        x = self.block_5(x)\n",
    "        x = self.classifier(x) # logits\n",
    "\n",
    "        return x\n",
    "\n",
    "\n",
    "model = VGG16(num_classes=num_classes)\n",
    "\n",
    "device = torch.device('cuda:0')\n",
    "model = model.to(device)\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/050 | Batch 0000/0352 | Loss: 2.3034\n",
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      "***Epoch: 001/050 | Train. Acc.: 24.324% | Loss: 1.883\n",
      "***Epoch: 001/050 | Valid. Acc.: 24.480% | Loss: 1.873\n",
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      "***Epoch: 002/050 | Train. Acc.: 33.400% | Loss: 1.765\n",
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      "***Epoch: 003/050 | Train. Acc.: 41.444% | Loss: 1.540\n",
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      "***Epoch: 004/050 | Train. Acc.: 48.211% | Loss: 1.400\n",
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      "***Epoch: 005/050 | Train. Acc.: 55.909% | Loss: 1.202\n",
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      "***Epoch: 006/050 | Train. Acc.: 62.300% | Loss: 1.034\n",
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      "***Epoch: 007/050 | Train. Acc.: 67.273% | Loss: 0.909\n",
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      "***Epoch: 008/050 | Train. Acc.: 69.647% | Loss: 0.844\n",
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      "***Epoch: 009/050 | Train. Acc.: 71.738% | Loss: 0.787\n",
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      "***Epoch: 010/050 | Train. Acc.: 73.676% | Loss: 0.734\n",
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      "***Epoch: 015/050 | Train. Acc.: 80.913% | Loss: 0.556\n",
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      "***Epoch: 021/050 | Train. Acc.: 88.662% | Loss: 0.340\n",
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      "***Epoch: 022/050 | Train. Acc.: 90.007% | Loss: 0.310\n",
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      "***Epoch: 023/050 | Train. Acc.: 91.322% | Loss: 0.261\n",
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      "***Epoch: 024/050 | Train. Acc.: 91.958% | Loss: 0.241\n",
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      "***Epoch: 025/050 | Train. Acc.: 89.602% | Loss: 0.304\n",
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      "***Epoch: 027/050 | Train. Acc.: 93.753% | Loss: 0.196\n",
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      "***Epoch: 028/050 | Train. Acc.: 94.836% | Loss: 0.152\n",
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      "***Epoch: 029/050 | Train. Acc.: 94.131% | Loss: 0.181\n",
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      "***Epoch: 030/050 | Train. Acc.: 96.044% | Loss: 0.126\n",
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      "***Epoch: 031/050 | Train. Acc.: 96.498% | Loss: 0.112\n",
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      "***Epoch: 032/050 | Train. Acc.: 96.169% | Loss: 0.121\n",
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      "***Epoch: 033/050 | Train. Acc.: 95.909% | Loss: 0.136\n",
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      "***Epoch: 034/050 | Train. Acc.: 96.098% | Loss: 0.124\n",
      "***Epoch: 034/050 | Valid. Acc.: 75.720% | Loss: 1.318\n",
      "Time elapsed: 18.47 min\n",
      "Epoch: 035/050 | Batch 0000/0352 | Loss: 0.0288\n",
      "Epoch: 035/050 | Batch 0200/0352 | Loss: 0.0558\n",
      "***Epoch: 035/050 | Train. Acc.: 95.222% | Loss: 0.162\n",
      "***Epoch: 035/050 | Valid. Acc.: 75.160% | Loss: 1.388\n",
      "Time elapsed: 19.01 min\n",
      "Epoch: 036/050 | Batch 0000/0352 | Loss: 0.0136\n",
      "Epoch: 036/050 | Batch 0200/0352 | Loss: 0.0345\n",
      "***Epoch: 036/050 | Train. Acc.: 95.422% | Loss: 0.151\n",
      "***Epoch: 036/050 | Valid. Acc.: 74.820% | Loss: 1.354\n",
      "Time elapsed: 19.55 min\n",
      "Epoch: 037/050 | Batch 0000/0352 | Loss: 0.0644\n",
      "Epoch: 037/050 | Batch 0200/0352 | Loss: 0.0265\n",
      "***Epoch: 037/050 | Train. Acc.: 94.347% | Loss: 0.187\n",
      "***Epoch: 037/050 | Valid. Acc.: 74.560% | Loss: 1.423\n",
      "Time elapsed: 20.10 min\n",
      "Epoch: 038/050 | Batch 0000/0352 | Loss: 0.0368\n",
      "Epoch: 038/050 | Batch 0200/0352 | Loss: 0.0925\n",
      "***Epoch: 038/050 | Train. Acc.: 95.807% | Loss: 0.135\n",
      "***Epoch: 038/050 | Valid. Acc.: 75.380% | Loss: 1.365\n",
      "Time elapsed: 20.64 min\n",
      "Epoch: 039/050 | Batch 0000/0352 | Loss: 0.0510\n",
      "Epoch: 039/050 | Batch 0200/0352 | Loss: 0.2865\n",
      "***Epoch: 039/050 | Train. Acc.: 96.160% | Loss: 0.125\n",
      "***Epoch: 039/050 | Valid. Acc.: 75.940% | Loss: 1.406\n",
      "Time elapsed: 21.18 min\n",
      "Epoch: 040/050 | Batch 0000/0352 | Loss: 0.1398\n",
      "Epoch: 040/050 | Batch 0200/0352 | Loss: 0.0894\n",
      "***Epoch: 040/050 | Train. Acc.: 98.256% | Loss: 0.056\n",
      "***Epoch: 040/050 | Valid. Acc.: 77.300% | Loss: 1.251\n",
      "Time elapsed: 21.73 min\n",
      "Epoch: 041/050 | Batch 0000/0352 | Loss: 0.0201\n",
      "Epoch: 041/050 | Batch 0200/0352 | Loss: 0.0795\n",
      "***Epoch: 041/050 | Train. Acc.: 97.827% | Loss: 0.071\n",
      "***Epoch: 041/050 | Valid. Acc.: 77.000% | Loss: 1.379\n",
      "Time elapsed: 22.27 min\n",
      "Epoch: 042/050 | Batch 0000/0352 | Loss: 0.0309\n",
      "Epoch: 042/050 | Batch 0200/0352 | Loss: 0.0177\n",
      "***Epoch: 042/050 | Train. Acc.: 98.500% | Loss: 0.049\n",
      "***Epoch: 042/050 | Valid. Acc.: 77.280% | Loss: 1.315\n",
      "Time elapsed: 22.81 min\n",
      "Epoch: 043/050 | Batch 0000/0352 | Loss: 0.0442\n",
      "Epoch: 043/050 | Batch 0200/0352 | Loss: 0.0547\n",
      "***Epoch: 043/050 | Train. Acc.: 98.369% | Loss: 0.051\n",
      "***Epoch: 043/050 | Valid. Acc.: 77.680% | Loss: 1.303\n",
      "Time elapsed: 23.36 min\n",
      "Epoch: 044/050 | Batch 0000/0352 | Loss: 0.0294\n",
      "Epoch: 044/050 | Batch 0200/0352 | Loss: 0.0188\n",
      "***Epoch: 044/050 | Train. Acc.: 97.716% | Loss: 0.073\n",
      "***Epoch: 044/050 | Valid. Acc.: 77.100% | Loss: 1.381\n",
      "Time elapsed: 23.90 min\n",
      "Epoch: 045/050 | Batch 0000/0352 | Loss: 0.0226\n",
      "Epoch: 045/050 | Batch 0200/0352 | Loss: 0.0128\n",
      "***Epoch: 045/050 | Train. Acc.: 98.140% | Loss: 0.059\n",
      "***Epoch: 045/050 | Valid. Acc.: 76.420% | Loss: 1.395\n",
      "Time elapsed: 24.45 min\n",
      "Epoch: 046/050 | Batch 0000/0352 | Loss: 0.0170\n",
      "Epoch: 046/050 | Batch 0200/0352 | Loss: 0.0198\n",
      "***Epoch: 046/050 | Train. Acc.: 98.527% | Loss: 0.047\n",
      "***Epoch: 046/050 | Valid. Acc.: 77.000% | Loss: 1.548\n",
      "Time elapsed: 24.99 min\n",
      "Epoch: 047/050 | Batch 0000/0352 | Loss: 0.0011\n",
      "Epoch: 047/050 | Batch 0200/0352 | Loss: 0.0362\n",
      "***Epoch: 047/050 | Train. Acc.: 98.682% | Loss: 0.041\n",
      "***Epoch: 047/050 | Valid. Acc.: 77.260% | Loss: 1.345\n",
      "Time elapsed: 25.54 min\n",
      "Epoch: 048/050 | Batch 0000/0352 | Loss: 0.0180\n",
      "Epoch: 048/050 | Batch 0200/0352 | Loss: 0.0191\n",
      "***Epoch: 048/050 | Train. Acc.: 98.838% | Loss: 0.036\n",
      "***Epoch: 048/050 | Valid. Acc.: 77.300% | Loss: 1.412\n",
      "Time elapsed: 26.08 min\n",
      "Epoch: 049/050 | Batch 0000/0352 | Loss: 0.0114\n",
      "Epoch: 049/050 | Batch 0200/0352 | Loss: 0.0217\n",
      "***Epoch: 049/050 | Train. Acc.: 98.378% | Loss: 0.054\n",
      "***Epoch: 049/050 | Valid. Acc.: 77.460% | Loss: 1.459\n",
      "Time elapsed: 26.63 min\n",
      "Epoch: 050/050 | Batch 0000/0352 | Loss: 0.0044\n",
      "Epoch: 050/050 | Batch 0200/0352 | Loss: 0.0069\n",
      "***Epoch: 050/050 | Train. Acc.: 98.804% | Loss: 0.036\n",
      "***Epoch: 050/050 | Valid. Acc.: 77.700% | Loss: 1.365\n",
      "Time elapsed: 27.17 min\n",
      "Total Training Time: 27.17 min\n"
     ]
    }
   ],
   "source": [
    "_ = train_classifier_simple_v1(num_epochs=num_epochs, model=model, \n",
    "                               optimizer=optimizer, device=device, \n",
    "                               train_loader=train_loader, valid_loader=valid_loader, \n",
    "                               logging_interval=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3) VGG16 with Pipeline Parallelism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we first define the blocks we are going to wrap into the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "block_1 = torch.nn.Sequential(\n",
    "        torch.nn.Conv2d(in_channels=3,\n",
    "                  out_channels=64,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  # (1(32-1)- 32 + 3)/2 = 1\n",
    "                  padding=1), \n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Conv2d(in_channels=64,\n",
    "                  out_channels=64,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                     stride=(2, 2))\n",
    ")\n",
    "\n",
    "block_2 = torch.nn.Sequential(\n",
    "        torch.nn.Conv2d(in_channels=64,\n",
    "                  out_channels=128,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Conv2d(in_channels=128,\n",
    "                  out_channels=128,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                     stride=(2, 2))\n",
    ")\n",
    "        \n",
    "block_3 = torch.nn.Sequential(        \n",
    "        torch.nn.Conv2d(in_channels=128,\n",
    "                  out_channels=256,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.Conv2d(in_channels=256,\n",
    "                  out_channels=256,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),        \n",
    "        torch.nn.Conv2d(in_channels=256,\n",
    "                  out_channels=256,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),\n",
    "        torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                     stride=(2, 2))\n",
    ")\n",
    "        \n",
    "          \n",
    "block_4 = torch.nn.Sequential(   \n",
    "        torch.nn.Conv2d(in_channels=256,\n",
    "                  out_channels=512,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),        \n",
    "        torch.nn.Conv2d(in_channels=512,\n",
    "                  out_channels=512,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),        \n",
    "        torch.nn.Conv2d(in_channels=512,\n",
    "                  out_channels=512,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),            \n",
    "        torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                     stride=(2, 2))\n",
    ")\n",
    "        \n",
    "block_5 = torch.nn.Sequential(\n",
    "        torch.nn.Conv2d(in_channels=512,\n",
    "                  out_channels=512,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),            \n",
    "        torch.nn.Conv2d(in_channels=512,\n",
    "                  out_channels=512,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),            \n",
    "        torch.nn.Conv2d(in_channels=512,\n",
    "                  out_channels=512,\n",
    "                  kernel_size=(3, 3),\n",
    "                  stride=(1, 1),\n",
    "                  padding=1),\n",
    "        torch.nn.ReLU(),    \n",
    "        torch.nn.MaxPool2d(kernel_size=(2, 2),\n",
    "                     stride=(2, 2))             \n",
    ")\n",
    "            \n",
    "classifier = torch.nn.Sequential(\n",
    "    torch.nn.Flatten(),\n",
    "    torch.nn.Linear(512, 4096),\n",
    "    torch.nn.ReLU(True),\n",
    "    #torch.nn.Dropout(p=0.5),\n",
    "    torch.nn.Linear(4096, 4096),\n",
    "    torch.nn.ReLU(True),\n",
    "    #torch.nn.Dropout(p=0.5),\n",
    "    torch.nn.Linear(4096, num_classes),\n",
    ")\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before setting up the environment for the distributed run, we check if the distributed setting is supported on our machine. The following should return `True`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.distributed.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we set the following environment variables for your machine:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For `MASTER_ADDR` just use the IP address of your machine. E.g., 123.45.67.89"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%env MASTER_ADDR=xxx.xx.xx.xx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Choose a free port:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: MASTER_PORT=8891\n"
     ]
    }
   ],
   "source": [
    "%env MASTER_PORT=8891"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up the RPC if it is not already running (more details at https://pytorch.org/docs/stable/rpc.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    torch.distributed.rpc.init_rpc(name='node1', rank=0, world_size=1)\n",
    "except RuntimeError as e:\n",
    "    if str(e) == 'Address already in use':\n",
    "        pass\n",
    "    else:\n",
    "        raise RuntimeError(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the main part for running the model on multiple GPUs.\n",
    "\n",
    "1. We wrap the individual blocks into a `Sequential` model\n",
    "2. The chunks refer to the `microbatches`, for more details, see https://pytorch.org/docs/1.8.0/pipeline.html?highlight=pipeline#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.distributed.pipeline.sync import Pipe\n",
    "\n",
    "\n",
    "block1 = block_1.cuda(0)\n",
    "block2 = block_2.cuda(0)\n",
    "block3 = block_3.cuda(2)\n",
    "block4 = block_4.cuda(2)\n",
    "block4 = block_5.cuda(3)\n",
    "block4 = classifier.cuda(0)\n",
    "\n",
    "model_parallel = torch.nn.Sequential(\n",
    "    block_1, block_2, block_3, block_4, block_5, classifier)\n",
    "model_parallel = Pipe(model_parallel, chunks=8)\n",
    "optimizer = torch.optim.Adam(model_parallel.parameters(), lr=learning_rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 001/050 | Batch 0000/0352 | Loss: 2.3043\n",
      "Epoch: 001/050 | Batch 0200/0352 | Loss: 2.0182\n",
      "***Epoch: 001/050 | Train. Acc.: 21.711% | Loss: 1.914\n",
      "***Epoch: 001/050 | Valid. Acc.: 21.420% | Loss: 1.906\n",
      "Time elapsed: 1.35 min\n",
      "Epoch: 002/050 | Batch 0000/0352 | Loss: 1.9691\n",
      "Epoch: 002/050 | Batch 0200/0352 | Loss: 1.7432\n",
      "***Epoch: 002/050 | Train. Acc.: 28.180% | Loss: 1.881\n",
      "***Epoch: 002/050 | Valid. Acc.: 28.580% | Loss: 1.895\n",
      "Time elapsed: 2.68 min\n",
      "Epoch: 003/050 | Batch 0000/0352 | Loss: 1.8837\n",
      "Epoch: 003/050 | Batch 0200/0352 | Loss: 1.5357\n",
      "***Epoch: 003/050 | Train. Acc.: 38.922% | Loss: 1.584\n",
      "***Epoch: 003/050 | Valid. Acc.: 38.960% | Loss: 1.587\n",
      "Time elapsed: 4.02 min\n",
      "Epoch: 004/050 | Batch 0000/0352 | Loss: 1.6131\n",
      "Epoch: 004/050 | Batch 0200/0352 | Loss: 1.4481\n",
      "***Epoch: 004/050 | Train. Acc.: 48.900% | Loss: 1.360\n",
      "***Epoch: 004/050 | Valid. Acc.: 49.240% | Loss: 1.367\n",
      "Time elapsed: 5.36 min\n",
      "Epoch: 005/050 | Batch 0000/0352 | Loss: 1.3710\n",
      "Epoch: 005/050 | Batch 0200/0352 | Loss: 1.2681\n",
      "***Epoch: 005/050 | Train. Acc.: 55.542% | Loss: 1.225\n",
      "***Epoch: 005/050 | Valid. Acc.: 55.100% | Loss: 1.246\n",
      "Time elapsed: 6.70 min\n",
      "Epoch: 006/050 | Batch 0000/0352 | Loss: 1.1851\n",
      "Epoch: 006/050 | Batch 0200/0352 | Loss: 1.2468\n",
      "***Epoch: 006/050 | Train. Acc.: 60.782% | Loss: 1.082\n",
      "***Epoch: 006/050 | Valid. Acc.: 58.540% | Loss: 1.123\n",
      "Time elapsed: 8.04 min\n",
      "Epoch: 007/050 | Batch 0000/0352 | Loss: 1.0370\n",
      "Epoch: 007/050 | Batch 0200/0352 | Loss: 1.1411\n",
      "***Epoch: 007/050 | Train. Acc.: 65.060% | Loss: 0.969\n",
      "***Epoch: 007/050 | Valid. Acc.: 63.240% | Loss: 1.023\n",
      "Time elapsed: 9.38 min\n",
      "Epoch: 008/050 | Batch 0000/0352 | Loss: 0.9135\n",
      "Epoch: 008/050 | Batch 0200/0352 | Loss: 1.0559\n",
      "***Epoch: 008/050 | Train. Acc.: 68.411% | Loss: 0.876\n",
      "***Epoch: 008/050 | Valid. Acc.: 66.260% | Loss: 0.946\n",
      "Time elapsed: 10.73 min\n",
      "Epoch: 009/050 | Batch 0000/0352 | Loss: 0.8361\n",
      "Epoch: 009/050 | Batch 0200/0352 | Loss: 1.0417\n",
      "***Epoch: 009/050 | Train. Acc.: 71.067% | Loss: 0.815\n",
      "***Epoch: 009/050 | Valid. Acc.: 67.320% | Loss: 0.908\n",
      "Time elapsed: 12.06 min\n",
      "Epoch: 010/050 | Batch 0000/0352 | Loss: 0.7411\n",
      "Epoch: 010/050 | Batch 0200/0352 | Loss: 0.9193\n",
      "***Epoch: 010/050 | Train. Acc.: 74.264% | Loss: 0.729\n",
      "***Epoch: 010/050 | Valid. Acc.: 70.560% | Loss: 0.847\n",
      "Time elapsed: 13.39 min\n",
      "Epoch: 011/050 | Batch 0000/0352 | Loss: 0.6631\n",
      "Epoch: 011/050 | Batch 0200/0352 | Loss: 0.7807\n",
      "***Epoch: 011/050 | Train. Acc.: 75.558% | Loss: 0.685\n",
      "***Epoch: 011/050 | Valid. Acc.: 71.380% | Loss: 0.840\n",
      "Time elapsed: 14.74 min\n",
      "Epoch: 012/050 | Batch 0000/0352 | Loss: 0.6035\n",
      "Epoch: 012/050 | Batch 0200/0352 | Loss: 0.7203\n",
      "***Epoch: 012/050 | Train. Acc.: 76.527% | Loss: 0.658\n",
      "***Epoch: 012/050 | Valid. Acc.: 71.760% | Loss: 0.856\n",
      "Time elapsed: 16.08 min\n",
      "Epoch: 013/050 | Batch 0000/0352 | Loss: 0.5782\n",
      "Epoch: 013/050 | Batch 0200/0352 | Loss: 0.6597\n",
      "***Epoch: 013/050 | Train. Acc.: 77.604% | Loss: 0.629\n",
      "***Epoch: 013/050 | Valid. Acc.: 71.920% | Loss: 0.859\n",
      "Time elapsed: 17.42 min\n",
      "Epoch: 014/050 | Batch 0000/0352 | Loss: 0.5054\n",
      "Epoch: 014/050 | Batch 0200/0352 | Loss: 0.6732\n",
      "***Epoch: 014/050 | Train. Acc.: 77.451% | Loss: 0.652\n",
      "***Epoch: 014/050 | Valid. Acc.: 71.200% | Loss: 0.883\n",
      "Time elapsed: 18.78 min\n",
      "Epoch: 015/050 | Batch 0000/0352 | Loss: 0.4921\n",
      "Epoch: 015/050 | Batch 0200/0352 | Loss: 0.5878\n",
      "***Epoch: 015/050 | Train. Acc.: 78.100% | Loss: 0.630\n",
      "***Epoch: 015/050 | Valid. Acc.: 71.240% | Loss: 0.907\n",
      "Time elapsed: 20.12 min\n",
      "Epoch: 016/050 | Batch 0000/0352 | Loss: 0.5568\n",
      "Epoch: 016/050 | Batch 0200/0352 | Loss: 0.5079\n",
      "***Epoch: 016/050 | Train. Acc.: 80.038% | Loss: 0.579\n",
      "***Epoch: 016/050 | Valid. Acc.: 71.440% | Loss: 0.927\n",
      "Time elapsed: 21.48 min\n",
      "Epoch: 017/050 | Batch 0000/0352 | Loss: 0.4743\n",
      "Epoch: 017/050 | Batch 0200/0352 | Loss: 0.5260\n",
      "***Epoch: 017/050 | Train. Acc.: 80.993% | Loss: 0.587\n",
      "***Epoch: 017/050 | Valid. Acc.: 71.620% | Loss: 0.972\n",
      "Time elapsed: 22.84 min\n",
      "Epoch: 018/050 | Batch 0000/0352 | Loss: 0.4304\n",
      "Epoch: 018/050 | Batch 0200/0352 | Loss: 0.4014\n",
      "***Epoch: 018/050 | Train. Acc.: 79.338% | Loss: 0.653\n",
      "***Epoch: 018/050 | Valid. Acc.: 69.800% | Loss: 1.060\n",
      "Time elapsed: 24.18 min\n",
      "Epoch: 019/050 | Batch 0000/0352 | Loss: 0.3735\n",
      "Epoch: 019/050 | Batch 0200/0352 | Loss: 0.3300\n",
      "***Epoch: 019/050 | Train. Acc.: 83.069% | Loss: 0.518\n",
      "***Epoch: 019/050 | Valid. Acc.: 73.120% | Loss: 0.991\n",
      "Time elapsed: 25.54 min\n",
      "Epoch: 020/050 | Batch 0000/0352 | Loss: 0.3183\n",
      "Epoch: 020/050 | Batch 0200/0352 | Loss: 0.4193\n",
      "***Epoch: 020/050 | Train. Acc.: 86.129% | Loss: 0.417\n",
      "***Epoch: 020/050 | Valid. Acc.: 74.960% | Loss: 0.961\n",
      "Time elapsed: 26.90 min\n",
      "Epoch: 021/050 | Batch 0000/0352 | Loss: 0.1804\n",
      "Epoch: 021/050 | Batch 0200/0352 | Loss: 0.4772\n",
      "***Epoch: 021/050 | Train. Acc.: 84.840% | Loss: 0.465\n",
      "***Epoch: 021/050 | Valid. Acc.: 73.140% | Loss: 1.030\n",
      "Time elapsed: 28.23 min\n",
      "Epoch: 022/050 | Batch 0000/0352 | Loss: 0.1948\n",
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      "***Epoch: 022/050 | Train. Acc.: 84.476% | Loss: 0.483\n",
      "***Epoch: 022/050 | Valid. Acc.: 73.260% | Loss: 1.068\n",
      "Time elapsed: 29.55 min\n",
      "Epoch: 023/050 | Batch 0000/0352 | Loss: 0.2390\n",
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      "***Epoch: 023/050 | Train. Acc.: 87.033% | Loss: 0.407\n",
      "***Epoch: 023/050 | Valid. Acc.: 74.260% | Loss: 1.054\n",
      "Time elapsed: 30.90 min\n",
      "Epoch: 024/050 | Batch 0000/0352 | Loss: 0.2224\n",
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      "***Epoch: 024/050 | Train. Acc.: 88.540% | Loss: 0.345\n",
      "***Epoch: 024/050 | Valid. Acc.: 74.440% | Loss: 0.996\n",
      "Time elapsed: 32.25 min\n",
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      "***Epoch: 025/050 | Train. Acc.: 90.258% | Loss: 0.292\n",
      "***Epoch: 025/050 | Valid. Acc.: 75.200% | Loss: 1.034\n",
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      "***Epoch: 026/050 | Train. Acc.: 89.593% | Loss: 0.324\n",
      "***Epoch: 026/050 | Valid. Acc.: 74.920% | Loss: 1.139\n",
      "Time elapsed: 34.94 min\n",
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      "***Epoch: 027/050 | Train. Acc.: 90.831% | Loss: 0.287\n",
      "***Epoch: 027/050 | Valid. Acc.: 75.440% | Loss: 1.242\n",
      "Time elapsed: 36.29 min\n",
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      "***Epoch: 028/050 | Train. Acc.: 90.716% | Loss: 0.304\n",
      "***Epoch: 028/050 | Valid. Acc.: 74.680% | Loss: 1.334\n",
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      "***Epoch: 029/050 | Train. Acc.: 90.467% | Loss: 0.326\n",
      "***Epoch: 029/050 | Valid. Acc.: 73.800% | Loss: 1.357\n",
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      "***Epoch: 030/050 | Train. Acc.: 94.009% | Loss: 0.188\n",
      "***Epoch: 030/050 | Valid. Acc.: 75.640% | Loss: 1.134\n",
      "Time elapsed: 40.31 min\n",
      "Epoch: 031/050 | Batch 0000/0352 | Loss: 0.0434\n",
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      "***Epoch: 031/050 | Train. Acc.: 94.442% | Loss: 0.172\n",
      "***Epoch: 031/050 | Valid. Acc.: 76.360% | Loss: 1.172\n",
      "Time elapsed: 41.63 min\n",
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      "***Epoch: 032/050 | Train. Acc.: 94.224% | Loss: 0.182\n",
      "***Epoch: 032/050 | Valid. Acc.: 76.940% | Loss: 1.169\n",
      "Time elapsed: 42.96 min\n",
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      "***Epoch: 033/050 | Train. Acc.: 94.258% | Loss: 0.177\n",
      "***Epoch: 033/050 | Valid. Acc.: 76.440% | Loss: 1.189\n",
      "Time elapsed: 44.31 min\n",
      "Epoch: 034/050 | Batch 0000/0352 | Loss: 0.0264\n",
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      "***Epoch: 034/050 | Train. Acc.: 94.207% | Loss: 0.186\n",
      "***Epoch: 034/050 | Valid. Acc.: 76.520% | Loss: 1.160\n",
      "Time elapsed: 45.66 min\n",
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      "***Epoch: 035/050 | Train. Acc.: 94.469% | Loss: 0.187\n",
      "***Epoch: 035/050 | Valid. Acc.: 76.440% | Loss: 1.249\n",
      "Time elapsed: 47.01 min\n",
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      "***Epoch: 036/050 | Train. Acc.: 96.549% | Loss: 0.105\n",
      "***Epoch: 036/050 | Valid. Acc.: 77.480% | Loss: 1.154\n",
      "Time elapsed: 48.35 min\n",
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      "Epoch: 037/050 | Batch 0200/0352 | Loss: 0.0867\n"
     ]
    }
   ],
   "source": [
    "_ = train_classifier_simple_v1(num_epochs=num_epochs, model=model_parallel, \n",
    "                               optimizer=optimizer, device=torch.device('cuda:0'), \n",
    "                               train_loader=train_loader, valid_loader=valid_loader, \n",
    "                               logging_interval=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, the Training is slower as before. But this is expected because the main selling point of pipeline parallelism is to utilize more GPUs due to memory contraints not to speed up training."
   ]
  }
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