federated-learning-using-ignite.ipynb

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  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install pytorch-ignite\n",
        "!pip install syft==0.2.9"
      ],
      "metadata": {
        "id": "SJAprJGpYp5m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch.optim as optim\n",
        "from torchvision import datasets, transforms\n",
        "\n",
        "from ignite.contrib.handlers import ProgressBar\n",
        "from ignite.engine import Engine\n",
        "from ignite.metrics import Accuracy\n",
        "from ignite.engine import Events\n",
        "\n",
        "import syft as sy "
      ],
      "metadata": {
        "id": "AjguMBOm0lL_"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hook = sy.TorchHook(torch)  # attach the pytorch hook\n",
        "joe = sy.VirtualWorker(hook, id=\"joe\")  #  remote worker joe\n",
        "jane = sy.VirtualWorker(hook, id=\"jane\")  #  remote worker  jane"
      ],
      "metadata": {
        "id": "oQ9EUzOzNZxG"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "transform = transforms.Compose([\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize((0.5, ), (0.5, )),\n",
        "])\n",
        "\n",
        "train_set = datasets.MNIST(\n",
        "    \"~/.pytorch/MNIST_data/\", train=True, download=True, transform=transform)\n",
        "test_set = datasets.MNIST(\n",
        "    \"~/.pytorch/MNIST_data/\", train=False, download=True, transform=transform)\n",
        "\n",
        "federated_train_loader = sy.FederatedDataLoader(\n",
        "    train_set.federate((joe, jane)), batch_size=256, shuffle=True) # the federate() method splits the data within the workers\n",
        "\n",
        "test_loader = torch.utils.data.DataLoader(\n",
        "    test_set, batch_size=256, shuffle=True)"
      ],
      "metadata": {
        "id": "modlb-p8axmo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class Net(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Net, self).__init__()\n",
        "        self.fc1 = nn.Linear(784, 500)\n",
        "        self.fc2 = nn.Linear(500, 10)\n",
        "\n",
        "    def forward(self, x):\n",
        "        x = x.view(-1, 784)\n",
        "        x = self.fc1(x)\n",
        "        x = F.relu(x)\n",
        "        x = self.fc2(x)\n",
        "        return F.log_softmax(x, dim=1)\n",
        "        \n",
        "model = Net()\n",
        "print(model)\n",
        "optimizer = optim.SGD(model.parameters(), lr=0.01)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lpk-a2Sfaxki",
        "outputId": "86943fdc-4b28-4429-ea5b-14158ed63185"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Net(\n",
            "  (fc1): Linear(in_features=784, out_features=500, bias=True)\n",
            "  (fc2): Linear(in_features=500, out_features=10, bias=True)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train_step(engine, batch):\n",
        "    data, targets = batch\n",
        "    model.send(data.location) # send the model to the client device where the data is present\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    outputs = model(data)\n",
        "    loss = F.nll_loss(outputs, targets)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    model.get() # get back the improved model\n",
        "    return loss.get()\n",
        "\n",
        "trainer = Engine(train_step)\n",
        "ProgressBar().attach(trainer)"
      ],
      "metadata": {
        "id": "MYEx6JoTfwvf"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def validation_step(engine, batch):\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        x, y = batch\n",
        "        y_pred = model(x)\n",
        "\n",
        "    return y_pred, y\n",
        "    \n",
        "evaluator = Engine(validation_step)\n",
        "Accuracy().attach(evaluator, \"accuracy\")"
      ],
      "metadata": {
        "id": "Rskwn06AgRrJ"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "validate_every = 1\n",
        "log_every=1\n",
        "\n",
        "@trainer.on(Events.EPOCH_COMPLETED(every=validate_every))\n",
        "def run_validation():\n",
        "    evaluator.run(test_loader)\n",
        "\n",
        "@trainer.on(Events.EPOCH_COMPLETED(every=log_every))\n",
        "def log_validation():\n",
        "    metrics = evaluator.state.metrics\n",
        "    # print(metrics)\n",
        "    print(f\"Epoch: {trainer.state.epoch},  Accuracy: {metrics['accuracy']}\")"
      ],
      "metadata": {
        "id": "1jhj2Q6dgbau"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.run(federated_train_loader, max_epochs=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 242,
          "referenced_widgets": [
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          ]
        },
        "id": "Zl6OiEpsgcDr",
        "outputId": "4e26241e-327f-41c9-a618-6fdacef38788"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1/235]   0%|           [00:00<?]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fec4e9d47e464beb8ede129785bf2121"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 1,  Accuracy: 0.844\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "[1/235]   0%|           [00:00<?]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 2,  Accuracy: 0.8752\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "State:\n",
              "\titeration: 470\n",
              "\tepoch: 2\n",
              "\tepoch_length: 235\n",
              "\tmax_epochs: 2\n",
              "\toutput: <class 'torch.Tensor'>\n",
              "\tbatch: <class 'tuple'>\n",
              "\tmetrics: <class 'dict'>\n",
              "\tdataloader: <class 'syft.frameworks.torch.fl.dataloader.FederatedDataLoader'>\n",
              "\tseed: <class 'NoneType'>\n",
              "\ttimes: <class 'dict'>"
            ]
          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "bAPS7i25iWpP"
      },
      "execution_count": 9,
      "outputs": []
    }
  ]
}