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@@ -9,7 +9,7 @@ jobs: | |
| runs-on: ubuntu-22.04 | ||
| steps: | ||
| - name: Automatically update mergeable PRs | ||
| uses: adRise/[email protected] | ||
| uses: adRise/update-pr-branch@cd305ecbd76bf63056c9400ce2c725293fc3e0c0 # v0.7.0 | ||
| with: | ||
| token: ${{ secrets.FLWRMACHINE_TOKEN }} | ||
| base: 'main' | ||
|
|
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| [build-system] | ||
| requires = ["poetry-core>=1.4.0"] | ||
| build-backend = "poetry.core.masonry.api" | ||
|
|
||
| [tool.poetry] | ||
| name = "fds-e2e-pytorch" | ||
| version = "0.1.0" | ||
| description = "Flower Datasets with PyTorch" | ||
| authors = ["The Flower Authors <[email protected]>"] | ||
|
|
||
| [tool.poetry.dependencies] | ||
| python = "^3.8" | ||
| flwr-datasets = { path = "./../../", extras = ["vision"] } | ||
| torch = "^1.12.0" | ||
| torchvision = "^0.14.1" | ||
| parameterized = "==0.9.0" |
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| import unittest | ||
|
|
||
| import torch | ||
| import torch.nn as nn | ||
| import torch.nn.functional as F | ||
| import torch.optim as optim | ||
| from datasets.utils.logging import disable_progress_bar | ||
| from parameterized import parameterized_class, parameterized | ||
| from torch import Tensor | ||
| from torch.utils.data import DataLoader | ||
| from torchvision.transforms import Compose, ToTensor, Normalize | ||
|
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||
| from flwr_datasets import FederatedDataset | ||
|
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||
| class SimpleCNN(nn.Module): | ||
| def __init__(self): | ||
| super(SimpleCNN, self).__init__() | ||
| self.conv1 = nn.Conv2d(3, 6, 5) | ||
| self.pool = nn.MaxPool2d(2, 2) | ||
| self.conv2 = nn.Conv2d(6, 16, 5) | ||
| self.fc1 = nn.Linear(16 * 5 * 5, 120) | ||
| self.fc2 = nn.Linear(120, 84) | ||
| self.fc3 = nn.Linear(84, 10) | ||
|
|
||
| def forward(self, x): | ||
| x = self.pool(F.relu(self.conv1(x))) | ||
| x = self.pool(F.relu(self.conv2(x))) | ||
| x = x.view(-1, 16 * 5 * 5) | ||
| x = F.relu(self.fc1(x)) | ||
| x = F.relu(self.fc2(x)) | ||
| x = self.fc3(x) | ||
| return x | ||
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|
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| # Using parameterized testing, two different sets of parameters are specified: | ||
| # 1. CIFAR10 dataset with the simple ToTensor transform. | ||
| # 2. CIFAR10 dataset with a composed transform that first converts an image to a tensor | ||
| # and then normalizes it. | ||
| @parameterized_class( | ||
| [ | ||
| {"dataset_name": "cifar10", "test_split": "test", "transforms": ToTensor()}, | ||
| {"dataset_name": "cifar10", "test_split": "test", "transforms": Compose( | ||
| [ToTensor(), Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))] | ||
| )}, | ||
| ] | ||
| ) | ||
| class FdsToPyTorch(unittest.TestCase): | ||
| """Test the conversion from FDS to PyTorch Dataset and Dataloader.""" | ||
|
|
||
| dataset_name = "" | ||
| test_split = "" | ||
| transforms = None | ||
| trainloader = None | ||
| expected_img_shape_after_transform = [3, 32, 32] | ||
|
|
||
| @classmethod | ||
| def setUpClass(cls): | ||
| """Disable progress bar to keep the log clean. | ||
| """ | ||
| disable_progress_bar() | ||
|
|
||
| def _create_trainloader(self, batch_size: int) -> DataLoader: | ||
| """Create a trainloader from the federated dataset.""" | ||
| partition_id = 0 | ||
| fds = FederatedDataset(dataset=self.dataset_name, partitioners={"train": 100}) | ||
| partition = fds.load_partition(partition_id, "train") | ||
| partition_train_test = partition.train_test_split(test_size=0.2) | ||
| partition_train_test = partition_train_test.map( | ||
| lambda img: {"img": self.transforms(img)}, input_columns="img" | ||
| ) | ||
| trainloader = DataLoader( | ||
| partition_train_test["train"].with_format("torch"), batch_size=batch_size, | ||
| shuffle=True | ||
| ) | ||
| return trainloader | ||
|
|
||
| def test_create_partition_dataloader_with_transforms_shape(self) -> None: | ||
| """Test if the DataLoader returns batches with the expected shape.""" | ||
| batch_size = 16 | ||
| trainloader = self._create_trainloader(batch_size) | ||
| batch = next(iter(trainloader)) | ||
| images = batch["img"] | ||
| self.assertEqual(tuple(images.shape), | ||
| (batch_size, *self.expected_img_shape_after_transform)) | ||
|
|
||
| def test_create_partition_dataloader_with_transforms_batch_type(self) -> None: | ||
| """Test if the DataLoader returns batches of type dictionary.""" | ||
| batch_size = 16 | ||
| trainloader = self._create_trainloader(batch_size) | ||
| batch = next(iter(trainloader)) | ||
| self.assertIsInstance(batch, dict) | ||
|
|
||
| def test_create_partition_dataloader_with_transforms_data_type(self) -> None: | ||
| """Test to verify if the data in the DataLoader batches are of type Tensor.""" | ||
| batch_size = 16 | ||
| trainloader = self._create_trainloader(batch_size) | ||
| batch = next(iter(trainloader)) | ||
| images = batch["img"] | ||
| self.assertIsInstance(images, Tensor) | ||
|
|
||
| @parameterized.expand([ | ||
| ("not_nan", torch.isnan), | ||
| ("not_inf", torch.isinf), | ||
| ]) | ||
| def test_train_model_loss_value(self, name, condition_func): | ||
| """Test if the model trains and if the loss is a correct number.""" | ||
| trainloader = self._create_trainloader(16) | ||
| device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") | ||
|
|
||
| # Create the model, criterion, and optimizer | ||
| net = SimpleCNN().to(device) | ||
| criterion = nn.CrossEntropyLoss() | ||
| optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9) | ||
|
|
||
| # Training loop for one epoch | ||
| net.train() | ||
| loss = None | ||
| for i, data in enumerate(trainloader, 0): | ||
| inputs, labels = data['img'].to(device), data['label'].to(device) | ||
| optimizer.zero_grad() | ||
| outputs = net(inputs) | ||
| loss = criterion(outputs, labels) | ||
| loss.backward() | ||
| optimizer.step() | ||
|
|
||
| self.assertFalse(condition_func(loss).item()) | ||
|
|
||
|
|
||
| if __name__ == '__main__': | ||
| unittest.main() |
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| Original file line number | Diff line number | Diff line change |
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| How to build Docker Flower images locally | ||
| ========================================= | ||
|
|
||
| Flower provides pre-made docker images on `Docker Hub <https://hub.docker.com/r/flwr/server/tags>`_ | ||
| that include all necessary dependencies for running the server. You can also build your own custom | ||
| docker images from scratch with a different version of Python or Ubuntu if that is what you need. | ||
| In this guide, we will explain what images exist and how to build them locally. | ||
|
|
||
| Before we can start, we need to meet a few prerequisites in our local development environment. | ||
|
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||
| #. Clone the flower repository. | ||
|
|
||
| .. code-block:: bash | ||
| $ git clone https://github.com/adap/flower.git && cd flower | ||
| #. Verify the Docker daemon is running. | ||
|
|
||
| Please follow the first section on | ||
| `Run Flower using Docker <https://flower.dev/docs/framework/how-to-run-flower-using-docker>`_ | ||
| which covers this step in more detail. | ||
|
|
||
| Currently, Flower provides two images, a base image and a server image. There will also be a client | ||
| image soon. The base image, as the name suggests, contains basic dependencies that both the server | ||
| and the client need. This includes system dependencies, Python and Python tools. The server image is | ||
| based on the base image, but it additionally installs the Flower server using ``pip``. | ||
|
|
||
| The build instructions that assemble the images are located in the respective Dockerfiles. You | ||
| can find them in the subdirectories of ``src/docker``. | ||
|
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||
| Both, base and server image are configured via build arguments. Through build arguments, we can make | ||
| our build more flexible. For example, in the base image, we can specify the version of Python to | ||
| install using the ``PYTHON_VERSION`` build argument. Some of the build arguments have default | ||
| values, others must be specified when building the image. All available build arguments for each | ||
| image are listed in one of the tables below. | ||
|
|
||
| Building the base image | ||
| ----------------------- | ||
|
|
||
| .. list-table:: | ||
| :widths: 25 45 15 15 | ||
| :header-rows: 1 | ||
|
|
||
| * - Build argument | ||
| - Description | ||
| - Required | ||
| - Example | ||
| * - ``PYTHON_VERSION`` | ||
| - Version of ``python`` to be installed. | ||
| - Yes | ||
| - ``3.11`` | ||
| * - ``PIP_VERSION`` | ||
| - Version of ``pip`` to be installed. | ||
| - Yes | ||
| - ``23.0.1`` | ||
| * - ``SETUPTOOLS_VERSION`` | ||
| - Version of ``setuptools`` to be installed. | ||
| - Yes | ||
| - ``69.0.2`` | ||
| * - ``UBUNTU_VERSION`` | ||
| - Version of the official Ubuntu Docker image. | ||
| - Defaults to ``22.04``. | ||
| - | ||
|
|
||
| The following example creates a base image with Python 3.11.0, pip 23.0.1 and setuptools 69.0.2: | ||
|
|
||
| .. code-block:: bash | ||
| $ cd src/docker/base/ | ||
| $ docker build \ | ||
| --build-arg PYTHON_VERSION=3.11.0 \ | ||
| --build-arg PIP_VERSION=23.0.1 \ | ||
| --build-arg SETUPTOOLS_VERSION=69.0.2 \ | ||
| -t flwr_base:0.1.0 . | ||
| The name of image is ``flwr_base`` and the tag ``0.1.0``. Remember that the build arguments as well | ||
| as the name and tag can be adapted to your needs. These values serve as examples only. | ||
|
|
||
| Building the server image | ||
| ------------------------- | ||
|
|
||
| .. list-table:: | ||
| :widths: 25 45 15 15 | ||
| :header-rows: 1 | ||
|
|
||
| * - Build argument | ||
| - Description | ||
| - Required | ||
| - Example | ||
| * - ``BASE_REPOSITORY`` | ||
| - The repository name of the base image. | ||
| - Defaults to ``flwr/server``. | ||
| - | ||
| * - ``BASE_IMAGE_TAG`` | ||
| - The image tag of the base image. | ||
| - Defaults to ``py3.11-ubuntu22.04``. | ||
| - | ||
| * - ``FLWR_VERSION`` | ||
| - Version of Flower to be installed. | ||
| - Yes | ||
| - ``1.6.0`` | ||
|
|
||
| The following example creates a server image with the official Flower base image py3.11-ubuntu22.04 | ||
| and Flower 1.6.0: | ||
|
|
||
| .. code-block:: bash | ||
| $ cd src/docker/server/ | ||
| $ docker build \ | ||
| --build-arg BASE_IMAGE_TAG=py3.11-ubuntu22.04 \ | ||
| --build-arg FLWR_VERSION=1.6.0 \ | ||
| -t flwr_server:0.1.0 . | ||
| The name of image is ``flwr_server`` and the tag ``0.1.0``. Remember that the build arguments as well | ||
| as the name and tag can be adapted to your needs. These values serve as examples only. | ||
|
|
||
| If you want to use your own base image instead of the official Flower base image, all you need to do | ||
| is set the ``BASE_REPOSITORY`` and ``BASE_IMAGE_TAG`` build arguments. The value of | ||
| ``BASE_REPOSITORY`` must match the name of your image and the value of ``BASE_IMAGE_TAG`` must match | ||
| the tag of your image. | ||
|
|
||
| .. code-block:: bash | ||
| $ cd src/docker/server/ | ||
| $ docker build \ | ||
| --build-arg BASE_REPOSITORY=flwr_base \ | ||
| --build-arg BASE_IMAGE_TAG=0.1.0 \ | ||
| --build-arg FLWR_VERSION=1.6.0 \ | ||
| -t flwr_server:0.1.0 . | ||
| After creating the image, we can test whether the image is working: | ||
|
|
||
| .. code-block:: bash | ||
| $ docker run --rm flwr_server:0.1.0 --help |
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