.. _projects-label: ================================================ Develop a FEDn project ================================================ This guide explains how a FEDn project is structured, and details how to develop your own project. We assume knowledge of how to run a federated learning project with FEDn, corresponding to the tutorial: :ref:`quickstart-label`. Overview ========== A FEDn project is a convention for packaging/wrapping machine learning code to be used for federated learning with FEDn. At the core, a project is a directory of files (often a Git repository), containing your machine learning code, FEDn entry points, and a specification of the runtime environment for the client (python environment or a Docker image). The FEDn API and command-line tools provide functionality to help a user automate deployment and management of a project that follows the conventions. The structure of a FEDn project ================================ We recommend that projects have the following folder and file structure, here illustrated by the 'mnist-pytorch' example from the Getting Started Guide: | project | ├ client | │ ├ fedn.yaml | │ ├ python_env.yaml | │ ├ model.py | │ ├ data.py | │ ├ train.py | │ ├ validate.py | | └ .ignore | ├ data | │ └ mnist.npz | ├ README.md | ├ scripts / notebooks | └ Dockerfile | The content of the ``client`` folder is what we commonly refer to as the *compute package*. It contains modules and files specifying the logic of a single client. The file ``fedn.yaml`` is the FEDn Project File. It is used by FEDn to get information about the specific commands to run when building the initial 'seed model', and when a client recieves a training request or a validation request from the server. These commmands are referred to as the ``entry points``. The compute package (client folder) ==================================== **The Project File (fedn.yaml)** FEDn uses a project file 'fedn.yaml' to specify which entry points to execute when the client recieves a training or validation request, and (optionally) what runtime environment to execute those entry points in. There are up to four entry points: - **build** - used for any kind of setup that needs to be done before the client starts up, such as initializing the global seed model. - **startup** - invoked immediately after the client starts up and the environment has been initalized. - **train** - invoked by the FEDn client to perform a model update. - **validate** - invoked by the FEDn client to perform a model validation. To illustrate this, we look the ``fedn.yaml`` from the 'mnist-pytorch' project used in the Getting Started Guide: .. code-block:: yaml python_env: python_env.yaml entry_points: build: command: python model.py startup: command: python data.py train: command: python train.py validate: command: python validate.py In this example, all entrypoints are python scripts (model.py, data.py, train.py and validate.py). They are executed by FEDn using the system default python interpreter 'python', in an environment with dependencies specified by "python_env.yaml". Next, we look at the environment specification and each entry point in more detail. **Environment (python_env.yaml)** FEDn assumes that all entry points (build, startup, train, validate) are executable within the client's runtime environment. You have two main options to handle the environment: 1. Let FEDn create and initalize the environment automatically by specifying ``python_env``. FEDn will then create an isolated virtual environment and install the dependencies specified in ``python_env.yaml`` into it before starting up the client. FEDn currently supports Virtualenv environments, with packages on PyPI. 2. Manage the environment manually. Here you have several options, such as managing your own virtualenv, running in a Docker container, etc. Remove the ``python_env`` tag from ``fedn.yaml`` to handle the environment manually. **build (optional):** This entry point is used for any kind of setup that **needs to be done to initialize FEDn prior to federated training**. This is the only entrypoint not used by the client during global training rounds - rather it is used by the project initator. Most often it is used to build the seed model. In the 'mnist-pytorch' example, ``build`` executes 'model.py' (shown below). This script contains the definition of the CNN model along with a main method that instantiates a model object (with random weights), exctracts its parameters into a list of numpy arrays and writes them to a file "seed.npz". .. code-block:: python import collections import torch from fedn.utils.helpers.helpers import get_helper HELPER_MODULE = "numpyhelper" helper = get_helper(HELPER_MODULE) def compile_model(): """Compile the pytorch model. :return: The compiled model. :rtype: torch.nn.Module """ class Net(torch.nn.Module): def __init__(self): super(Net, self).__init__() self.fc1 = torch.nn.Linear(784, 64) self.fc2 = torch.nn.Linear(64, 32) self.fc3 = torch.nn.Linear(32, 10) def forward(self, x): x = torch.nn.functional.relu(self.fc1(x.reshape(x.size(0), 784))) x = torch.nn.functional.dropout(x, p=0.5, training=self.training) x = torch.nn.functional.relu(self.fc2(x)) x = torch.nn.functional.log_softmax(self.fc3(x), dim=1) return x return Net() def save_parameters(model, out_path): """Save model paramters to file. :param model: The model to serialize. :type model: torch.nn.Module :param out_path: The path to save to. :type out_path: str """ parameters_np = [val.cpu().numpy() for _, val in model.state_dict().items()] helper.save(parameters_np, out_path) def load_parameters(model_path): """Load model parameters from file and populate model. param model_path: The path to load from. :type model_path: str :return: The loaded model. :rtype: torch.nn.Module """ model = compile_model() parameters_np = helper.load(model_path) params_dict = zip(model.state_dict().keys(), parameters_np) state_dict = collections.OrderedDict({key: torch.tensor(x) for key, x in params_dict}) model.load_state_dict(state_dict, strict=True) return model def init_seed(out_path="seed.npz"): """Initialize seed model and save it to file. :param out_path: The path to save the seed model to. :type out_path: str """ # Init and save model = compile_model() save_parameters(model, out_path) if __name__ == "__main__": init_seed("../seed.npz") **startup (optional):** The entry point 'startup' is used by the client. It is called **once**, immediately after the client starts up and the environment has been initalized. It can be used to do runtime configurations of the client's local execution environment. In the 'mnist-pytorch' project, the startup entry point invokes a script that downloads the MNIST dataset from an external server and creates a partition to be used by that client. Not all projects will specify a startup script. In the case of the mnist-pytorch example it is simply used as a convenience to automate experiments by splitting a publicly available dataset. However, in real-world settings with truly private data, the client will have the data locally. **train (mandatory):** This entry point is invoked when the client recieves a new model update (training) request from the server. The training entry point must be a single-input single-output (SISO) program. Upon recipt of a traing request, the FEDn client will download the latest version of the global model, write it to a (temporary) file and execute the command specified in the entrypoint: .. code-block:: python python train.py model_in model_out where 'model_in' is the **file** containing the current global model (parameters) to be updated, and 'model_out' is a **path** to write the new model update to (FEDn substitutes this path for tempfile location). When a traing update is complete, FEDn reads the updated paramters from 'model_out' and streams them back to the server for aggregation. .. note:: The training entrypoint must also write metadata to a json-file. The entry ``num_example`` is mandatory - it is used by the aggregators to compute a weighted average. The user can in addition choose to log other variables such as hyperparamters. These will then be stored in the backend database and accessible via the API and UI. In our 'mnist-pytorch' example, upon startup a client downloads the MNIST image dataset and creates partitions (one for each client). This partition is in turn divided into a train/test split. The file 'train.py' (shown below) reads the train split, runs an epoch of training and writes the updated paramters to file. To learn more about how model serialization and model marshalling works in FEDn, see :ref:`helper-label` and :ref:`agg-label`. .. code-block:: python import math import os import sys import torch from model import load_parameters, save_parameters from data import load_data from fedn.utils.helpers.helpers import save_metadata dir_path = os.path.dirname(os.path.realpath(__file__)) sys.path.append(os.path.abspath(dir_path)) def train(in_model_path, out_model_path, data_path=None, batch_size=32, epochs=1, lr=0.01): """Complete a model update. Load model paramters from in_model_path (managed by the FEDn client), perform a model update, and write updated paramters to out_model_path (picked up by the FEDn client). :param in_model_path: The path to the input model. :type in_model_path: str :param out_model_path: The path to save the output model to. :type out_model_path: str :param data_path: The path to the data file. :type data_path: str :param batch_size: The batch size to use. :type batch_size: int :param epochs: The number of epochs to train. :type epochs: int :param lr: The learning rate to use. :type lr: float """ # Load data x_train, y_train = load_data(data_path) # Load parmeters and initialize model model = load_parameters(in_model_path) # Train optimizer = torch.optim.SGD(model.parameters(), lr=lr) n_batches = int(math.ceil(len(x_train) / batch_size)) criterion = torch.nn.NLLLoss() for e in range(epochs): # epoch loop for b in range(n_batches): # batch loop # Retrieve current batch batch_x = x_train[b * batch_size : (b + 1) * batch_size] batch_y = y_train[b * batch_size : (b + 1) * batch_size] # Train on batch optimizer.zero_grad() outputs = model(batch_x) loss = criterion(outputs, batch_y) loss.backward() optimizer.step() # Log if b % 100 == 0: print(f"Epoch {e}/{epochs-1} | Batch: {b}/{n_batches-1} | Loss: {loss.item()}") # Metadata needed for aggregation server side metadata = { # num_examples are mandatory "num_examples": len(x_train), "batch_size": batch_size, "epochs": epochs, "lr": lr, } # Save JSON metadata file (mandatory) save_metadata(metadata, out_model_path) # Save model update (mandatory) save_parameters(model, out_model_path) if __name__ == "__main__": train(sys.argv[1], sys.argv[2]) **validate (optional):** When training a global model with FEDn, the data scientist can choose to ask clients to perform local model validation of each new global model version by specifying an entry point called 'validate'. Similar to the training entrypoint, the validation entry point must be a SISO program. It should reads a model update from file, validate it (in any way suitable to the user), and write a **json file** containing validation data: .. code-block:: python python validate.py model_in validations.json The content of the file 'validations.json' is captured by FEDn, passed on to the server and then stored in the database backend. The validate entry point is optional. In our 'mnist-pytorch' example, upon startup a client downloads the MNIST image dataset and creates partitions (one for each client). This partition is in turn divided into a train/test split. The file 'validate.py' (shown below) reads both the train and test splits and computes accuracy scores and the loss. It is a requirement that the output of validate.py is valid json. Furthermore, the FEDn Studio UI will be able to capture and visualize all **scalar metrics** specified in this file. The entire conent of the json file will be retrievable programatically using the FEDn APIClient, and can be downloaded from the Studio UI. .. code-block:: python import os import sys import torch from model import load_parameters from data import load_data from fedn.utils.helpers.helpers import save_metrics dir_path = os.path.dirname(os.path.realpath(__file__)) sys.path.append(os.path.abspath(dir_path)) def validate(in_model_path, out_json_path, data_path=None): """Validate model. :param in_model_path: The path to the input model. :type in_model_path: str :param out_json_path: The path to save the output JSON to. :type out_json_path: str :param data_path: The path to the data file. :type data_path: str """ # Load data x_train, y_train = load_data(data_path) x_test, y_test = load_data(data_path, is_train=False) # Load model model = load_parameters(in_model_path) model.eval() # Evaluate criterion = torch.nn.NLLLoss() with torch.no_grad(): train_out = model(x_train) training_loss = criterion(train_out, y_train) training_accuracy = torch.sum(torch.argmax(train_out, dim=1) == y_train) / len(train_out) test_out = model(x_test) test_loss = criterion(test_out, y_test) test_accuracy = torch.sum(torch.argmax(test_out, dim=1) == y_test) / len(test_out) # JSON schema report = { "training_loss": training_loss.item(), "training_accuracy": training_accuracy.item(), "test_loss": test_loss.item(), "test_accuracy": test_accuracy.item(), } # Save JSON save_metrics(report, out_json_path) if __name__ == "__main__": validate(sys.argv[1], sys.argv[2]) Testing the entrypoints ======================= We recommend you to test your training and validation entry points locally before creating the compute package and uploading it to Studio. To run the 'build' entrypoint and create the seed model (deafult filename 'seed.npz'): .. code-block:: python fedn run build --path client Run the 'startup' entrypoint to download the dataset: .. code-block:: python fedn run startup --path client Then, standing inside the 'client folder', you can test *train* and *validate* by: .. code-block:: bash python train.py ../seed.npz ../model_update.npz --data_path data/clients/1/mnist.pt python validate.py ../model_update.npz ../validation.json --data_path data/clients/1/mnist.pt You can also test *train* and *validate* entrypoint using CLI command: .. note:: Before running the fedn run train or fedn run validate commands, make sure to download the training and test data. The downloads are usually handled by the "fedn run startup" command in the examples provided by FEDn. .. code-block:: bash fedn run train --path client --input --output fedn run validate --path client --input --output Packaging for training on FEDn =============================== To run a project on FEDn we compress the entire client folder as a .tgz file. There is a utility command in the FEDn CLI to do this: .. code-block:: bash fedn package create --path client You can include a .ignore file in the client folder to exclude files from the package. This is useful for excluding large data files, temporary files, etc. To learn how to initialize FEDn with the package seed model, see :ref:`quickstart-label`. How is FEDn using the project? =============================== With an understanding of the FEDn project, the compute package (entrypoints), we can take a closer look at how FEDn is using the project during federated training. The figure below shows the logical view of how a training request is handled. A training round is initiated by the controller. It asks a Combiner for a model update. The model in turn asks clients to compute a model update, by publishing a training request to its request stream. The FEDn Client, :py:mod:`fedn.network.client`, subscribes to the stream and picks up the request. It then calls upon the Dispatcher, :py:mod:`fedn.utils.Dispatcher`. The dispatcher reads the Project File, 'fedn.yaml', looking up the entry point definition and executes that command. Upon successful execution, the FEDn Client reads the model update and metadata from file, and streams the content back to the combiner for aggregration. .. image:: img/ComputePackageOverview.png :alt: Compute package overview :width: 100% :align: center Where to go from here? ====================== With an understanding of how FEDn Projects are structured and created, you can explore our library of example projects. They demonstrate different use case scenarios of FEDn and its integration with popular machine learning frameworks like PyTorch and TensorFlow. - `FEDn + PyTorch `__ - `FEDn + Tensforflow/Keras `__ - `FEDn + MONAI `__ - `FEDn + Hugging Face `__ - `FEDn + Flower `__ - `FEDN + Self-supervised learning `__ .. meta:: :description lang=en: A FEDn project is a convention for packaging/wrapping machine learning code to be used for federated learning with FEDn. :keywords: Federated Learning, Machine Learning, Federated Learning Framework, Federated Learning Platform, FEDn, Scaleout Systems