Simulating PPFL#

This package provides users with the capability of simulating PPFL on either a single machine or an HPC cluster.


Running PPFL on multiple heterogeneous machines is described in Training PPFL.

We describe how to simulate PPFL with a given model and datasets. For simulation, we assume that test_data is available to validate the trained model.

Serial run#

Serial runs begin simply by calling the following API function.

Some remarks are made as follows:

  • Parameter cfg: DictConfig reads the configuration of runs. See How to set configuration for details about configuration.

  • Parameters model, train_data, and test_data should be given by users; see User-defined model and User-defined dataset.

Parallel run with MPI#

We can parallelize the PPFL simulation by usinig MPI through mpi4py package. The following two API functions need to be called for parallelization.

The server and the clients begin by run_server and run_client, respectively, where MPI communicator (e.g., MPI.COMM_WORLD in this example) is given as an argument.


We assume that MPI process 0 runs the server, and the other processes run clients.


mpiexec may need to specify additional argument to use CUDA: --mca opal_cuda_support 1