Simulation with Message Passing Interface (MPI)

MPI-based Federated Learning for cross-GPU/CPU servers.

In this example, we will present how to apply the standalone simulation of FedML in the MINIST image classification using an MPI-based FL simulator. The complete code is available at https://github.com/FedML-AI/FedML/tree/master/python/examples/simulation/mpi_torch_fedavg_mnist_lr_example在新窗口打开.

One line API

Step 1. preparation

In this example, we have to complete the operation of step1 in the One line API first, and then proceed to the subsequent operations.

We use the mpi4py package for the MPI implementation in Python. Here you need to install mpi4py with conda install instead of pip install because pip install will report an error.

conda install mpi4py

Step 2. setup Parameters

config/fedml_config.yaml is almost the same as fedml_config.yaml in One line API step2, only the backend setting in comm_args is different. Here comm_args.backend is "MPI", which means the program is running based on an MPI-based FL Simulator.

comm_args:
  backend: "MPI"
  is_mobile: 0

Step 3. training

Now that we have configured all the dependencies, we can quickly implement the training of the federated learning model based on MPI-based FL Simulator on the MNIST dataset with the following line of code.

# 4 means 4 processes
bash run_one_line_example.sh 4

Note that if you download the code on Windows and upload it to a Linux environment, you will see the following output when the program just starts running.

run_one_line_example.sh: line 2: $' \r ': command not found
run_one_line_example.sh: line 4: $'\r': command not found
expr: non-integer argument
run_one_line_example.sh: line 7: $'\r': command not found
run_one_line_example.sh: line 9: $'\r': command not found

The main way to deal with this is to end the program and then run the following command, filename here is run_one_line_example.sh

sed -i 's/\r$//' filename

When it officially starts running, you can see the real-time output of the program running on the terminal. When the process is finished and you see an output similar to the following, it means that the whole process is running successfully.

FedML-Server(0) @device-id-0 - Tue, 26 Apr 2022 02:53:22 FedAvgClientManager.py[line:71] INFO #######training########### round_id = 49
[2022-04-26 02:53:22,054] [INFO] [my_model_trainer_classification.py:56:train] Update Epoch: 0 [10/30 (33%)]    Loss: 1.775815
[2022-04-26 02:53:22,055] [INFO] [my_model_trainer_classification.py:56:train] Update Epoch: 0 [20/30 (67%)]    Loss: 1.920599
[2022-04-26 02:53:22,055] [INFO] [my_model_trainer_classification.py:56:train] Update Epoch: 0 [30/30 (100%)]   Loss: 1.839799
[2022-04-26 02:53:22,055] [INFO] [my_model_trainer_classification.py:63:train] Client Index = 3 Epoch: 0        Loss: 1.845405
FedML-Server(0) @device-id-0 - Tue, 26 Apr 2022 02:53:22 client_manager.py[line:104] INFO Sending message (type 3) to server
FedML-Server(0) @device-id-0 - Tue, 26 Apr 2022 02:53:22 client_manager.py[line:118] INFO __finish client
--------------------------------------------------------------------------
MPI_ABORT was invoked on rank 4 in communicator MPI_COMM_WORLD
with errorcode 0.

NOTE: invoking MPI_ABORT causes Open MPI to kill all MPI processes.
You may or may not see output from other processes, depending on
exactly when Open MPI kills them.
--------------------------------------------------------------------------

Let's have a look at the run_one_line_example.sh

When we run the above command, 4 will be assigned to the parameter WORKER_NUM, representing 4 client processes.
Executing echo $PROCESS_NUM will output 5 on the terminal, representing the total number of processes is 5 (including server).
hostname > mpi_host_file
$(which mpirun) -np $PROCESS_NUM \-hostfile mpi_host_file --oversubscribe \python torch_fedavg_mnist_lr_one_line_example.py --cf config/fedml_config.yaml This line of code mpirun will run the program using the mpi method and specify the parameter file with -np $PROCESS_NUM specifies the total number of processes in the program, --cf config/fedml_config.yaml, hostname > mpi_host_file write the hostname to the mpi_host_file fileand -hostfile mpi_host_file specifies the host file of the mpi

#!/usr/bin/env bash

WORKER_NUM=$1

PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM

hostname > mpi_host_file

$(which mpirun) -np $PROCESS_NUM \
-hostfile mpi_host_file --oversubscribe \
python torch_fedavg_mnist_lr_one_line_example.py --cf config/fedml_config.yaml

The code for torch_fedavg_mnist_lr_one_line_example.py is shown below:

import fedml


if __name__ == "__main__":
    fedml.run_simulation(backend="MPI")

Step 4. view the results

You can view the output log files in the /log directory under the current directory.

Step by step API

First, we should also complete the step1 and step2 operations in the one-line example and quickly implement the federation learning model training on the MNIST dataset with the following line of code for the MPI-based FL Simulator.

sh run_step_by_step_example.sh 4

The code of run_step_by_step_example.sh is as follows, which is generally the same as run_one_line_example.sh in this section of the One line API

#!/usr/bin/env bash

WORKER_NUM=$1

PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM

hostname > mpi_host_file

$(which mpirun) -np $PROCESS_NUM \
-hostfile mpi_host_file --oversubscribe \
python torch_fedavg_mnist_lr_step_by_step_example.py --cf config/fedml_config.yaml

The code of torch_fedavg_mnist_lr_step_by_step_example.py is shown below. We can see that the code follows the steps of the Step by step API in Example: Simulate FL using a single process.

The difference is that simulator = SimulatorMPI(args, device, dataset, model) is used to initialize the model object, which means that MPI-based FL Simulator is used here for training

import fedml
from fedml.simulation import SimulatorMPI

if __name__ == "__main__":
    # init FedML framework
    args = fedml.init()

    # init device
    device = fedml.device.get_device(args)

    # load data
    dataset, output_dim = fedml.data.load(args)

    # load model
    model = fedml.model.create(args, output_dim)

    # start training
    simulator = SimulatorMPI(args, device, dataset, model)
    simulator.run()

Custom data and model

The operation of this part is similar to the custom data and model part in example Simulate FL using a single process .

In this section we will present how to customize the dataset and model using FedML based on the Step by step example and implement a stand-alone simulated version of the FedAvg algorithm.

First we still need to complete the first two cases step1 and step2, and then we can quickly implement the federation learning model training on the MNIST dataset with the following line of code for stand-alone simulation:

python torch_fedavg_mnist_lr_custum_data_and_model_example.py --cf fedml_config.yaml

The torch_fedavg_mnist_lr_custum_data_and_model_example.py defines the

load_data(args) function for loading the dataset and related information, and the

LogisticRegression(torch.nn. Module) class defines the LogisticRegression model. The specific code is as follows:

def load_data(args):
    download_mnist(args.data_cache_dir)
    fedml.logger.info("load_data. dataset_name = %s" % args.dataset)

    """
    Please read through the data loader at to see how to customize the dataset for FedML framework.
    """
    (
        client_num,
        train_data_num,
        test_data_num,
        train_data_global,
        test_data_global,
        train_data_local_num_dict,
        train_data_local_dict,
        test_data_local_dict,
        class_num,
    ) = load_partition_data_mnist(
        args.batch_size,
        train_path=args.data_cache_dir + "MNIST/train",
        test_path=args.data_cache_dir + "MNIST/test",
    )
    """
    For shallow NN or linear models, 
    we uniformly sample a fraction of clients each round (as the original FedAvg paper)
    """
    args.client_num_in_total = client_num
    dataset = [
        train_data_num,
        test_data_num,
        train_data_global,
        test_data_global,
        train_data_local_num_dict,
        train_data_local_dict,
        test_data_local_dict,
        class_num,
    ]
    return dataset, class_num

class LogisticRegression(torch.nn.Module):
    def __init__(self, input_dim, output_dim):
        super(LogisticRegression, self).__init__()
        self.linear = torch.nn.Linear(input_dim, output_dim)

    def forward(self, x):
        outputs = torch.sigmoid(self.linear(x))
        return outputs

torch_fedavg_mnist_lr_custum_data_and_model_example.py is similar to torch_fedavg_mnist_lr_step_by_step_example.py, the code includes the same parts, though torch_fedavg_mnist_lr_custum_data_and_model_example.py loads the dataset and the model definition part using custom function and class. The code for the training process is shown below:

if __name__ == "__main__":
    # init FedML framework
    args = fedml.init()

    # init device
    device = fedml.device.get_device(args)

    # load data
    dataset, output_dim = load_data(args)

    # load model (the size of MNIST image is 28 x 28)
    model = LogisticRegression(28 * 28, output_dim)

    # start training
    simulator = SimulatorMPI(args, device, dataset, model)
    simulator.run()

Develop new algorithms

Base framework

python/examples/simulation/mpi_base_framework_example

This is a base framework used to develop new algorithm. You can copy this directory and modify directly. The basic message flow is workable. What you need to do is designing the message flow and defining the payload of each message.

As a research library, our philosophy is to give flexibility to users and avoid over-designed software patterns.

Run the example:

sh run.sh 4

run.sh

#!/usr/bin/env bash

WORKER_NUM=$1

PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM

hostname > mpi_host_file

$(which mpirun) -np $PROCESS_NUM \
-hostfile mpi_host_file --oversubscribe \
python mpi_base_framework_example.py --cf config/fedml_config.yaml

mpi_base_framework_example.py

import fedml
from fedml import SimulatorMPI

if __name__ == "__main__":
    # init FedML framework
    args = fedml.init()

    # start training
    simulator = SimulatorMPI(args, None, None, None)
    simulator.run()

You can customize device, dataset, model in this base framework.

Decentralized framework

python/examples/simulation/mpi_decentralized_fl_example

This is a decentralized framework used to develop new algorithm. You can copy this directory and modify directly. The basic message flow is workable. What you need to do is designing the message flow and defining the payload of each message.

As a research library, our philosophy is giving flexibility to users and avoid overdesigned software pattern.

Run the example:

sh run.sh 4

run.sh

#!/usr/bin/env bash

WORKER_NUM=$1

PROCESS_NUM=`expr $WORKER_NUM + 1`
echo $PROCESS_NUM

hostname > mpi_host_file

$(which mpirun) -np $PROCESS_NUM \
-hostfile mpi_host_file --oversubscribe \
python mpi_decentralized_fl_example.py --cf config/fedml_config.yaml

mpi_decentralized_fl_example.py

import fedml
from fedml import SimulatorMPI

if __name__ == "__main__":
    # init FedML framework
    args = fedml.init()

    # start training
    simulator = SimulatorMPI(args, None, None, None)
    simulator.run()

You can customize device, dataset, model in this base framework.

# Simulation with Message Passing Interface (MPI)

# One line API

# Step 1. preparation

# Step 2. setup Parameters

# Step 3. training

# Step 4. view the results

# Step by step API

# Custom data and model

# Develop new algorithms

# Base framework

# Decentralized framework