examples/cifar_distributed_cnn/autograd/sparsification_mnist.py

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from mnist_cnn import *
import multiprocessing
import sys

if __name__ == '__main__':

    # Generate a NCCL ID to be used for collective communication
    nccl_id = singa.NcclIdHolder()

    # Number of GPUs to be used
    world_size = int(sys.argv[1])

    # Use sparsification with parameters
    topK = False  # When topK = False, Sparsification based on a constant absolute threshold
    corr = True  # If True, uses local accumulate gradient for the correction
    sparsThreshold = 0.05  # The constant absolute threshold for sparsification

    process = []
    for local_rank in range(0, world_size):
        process.append(
            multiprocessing.Process(target=train_mnist_cnn,
                                    args=(True, local_rank, world_size, nccl_id,
                                          sparsThreshold, topK, corr)))

    for p in process:
        p.start()