Horovod with MXNetΒΆ

Horovod supports Apache MXNet and regular TensorFlow in similar ways.

See full training MNIST and ImageNet examples. The script below provides a simple skeleton of code block based on the Apache MXNet Gluon API.

import mxnet as mx
import horovod.mxnet as hvd
from mxnet import autograd

# Initialize Horovod

# Pin GPU to be used to process local rank
context = mx.gpu(hvd.local_rank())
num_workers = hvd.size()

# Build model
model = ...

# Create optimizer
optimizer_params = ...
opt = mx.optimizer.create('sgd', **optimizer_params)

# Initialize parameters
model.initialize(initializer, ctx=context)

# Fetch and broadcast parameters
params = model.collect_params()
if params is not None:
    hvd.broadcast_parameters(params, root_rank=0)

# Create DistributedTrainer, a subclass of gluon.Trainer
trainer = hvd.DistributedTrainer(params, opt)

# Create loss function
loss_fn = ...

# Train model
for epoch in range(num_epoch):
    for nbatch, batch in enumerate(train_data, start=1):
        data = batch.data[0].as_in_context(context)
        label = batch.label[0].as_in_context(context)
        with autograd.record():
            output = model(data.astype(dtype, copy=False))
            loss = loss_fn(output, label)


Some MXNet versions do not work with Horovod:

  • MXNet 1.4.0 and earlier have GCC incompatibility issues. Use MXNet 1.4.1 or later with Horovod 0.16.2 or later to avoid these incompatibilities.

  • MXNet 1.5.1, 1.6.0, 1.7.0, and 1.7.0.post1 are missing MKLDNN headers, so they do not work with Horovod. Use 1.5.1.post0, 1.6.0.post0, and 1.7.0.post0, respectively.

  • MXNet 1.6.0.post0 and 1.7.0.post0 are only available as mxnet-cu101 and mxnet-cu102.