Distributed Training using MXNet with Horovod

Horovod is a distributed training framework that demonstrates excellent scaling efficiency for dense models running on a large number of nodes. It currently supports mainstream deep learning frameworks such as MXNet, TensorFlow, Keras, and PyTorch. It is created at Uber and currently hosted by the Linux Foundation Deep Learning(LF DL).

MXNet is supported starting from Horovod 0.16.0 release.

What's New?

Compared with the standard distributed training script in MXNet which uses parameter server to distribute and aggregate parameters, Horovod uses ring allreduce and/or tree-based allreduce algorithm to communicate parameters between workers. There is no dedicated server and the communication data size between workers does not depend on the number of workers. Therefore, it scales well in the case where there are a large number of workers and network bandwidth is the bottleneck.

Install

Install MXNet

$ pip install mxnet

Note: The known issue when running Horovod with MXNet on a Linux system with GCC version 5.X and above has been resolved. Please use MXNet 1.4.1 or later releases with Horovod 0.16.2 or later releases to avoid the GCC incompatibility issue. MXNet 1.4.0 release works with Horovod 0.16.0 and 0.16.1 releases with the GCC incompatibility issue unsolved.

Install Horovod

$ pip install horovod

This basic installation is good for laptops and for getting to know Horovod. If you're installing Horovod on a server with GPUs, read the Horovod on GPU page. If you want to use Docker, read the Horovod in Docker page.

Install MPI

MPI is required to run distributed training with Horovod. Install Open MPI or another MPI implementation. Steps to install Open MPI are listed here.

Note: Open MPI 3.1.3 has an issue that may cause hangs. It is recommended to downgrade to Open MPI 3.1.2 or upgrade to Open MPI 4.0.0.

Usage

To run MXNet with Horovod, make the following additions to your training script:

1. Run hvd.init().

2. Pin the context to a processor using hvd.local_rank(). Typically, each Horovod worker is associated with one process. The local rank is a unique ID specifically for all processes running Horovod job on the same node.

3. Scale the learning rate by number of workers. Effective batch size in synchronous distributed training is scaled by the number of workers. An increase in learning rate compensates for the increased batch size.

4. Create hvd.DistributedTrainer with optimizer when using Gluon API or wrap optimizer in hvd.DistributedOptimizer when using Module API. The distributed trainer or optimizer delegates gradient computation to the original optimizer, averages gradients using allreduce, and then applies those averaged gradients.

5. Add hvd.broadcast_parameters to broadcast initial variable states from rank 0 to all other processes. This is necessary to ensure consistent initialization of all workers when training is started with random weights or restored from a checkpoint.

Example

Here we provide the building blocks to train a model using MXNet with Horovod. The full examples are in MNIST and ImageNet.

Gluon API

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

# Initialize Horovod
hvd.init()

# Set context to current process 
context = mx.cpu(hvd.local_rank()) if args.no_cuda else mx.gpu(hvd.local_rank())

num_workers = hvd.size()

# Build model
model = ...
model.hybridize()


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

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

# 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 loss function
loss_fn = ...

# Train model
for epoch in range(num_epoch):
    train_data.reset()
    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)
        loss.backward()
        trainer.step(batch_size)

Module API

import mxnet as mx
import horovod.mxnet as hvd

# Initialize Horovod
hvd.init()

# Set context to current process
context = mx.cpu(hvd.local_rank()) if args.no_cuda else mx.gpu(hvd.local_rank())
num_workers = hvd.size()

# Build model
model = ...

# Define hyper parameters
optimizer_params = ...

# Add Horovod Distributed Optimizer
opt = mx.optimizer.create('sgd', **optimizer_params)
opt = hvd.DistributedOptimizer(opt)

# Initialize parameters
initializer = mx.init.Xavier(rnd_type='gaussian', factor_type="in",
                             magnitude=2)
model.bind(data_shapes=train_data.provide_data,
           label_shapes=train_data.provide_label)
model.init_params(initializer)

# Fetch and broadcast parameters
(arg_params, aux_params) = model.get_params()
if arg_params:
    hvd.broadcast_parameters(arg_params, root_rank=0)
if aux_params:
    hvd.broadcast_parameters(aux_params, root_rank=0)
model.set_params(arg_params=arg_params, aux_params=aux_params)

# Train model
model.fit(train_data,
          kvstore=None,
          optimizer=opt,
          num_epoch=num_epoch)

Running Horovod

The example commands below show how to run distributed training. See the Running Horovod page for more instructions.

1. To run on a machine with 4 CPUs:

$ mpirun -np 4 \
    -H localhost:4 \
    -bind-to none -map-by slot \
    python train.py

2. To run on 2 machines with 4 GPUs each:

$ mpirun -np 8 \
    -H server1:4,server2:4 \
    -bind-to none -map-by slot \
    -x NCCL_DEBUG=INFO \
    -mca pml ob1 -mca btl ^openib \
    python train.py