Horovod with TensorFlow

To use Horovod with TensorFlow, make the following modifications to your training script:

  1. Run hvd.init().

  1. Pin each GPU to a single process.

    With the typical setup of one GPU per process, set this to local rank. The first process on the server will be allocated the first GPU, the second process will be allocated the second GPU, and so forth.

    For TensorFlow v1:

    config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())

    For TensorFlow v2:

    gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')

  1. Scale the learning rate by the 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.

  1. Wrap the optimizer in hvd.DistributedOptimizer.

    The distributed optimizer delegates gradient computation to the original optimizer, averages gradients using allreduce or allgather, and then applies those averaged gradients.

    For TensorFlow v2, when using a tf.GradientTape, wrap the tape in hvd.DistributedGradientTape instead of wrapping the optimizer.

  1. Broadcast the 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.

    For TensorFlow v1, add hvd.BroadcastGlobalVariablesHook(0) when using a MonitoredTrainingSession. When not using MonitoredTrainingSession, execute the hvd.broadcast_global_variables op after global variables have been initialized.

    For TensorFlow v2, use hvd.broadcast_variables after models and optimizers have been initialized.

  1. Modify your code to save checkpoints only on worker 0 to prevent other workers from corrupting them.

    For TensorFlow v1, accomplish this by passing checkpoint_dir=None to tf.train.MonitoredTrainingSession if hvd.rank() != 0.

    For TensorFlow v2, construct a tf.train.Checkpoint and only call checkpoint.save() when hvd.rank() == 0.

TensorFlow v1 Example (see the examples directory for full training examples):

import tensorflow as tf
import horovod.tensorflow as hvd


# Initialize Horovod
hvd.init()

# Pin GPU to be used to process local rank (one GPU per process)
config = tf.ConfigProto()
config.gpu_options.visible_device_list = str(hvd.local_rank())

# Build model...
loss = ...
opt = tf.train.AdagradOptimizer(0.01 * hvd.size())

# Add Horovod Distributed Optimizer
opt = hvd.DistributedOptimizer(opt)

# Add hook to broadcast variables from rank 0 to all other processes during
# initialization.
hooks = [hvd.BroadcastGlobalVariablesHook(0)]

# Make training operation
train_op = opt.minimize(loss)

# Save checkpoints only on worker 0 to prevent other workers from corrupting them.
checkpoint_dir = '/tmp/train_logs' if hvd.rank() == 0 else None

# The MonitoredTrainingSession takes care of session initialization,
# restoring from a checkpoint, saving to a checkpoint, and closing when done
# or an error occurs.
with tf.train.MonitoredTrainingSession(checkpoint_dir=checkpoint_dir,
                                       config=config,
                                       hooks=hooks) as mon_sess:
  while not mon_sess.should_stop():
    # Perform synchronous training.
    mon_sess.run(train_op)

TensorFlow v2 Example (from the MNIST example):

import tensorflow as tf
import horovod.tensorflow as hvd

# Initialize Horovod
hvd.init()

# Pin GPU to be used to process local rank (one GPU per process)
gpus = tf.config.experimental.list_physical_devices('GPU')
for gpu in gpus:
    tf.config.experimental.set_memory_growth(gpu, True)
if gpus:
    tf.config.experimental.set_visible_devices(gpus[hvd.local_rank()], 'GPU')

# Build model and dataset
dataset = ...
model = ...
loss = tf.losses.SparseCategoricalCrossentropy()
opt = tf.optimizers.Adam(0.001 * hvd.size())

checkpoint_dir = './checkpoints'
checkpoint = tf.train.Checkpoint(model=model, optimizer=opt)

@tf.function
def training_step(images, labels, first_batch):
    with tf.GradientTape() as tape:
        probs = mnist_model(images, training=True)
        loss_value = loss(labels, probs)

    # Horovod: add Horovod Distributed GradientTape.
    tape = hvd.DistributedGradientTape(tape)

    grads = tape.gradient(loss_value, mnist_model.trainable_variables)
    opt.apply_gradients(zip(grads, mnist_model.trainable_variables))

    # Horovod: 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.
    #
    # Note: broadcast should be done after the first gradient step to ensure optimizer
    # initialization.
    if first_batch:
        hvd.broadcast_variables(mnist_model.variables, root_rank=0)
        hvd.broadcast_variables(opt.variables(), root_rank=0)

    return loss_value

# Horovod: adjust number of steps based on number of GPUs.
for batch, (images, labels) in enumerate(dataset.take(10000 // hvd.size())):
    loss_value = training_step(images, labels, batch == 0)

    if batch % 10 == 0 and hvd.local_rank() == 0:
        print('Step #%d\tLoss: %.6f' % (batch, loss_value))

# Horovod: save checkpoints only on worker 0 to prevent other workers from
# corrupting it.
if hvd.rank() == 0:
    checkpoint.save(checkpoint_dir)

Horovod with TensorFlow Data Service

A TensorFlow Data Service allows to move CPU intensive processing of your dataset from your training process to a cluster of CPU-rich processes.

With Horovod, it is easy to spin up a TensorFlow Data Service on your Horovod cluster and to connect your Horovod training job to it.

Run the following command to run a TensorFlow Data Service via Horovod:

horovodrun -np 4 python -m horovod.tensorflow.data.compute_worker /tmp/compute.json

This starts a TensorFlow Data Service (here called compute job) with one dispatcher and four workers.

Note

The config file is written by the compute job and has to be located on a path that is accessible to all nodes that run the compute job, e.g. a distributed file system.

Your training job can then move CPU intensive dataset operations to this data service by calling .send_to_data_service(…) on the TensorFlow dataset:

from horovod.tensorflow.data.compute_service import TfDataServiceConfig

hvd.init()
rank = hvd.rank()
size = hvd.size()

compute_config = TfDataServiceConfig.read('/tmp/compute.json', wait_for_file_creation=True)

dataset = dataset.repeat() \
    .shuffle(10000) \
    .batch(128) \
    .send_to_data_service(compute_config, rank, size) \
    .prefetch(tf.data.experimental.AUTOTUNE)

All transformations before calling send_to_data_service will be executed by the data service, while all transformations after it are executed locally by the training script.

You can find the tensorflow2_mnist_data_service.py example in the examples directory.

First start the data service as shown above. While the data service is running, start the example training script:

horovodrun -np 2 python tensorflow2_mnist_data_service.py /tmp/compute.json

The compute job normally runs on CPU nodes while the training job runs on GPU nodes. This allows to run CPU intensive dataset transformation on CPU nodes while running GPU intensive training on GPU nodes. There can be multiple CPUs dedicated to one GPU task.

Use the --hosts argument to run compute and train job on CPU (here cpu-node-1 and cpu-node-2) and GPU nodes (here gpu-node-1 and gpu-node-2), respectively:

horovodrun -np 4 --hosts cpu-node-1:2,cpu-node-2:2 python -m horovod.tensorflow.data.compute_worker /tmp/compute.json
horovodrun -np 2 --hosts gpu-node-1:1,gpu-node-2:1 python tensorflow2_mnist_data_service.py /tmp/compute.json

Note

Please make sure you understand how TensorFlow Data Service distributes dataset transformations: See the distribute transformation.

Multiple Dispatchers

The data service allows for multiple dispatchers, one per training task. Each dispatcher gets the same number of workers. As workers are dedicated to a single dispatcher, workers get dedicated to a single training task. The size of your compute job (-np 4) has to be a multiple of the number of dispatchers (--dispatchers 2):

horovodrun -np 4 python -m horovod.tensorflow.data.compute_worker --dispatchers 2 /tmp/compute.json

This requires the number of dispatchers (--dispatchers 2) to match the size of your training job (-np 2):

horovodrun -np 2 python tensorflow2_mnist_data_service.py /tmp/compute.json

Single Dispatchers

With a single dispatcher, TensorFlow allows to reuse the dataset across all training tasks. This is done on a first-come-first-serve basis, or round robin. The only supported processing mode is "distributed_epoch".

Training-side dispatchers

The dispatchers by default run inside the compute job. You can, however, also run them inside the training job. Add --dispatcher-side training to tell the compute job that dispatchers are started by the training job.

horovodrun -np 4 python -m horovod.tensorflow.data.compute_worker --dispatcher-side training /tmp/compute.json

The training script then starts the dispatchers via with tf_data_service(…) and distributes the dataset itself:

hvd.init()
rank = hvd.rank()
size = hvd.size()

compute_config = TfDataServiceConfig.read('/tmp/compute.json', wait_for_file_creation=True)

with tf_data_service(compute_config, rank) as dispatcher_address:

    dataset = dataset.repeat() \
        .shuffle(10000) \
        .batch(128) \
        .apply(tf.data.experimental.service.distribute(
            processing_mode="distributed_epoch",
            service=dispatcher_address,
            job_name='job' if reuse_dataset else None,
            consumer_index=rank if round_robin else None,
            num_consumers=size if round_robin else None)) \
        .prefetch(tf.data.experimental.AUTOTUNE)

To see the specific changes needed to make the training job run dispatchers, simply diff the training-side example with the compute-side example:

diff -w examples/tensorflow2/tensorflow2_mnist_data_service_train_fn_*

Compute job on Spark cluster

The compute job can be started on a Spark cluster using spark-submit:

worker_py=$(python -c "import horovod.spark.tensorflow.compute_worker as worker; print(worker.__file__)")
spark-submit --master "local[4]" "$worker_py" /tmp/compute.json

While the compute job is running, start the training job:

cd examples/spark/tensorflow2 spark-submit –master “local[2]” –py-files tensorflow2_mnist_data_service_train_fn_compute_side_dispatcher.py,tensorflow2_mnist_data_service_train_fn_training_side_dispatcher.py tensorflow2_mnist_data_service.py /tmp/compute.json

As usual, the config file has to be located on a path that is accessible to all nodes that run the compute job.