tensorflow.rst

Horovod with TensorFlow

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

1. Run hvd.init().

2. 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')

3. 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.

4. 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.

5. 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.

6. 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)