Running Distributed Training Using Horovod and MPI#

This example demonstrates how to conduct distributed training of a CNN on MNIST data.

To begin, import the necessary dependencies.

import os
import pathlib

import flytekit
import tensorflow as tf
from flytekit import Resources, task, workflow
from flytekit.core.base_task import IgnoreOutputs
from import FlyteDirectory
from flytekitplugins.kfmpi import Launcher, MPIJob, Worker

In the context of this example, we define a training step that will be invoked during the training loop. In this step, the training loss is calculated and the model weights are adjusted using gradients.

def training_step(images, labels, first_batch, mnist_model, loss, opt):
    import horovod.tensorflow as hvd

    with tf.GradientTape() as tape:
        probs = mnist_model(images, training=True)
        loss_value = loss(labels, probs)

    # Add Horovod Distributed GradientTape โ€” a tape that wraps another tf.GradientTape,
    # using an allreduce to combine gradient values before applying gradients to model weights.
    tape = hvd.DistributedGradientTape(tape)

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

    # 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

To create an MPI task, add MPIJob config to the Flyte task. The configuration given in the MPIJob constructor will be used to set up the distributed training environment.

Broadly, let us define a task that executes the following operations:

  1. Loads the MNIST data

  2. Prepares the data for training

  3. Initializes a CNN model

  4. Invokes the training_step() function to train the model

  5. Saves the model, checkpoint history, and returns the result


For running Horovod code specifically, an alternative to using the MPIJob configuration is to employ the HorovodJob configuration. Internally, the HorovodJob configuration utilizes the horovodrun command, while the MPIJob configuration utilizes mpirun.

    requests=Resources(cpu="1", mem="1000Mi"),
def horovod_train_task(batch_size: int, buffer_size: int, dataset_size: int) -> FlyteDirectory:
    import horovod.tensorflow as hvd


    (mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(path="mnist-%d.npz" % hvd.rank())

    dataset =
            tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32),
            tf.cast(mnist_labels, tf.int64),
    dataset = dataset.repeat().shuffle(buffer_size).batch(batch_size)

    mnist_model = tf.keras.Sequential(
            tf.keras.layers.Conv2D(32, [3, 3], activation="relu"),
            tf.keras.layers.Conv2D(64, [3, 3], activation="relu"),
            tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
            tf.keras.layers.Dense(128, activation="relu"),
            tf.keras.layers.Dense(10, activation="softmax"),
    loss = tf.losses.SparseCategoricalCrossentropy()

    # Adjust learning rate based on number of GPUs
    opt = tf.optimizers.Adam(0.001 * hvd.size())

    checkpoint_dir = ".checkpoint"

    checkpoint = tf.train.Checkpoint(model=mnist_model, optimizer=opt)

    # Adjust number of steps based on number of GPUs
    for batch, (images, labels) in enumerate(dataset.take(dataset_size // hvd.size())):
        loss_value = training_step(images, labels, batch == 0, mnist_model, loss, opt)

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

    if hvd.rank() != 0:
        raise IgnoreOutputs("I am not rank 0")

    working_dir = flytekit.current_context().working_directory
    checkpoint_prefix = pathlib.Path(os.path.join(working_dir, "checkpoint"))

    return FlyteDirectory(path=str(working_dir))

Lastly, define a workflow.

def horovod_training_wf(batch_size: int = 128, buffer_size: int = 10000, dataset_size: int = 10000) -> FlyteDirectory:
    :param batch_size: Represents the number of consecutive elements of the dataset to combine in a single batch.
    :param buffer_size: Defines the size of the buffer used to hold elements of the dataset used for training.
    :param dataset_size: The number of elements of this dataset that should be taken to form the new dataset when
        running batched training.
    return horovod_train_task(batch_size=batch_size, buffer_size=buffer_size, dataset_size=dataset_size)

You can execute the workflow locally.

if __name__ == "__main__":
    model, plot, logs = horovod_training_wf()
    print(f"Model: {model}, plot PNG: {plot}, Tensorboard Log Dir: {logs}")


In the context of distributed training, itโ€™s important to acknowledge that return values from various workers could potentially vary. If you need to regulate which workerโ€™s return value gets passed on to subsequent tasks in the workflow, you have the option to raise an IgnoreOutputs exception for all remaining ranks.