Welcome to the Horovod MPI Tutorial! This guide will walk you through setting up and using Horovod with MPI for distributed training of deep learning models.

Overview

Horovod is an open-source, scalable distributed training framework for deep learning models. It allows you to distribute the training process across multiple GPUs and multiple machines with minimal changes to your code.

Key Concepts

  • MPI (Message Passing Interface): A standardized and portable message-passing system designed by a group of researchers from academia and industry to function on a variety of parallel computers.
  • Distributed Training: Training a model across multiple GPUs or machines to improve performance and speed.

Prerequisites

  • Python 3.5+
  • TensorFlow 2.x
  • Horovod

Installation

You can install Horovod using pip:

pip install horovod

For more information, please refer to the Horovod installation guide.

Setting Up MPI

Before you can use Horovod with MPI, you need to set up an MPI environment. This typically involves installing an MPI library like OpenMPI or MPICH.

For more information on setting up MPI, please refer to the MPI setup guide.

Example

Here's an example of how to use Horovod with MPI to train a TensorFlow model:

import tensorflow as tf
from horovod.tensorflow import HorovodRunner

# Load the model
model = tf.keras.models.load_model('my_model.h5')

# Create a Horovod runner
runner = HorovodRunner()

# Compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

# Train the model
runner.run(model.fit(x_train, y_train, batch_size=32, epochs=10))

Tips and Tricks

  • Load Balancing: Horovod automatically handles load balancing across all participating nodes.
  • Checkpointing: You can use TensorFlow's built-in checkpointing feature to save and restore model states during training.
  • Scaling: Horovod scales to any number of GPUs and nodes, making it suitable for both small and large-scale distributed training.

Resources

For more detailed information, please refer to the following resources:

By following this tutorial, you should now be able to set up and use Horovod with MPI for distributed training of deep learning models. Happy training!