Horovod is a high-performance distributed training framework for TensorFlow, Keras, and PyTorch. This document provides an overview of advanced distributed training techniques using Horovod, including setup, configuration, and best practices.

Prerequisites

Before you start, make sure you have the following prerequisites:

  • Python 3.5 or higher
  • TensorFlow, Keras, or PyTorch installed
  • Horovod installed (pip install horovod)

Setup

To set up distributed training with Horovod, you need to enable Horovod in your training script. Here's an example of how to do this with TensorFlow:

import tensorflow as tf
import horovod.tensorflow as hvd

# Initialize Horovod
hvd.init()

# Enable distributed strategy
strategy = tf.distribute.experimental.HorovodStrategy()

with strategy.scope():
    # Define your model and training loop
    # ...

Configuration

Horovod supports various configuration options to customize your distributed training. You can set these options using environment variables or a configuration file.

Environment Variables

  • HOROVODMASTER: The address of the master node (e.g., tcp://localhost:23456).
  • HOROVODNNODES: The number of nodes participating in the training.
  • HOROVODPORT: The port number for Horovod communication.

Configuration File

You can also use a configuration file (e.g., horovod.conf) to set these options:

master = tcp://localhost:23456
nnodes = 2
port = 23456

Best Practices

Here are some best practices for distributed training with Horovod:

  • Use a cluster of machines for better performance.
  • Enable Horovod's all-reduce algorithm for efficient communication.
  • Tune your batch size and learning rate for distributed training.
  • Monitor your training progress using TensorBoard or another monitoring tool.

Resources

For more information, please refer to the following resources:

Horovod Architecture