Advanced Horovod Guide

Horovod is an open-source library for distributed training of deep learning models. This guide will help you understand the advanced features and usage of Horovod.

Overview

Horovod provides high-performance distributed training across multiple GPUs on a single machine, and across multiple machines. It is designed to be easy to use and integrate with existing deep learning frameworks like TensorFlow, Keras, PyTorch, and Apache MXNet.

Key Features

• High Performance: Achieve high throughput by running many training iterations in parallel.
• Easy to Use: Simple API integration with existing deep learning frameworks.
• Scalable: Supports training across multiple GPUs and machines.

Getting Started

To get started with Horovod, you can install it using pip:

pip install horovod

For more detailed installation instructions, please refer to the Horovod installation guide.

Advanced Usage

Distributed Training

Horovod allows you to distribute your training across multiple GPUs on a single machine or across multiple machines. Here's an example of how to set up distributed training with TensorFlow:

import tensorflow as tf
import horovod.tensorflow as hvd

# Initialize Horovod
hvd.init()

# Create a model
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(100,)),
    tf.keras.layers.Dense(1)
])

# Compile the model
model.compile(optimizer='adam',
              loss='mean_squared_error')

# Train the model
model.fit(x, y, batch_size=32, epochs=10)

For more information on distributed training, please refer to the Horovod distributed training guide.

Load Balancing

Horovod supports load balancing to ensure that each GPU receives a fair share of the data. This is particularly useful when training on a cluster with a mix of GPUs with different memory sizes.

import horovod.tensorflow as hvd

# Initialize Horovod
hvd.init()

# Enable load balancing
hvd.load_balancing()

# Create a model
model = tf.keras.models.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(100,)),
    tf.keras.layers.Dense(1)
])

# Compile the model
model.compile(optimizer='adam',
              loss='mean_squared_error')

# Train the model
model.fit(x, y, batch_size=32, epochs=10)

For more information on load balancing, please refer to the Horovod load balancing guide.

Advanced Tuning

Horovod provides several advanced tuning options to optimize your training performance. Here are some key tuning parameters:

• Batch Size: Adjust the batch size to find the optimal balance between performance and memory usage.
• Optimizer: Choose the right optimizer for your specific problem.
• Learning Rate: Adjust the learning rate to improve convergence.

For more information on advanced tuning, please refer to the Horovod advanced tuning guide.

Conclusion

Horovod is a powerful tool for distributed training of deep learning models. By following this guide, you can leverage Horovod's advanced features to achieve high-performance training on your data.