Horovod is an open-source library for distributed training of deep learning models. It simplifies the process of scaling TensorFlow, Keras, and PyTorch models to multiple GPUs, multiple machines, and even across multiple clouds.

Key Features

  • Ease of Use: Horovod provides a simple API that allows you to easily distribute your training across multiple GPUs, machines, or clouds.
  • Scalability: With Horovod, you can scale your training to any size cluster, from a single machine to thousands of nodes.
  • Performance: Horovod achieves high performance by using efficient communication algorithms and avoiding the overhead of traditional data parallelism techniques.

Getting Started

To get started with Horovod, you first need to install it. You can do so using pip:

pip install horovod

For detailed installation instructions and dependencies, please visit the Horovod installation guide.

Usage

Here's a simple example of using Horovod with TensorFlow:

import tensorflow as tf
from horovod.tensorflow import Horovod

with Horovod() as hvd:
    model = tf.keras.models.Sequential([
        tf.keras.layers.Dense(10, activation='relu', input_shape=(100,)),
        tf.keras.layers.Dense(1)
    ])
    model.compile(optimizer='adam',
                  loss='mean_squared_error')

    # ... training code ...

For more examples and detailed usage instructions, please refer to the Horovod documentation.

Community and Support

Horovod has a vibrant community of users and contributors. If you have questions or need support, you can join the Horovod Slack channel.

For more information on contributing to Horovod, please visit the Horovod GitHub repository.

Resources

For further reading on distributed deep learning, you may also be interested in Understanding Distributed Deep Learning.

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