Project Title

horovod — Distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet

Overview

Horovod is an open-source distributed deep learning training framework designed to simplify and accelerate the process of scaling deep learning models across multiple GPUs and servers. It is built on top of the MPI model, making it straightforward to use and requiring minimal code changes for distributed training. Horovod is known for its ease of use and high performance, achieving near-linear scaling efficiency on large clusters.

Key Features

Seamless scaling of deep learning models across multiple GPUs and servers
Support for popular deep learning frameworks: TensorFlow, Keras, PyTorch, and Apache MXNet
Built on MPI for simplicity and efficiency in distributed training
High scalability and performance, with 90% efficiency on 512-GPU benchmarks

Use Cases

Researchers and data scientists needing to train large-scale deep learning models on multiple GPUs or clusters
Enterprises looking to accelerate machine learning model development and deployment
Educational institutions teaching distributed deep learning concepts

Advantages

Easy to integrate with existing single-GPU training scripts
Minimal code changes required for distributed training
High performance and scalability, with efficient use of resources
Actively maintained and supported by the LF AI & Data Foundation

Limitations / Considerations

May require additional setup and configuration for distributed environments
Performance can be affected by network latency and hardware limitations in large-scale deployments

TensorFlow's Distributed Strategy: A built-in solution for distributed training in TensorFlow, but may require more code changes compared to Horovod.
PyTorch Distributed: PyTorch's native solution for distributed training, which is also easy to use but might not offer the same level of performance as Horovod in certain scenarios.
Apache MXNet's SageMaker Distributed Training: A distributed training feature of MXNet, tailored for AWS SageMaker, but not as widely applicable as Horovod across different frameworks and environments.

Basic Information

GitHub: https://github.com/horovod/horovod
Stars: 14,543
License: Unknown
Last Commit: 2025-07-16

📊 Project Information

Project Name: horovod
GitHub URL: https://github.com/horovod/horovod
Programming Language: Python
⭐ Stars: 14,543
🍴 Forks: 2,258
📅 Created: 2017-08-09
🔄 Last Updated: 2025-07-16

🏷️ Project Topics

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horovod

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