Horovod

Horovod is an open-source distributed deep learning framework developed by Uber for training neural networks across multiple GPUs and nodes. It uses the Message Passing Interface (MPI) model to enable efficient data-parallel training, significantly reducing communication overhead and improving scalability. It is designed to work with popular deep learning frameworks like TensorFlow, PyTorch, and Keras, making it easier to scale training jobs in high-performance computing environments.

Also known as: Horovod Distributed Training, Uber Horovod, Horovod MPI, Horovod Framework, Horovod DL

Why learn Horovod?

Developers should learn Horovod when they need to accelerate deep learning training on large datasets or complex models by distributing workloads across multiple GPUs or machines, such as in research, production AI systems, or cloud-based training pipelines. It is particularly useful for scenarios requiring high scalability, like training large language models or computer vision networks, as it minimizes communication bottlenecks and integrates seamlessly with existing deep learning workflows.

Learning Resources

• docs: Horovod Official Documentation
• docs: Horovod GitHub Repository
• tutorial: Distributed Deep Learning with Horovod (Uber Engineering Blog)
• video: Horovod Tutorial on YouTube
• tutorial: Hands-On Guide to Horovod (Towards Data Science Article)