Horovod

What it is

Horovod is a distributed deep learning training framework that accelerates the training of machine learning models by leveraging multiple GPUs or machines. It supports popular frameworks like TensorFlow, Keras, PyTorch, and Apache MXNet, making it easier to scale up model training without significant code changes.

Horovod stands out by providing seamless integration with multiple deep learning frameworks, enabling developers to scale their model training without significant changes to existing codebases.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited support for non-GPU environments

   Optimized primarily for GPU clusters, performance on CPU-only setups is suboptimal

2. 02

   Complex setup and configuration

   Requires detailed knowledge of MPI configurations and cluster management tools like Kubernetes or Docker Swarm

What it does well

• Support for multiple deep learning frameworks

• Easy integration with existing training code

• Scalability across multiple GPUs and machines

• Compatibility with popular distributed computing systems like MPI

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.