Why Teams Like OpenAI and NVIDIA Choose Horovod for Large-Scale Training

Here's a simple explanation of Horovod for a non-technical audience:

Horovod is an open-source library that helps distributed training of deep learning models run faster and scale better.

Let's say I'm training an image recognition model on a single GPU. It works, but takes a long time to train because the data and model are large.

Horovod allows me to easily spread the training across multiple GPUs on different servers to parallelize the work.

For example:

This simple change allows each GPU to train on a different slice of the data simultaneously.

Under the hood, Horovod uses an efficient algorithm called ring-allreduce to average all the gradient updates across the GPUs after each batch.

By training in parallel on many GPUs, I can speed up the training significantly while training on larger datasets and bigger models.

Horovod abstracts away the complex coordination logic so I can focus on my model code rather than infrastructure.

It provides these benefits without major code changes. This simplicity and scalability is why teams like OpenAI and NVIDIA use Horovod for their large-scale distributed training.