Horovod

What is Horovod?

Horovod is an open-source framework designed to simplify the process of training deep learning models in a distributed environment. Originally developed by Uber, Horovod leverages the Message Passing Interface (MPI) to enable efficient communication between multiple machines (nodes) and GPUs. It is particularly well-suited for scaling TensorFlow, Keras, PyTorch, and Apache MXNet models across multiple GPUs and nodes. By reducing the complexity of writing custom distributed training code, Horovod allows data scientists and machine learning engineers to focus more on model development and less on infrastructure. Its primary advantage is the ability to significantly speed up the training process, making it easier to handle large datasets and more complex models. Horovod achieves this by using a ring-allreduce algorithm, which reduces the overhead of synchronizing gradients between nodes, thereby improving training efficiency.

An open-source distributed deep learning framework.

Examples

• Uber: Uber's engineering team uses Horovod to train their deep learning models for applications like self-driving cars and ride prediction. By using Horovod, they have managed to scale their training workloads across multiple GPUs and nodes, reducing training times significantly.


• NVIDIA: NVIDIA employs Horovod to optimize the performance of their deep learning models. By integrating Horovod with their GPUs, NVIDIA has achieved faster training times and improved model accuracy, which is crucial for their various AI-driven products and services.

Additional Information

• Horovod supports popular deep learning frameworks like TensorFlow, Keras, PyTorch, and Apache MXNet.


• It uses the ring-allreduce algorithm to minimize communication overhead during distributed training.