Horovod is a distributed deep learning framework and gradient synchronizer designed to scale model training across multiple GPUs and compute nodes. It functions as a distributed training orchestrator and an elastic training engine, utilizing an MPI collective communication library to synchronize weights and gradients across TensorFlow, PyTorch, Keras, and MXNet models.
The system distinguishes itself through dynamic elastic scaling, which allows it to adjust the number of active workers at runtime and recover from node failures. It optimizes communication efficiency using tensor fusion batching and half-precision gradient compression to reduce network bandwidth requirements.
The framework covers a broad set of capabilities including cluster orchestration across Kubernetes, Spark, and Ray, as well as hardware-aware resource mapping for CPUs and GPUs. It provides tools for distributed data management, such as parallel loading from Parquet files and offloaded preprocessing. Performance is further supported by RDMA network optimization, execution tracing, and Bayesian training optimization to maximize throughput.
Deployment is supported through containerized training images and orchestrated environments for high-performance compute clusters.