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This article provides a comprehensive overview of Horovod, Uber's open-source distributed deep learning framework, covering its architecture, communication mechanisms, performance benchmarks, and deployment on Kubernetes and Spark for accelerated multi-GPU training.
This article reviews Horovod, a popular third‑party distributed deep‑learning training plugin, explaining its simple three‑line integration, the challenges of deadlocks in all‑reduce operations, and the architectural components—including background threads, coordinators, and MPI/Gloo controllers—that enable scalable and efficient data‑parallel training.
WeChat’s Scan‑to‑Identify system now trains its CNN models across multiple GPUs using Horovod’s data‑parallel, synchronous Ring All‑Reduce architecture built on MPI and NCCL, cutting training time from several days to under one day while maintaining accuracy, and future work will target I/O and further scaling.