April 7, 2025Distributed Training in MLOps Break GPU Vendor Lock-In: Distributed MLOps across mixed AMD and NVIDIA Clusters
This third article in the series on Distributed MLOps explores overcoming vendor lock-in by unifying AMD and NVIDIA GPUs in mixed clusters for distributed PyTorch training, all without requiring code rewrites:
Mixing GPU Vendors: It demonstrates how to combine AWS g4ad (AMD) and g4dn (NVIDIA) instances, bridging ROCm and CUDA to avoid being tied to a single vendor.
High-Performance Communication: It highlights the use of UCC and UCX to enable efficient operations like all_reduce and all_gather, ensuring smooth and synchronized training across diverse GPUs.
Kubernetes Made Simple: How Kubernetes, enhanced by Volcano for gang scheduling, can orchestrate these workloads on heterogeneous GPU setups.
Real-World Trade-Offs: While covering techniques like dynamic load balancing and gradient compression, it also notes challenges current limitations.
Overall, the piece illustrates how integrating mixed hardware can maximize resource potential, delivering faster, scalable, and cost-effective machine learning training.
