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Running distributed AI workloads in production requires solving three problems: package compatibility, hardware abstraction, and network configuration. AWS Neuron Deep Learning Containers (DLCs) address all three by providing open-source, production-ready images for Trainium and Inferentia. This lightning talk shows how DLCs eliminate common failure modes. We'll cover three layers: First, how DLCs solve dependency hell by versioning PyTorch, Neuron SDK, XLA backend, and PyTorch PrivateUse1 dispatcher together as a tested contract. Second, how Dynamic Resource Allocation (DRA) in Kubernetes abstracts hardware complexity—enabling Neuron core slicing, multi-tenant workloads, and topology-aware scheduling without manual device mapping. Third, how pre-configured EFA drivers settings ensure zero-copy data movement, avoiding silent performance degradation that can cost 10x throughput. We'll demonstrate scaling from laptop to 32-node cluster using the same container image and simple Kubernetes manifests. Attendees will learn how to eliminate weeks of setup time, achieve 65-80% cluster utilization, and deploy workloads confidently. We'll share the GitHub repository and extension patterns.
Yahav Biran is a Principal Architect at AWS, focusing on large-scale AI workloads. He contributes to open-source projects and publishes in AWS blogs and academic journals, including the AWS compute and AI blogs and the Journal of Systems Engineering. He frequently delivers technical... Read More →
