Accelerate your generative AI distributed training workloads with the NVIDIA NeMo Framework on Amazon EKS
Machine Learning Blog
This article provides a step-by-step guide for running distributed generative AI model training workloads on Amazon Elastic Kubernetes Service (EKS) using the NVIDIA NeMo Framework. It covers the challenges of training large language models and how NeMo addresses them with its comprehensive tools and optimizations.
Specifically, the article covers:
- Overview of the NVIDIA NeMo Framework and its benefits for distributed training
- Setting up an EFA-enabled EKS cluster with P4de instances and FSx for Lustre file system
- Configuring the environment with NVIDIA device plugin, KubeFlow operators, and NeMo
- Modifying NeMo's Kubernetes manifests for data preparation and model training
- Running data preparation and training jobs on the EKS cluster
- Monitoring and managing the training process
- Troubleshooting and cleanup steps
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