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Introduction
In today’s rapidly evolving landscape of artificial intelligence (AI), training large language models (LLMs) poses significant challenges. These models often require enormous computational resources and sophisticated infrastructure to handle the vast amounts of data and complex algorithms involved. Without a structured framework, the process can become prohibitively time-consuming, costly, and complex. Enterprises struggle with managing distributed training workloads, efficient resource utilization, and model accuracy and performance. This is where the NVIDIA NeMo Framework comes into play.
NVIDIA NeMo Framework
NVIDIA NeMo is an end-to-end cloud-centered framework for training and deploying generative AI models with billions and trillions of parameters at scale. The NVIDIA NeMo Framework provides a comprehensive set of tools, scripts, and recipes to support each stage of the LLM journey, from data preparation to training and deployment. It offers a variety of customization techniques and is optimized for at-scale inference of models for both language and image applications, using multi-GPU and multi-node configurations. NVIDIA NeMo simplifies generative AI model development, making it more cost-effective and efficient for enterprises. By providing end-to-end pipelines, advanced parallelism techniques, memory-saving strategies, and distributed checkpointing, NVIDIA NeMo makes sure AI model training is streamlined, scalable, and high-performing.
Solution Overview
You can deploy and manage NVIDIA NeMo using either Slurm or Kubernetes orchestration platforms. Amazon EKS is a managed Kubernetes service that makes it straightforward to run Kubernetes clusters on AWS. It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications.
Prerequisites
You need to be able to launch a CPU-based Amazon Elastic Compute Cloud (Amazon EC2) instance that you’ll use to create the EKS cluster. When your instance is up and running, SSH into your EC2 instance and install the following CLIs:
Launch an EKS Cluster
ECR p4de.24xlarge instances have the NVIDIA A100 80GB instances, which are highly popular for distributed training generative AI workloads. For more information, refer to Amazon EC2 Instance Types. In this section, we show how to create an EKS cluster with an On-Demand Capacity Reservation for p4de.24xlarge instances.
Troubleshooting Deployment Failures
If deployment fails due to incorrect setup or configuration, complete the following debug steps:
Clean Up
It’s important to spin down resources after model training in order to avoid costs associated with running idle instances. To clean up our setup, we must delete the FSx for Lustre file system before deleting the cluster because it’s associated with a subnet in the cluster’s VPC.
About the Authors
Ankur Srivastava is a Sr. Solutions Architect in the ML Frameworks Team. He focuses on helping customers with self-managed distributed training and inference at scale on AWS. His experience includes industrial predictive maintenance, digital twins, probabilistic design optimization and has completed his doctoral studies from Mechanical Engineering at Rice University and post-doctoral research from Massachusetts Institute of Technology.
Akshit Arora is a senior data scientist at NVIDIA, where he works on deploying conversational AI models on GPUs at scale. He’s a graduate of University of Colorado at Boulder, where he applied deep learning to improve knowledge tracking on a K-12 online tutoring platform. His work spans multilingual text-to-speech, time series classification, ed-tech, and practical applications of deep learning.
Eliuth Triana Isaza is a Developer Relations Manager at NVIDIA empowering Amazon’s AI MLOps, DevOps, Scientists and AWS technical experts to master the NVIDIA computing stack for accelerating and optimizing Generative AI Foundation models spanning from data curation, GPU training, model inference and production deployment on AWS GPU instances.
Wenhan Tan is a Solutions Architect at Nvidia assisting customers to adopt Nvidia AI solutions at large-scale. His work focuses on accelerating deep learning applications and addressing inference and training challenges.
Frequently Asked Questions
Question 1: What is the NVIDIA NeMo Framework?
The NVIDIA NeMo Framework is an end-to-end cloud-centered framework for training and deploying generative AI models with billions and trillions of parameters at scale.
Question 2: What are the benefits of using NVIDIA NeMo?
NVIDIA NeMo provides a comprehensive set of tools, scripts, and recipes to support each stage of the LLM journey, from data preparation to training and deployment. It offers a variety of customization techniques and is optimized for at-scale inference of models for both language and image applications.
Question 3: What is Amazon EKS?
Amazon EKS is a managed Kubernetes service that makes it straightforward to run Kubernetes clusters on AWS. It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications.
Question 4: What are the prerequisites for launching an EKS cluster?
You need to be able to launch a CPU-based Amazon Elastic Compute Cloud (Amazon EC2) instance that you’ll use to create the EKS cluster. When your instance is up and running, SSH into your EC2 instance and install the following CLIs.
Question 5: How do I troubleshoot deployment failures?
If deployment fails due to incorrect setup or configuration, complete the following debug steps: find the error message, stop any running jobs, and delete the Helm chart.
Conclusion
In this post, we demonstrated how to train generative AI models at scale using the NeMo Framework within an EKS cluster. We covered the challenges of training LLMs and how NeMo’s comprehensive tools and optimizations address these challenges, making the process more efficient and cost-effective. With NeMo, you can manage and scale distributed training workloads effectively.
