Unleashing AI Innovation with NVIDIA DGX Cloud on AWS
This post is co-written with Andrew Liu, Chelsea Isaac, Zoey Zhang, and Charlie Huang from NVIDIA.
Introduction to DGX Cloud on AWS
As artificial intelligence (AI) continues to evolve, the demand for high-performance computing infrastructure has become paramount. Enter NVIDIA DGX Cloud on Amazon Web Services (AWS)—a collaborative effort intended to democratize access to advanced AI tools and resources. By blending NVIDIA’s GPU power with AWS’s scalable cloud services, this platform empowers organizations to reduce operational complexity, speed up model training, and uncover new business potentials. It’s not just a service; it’s a foundation for AI innovation.
The Power of NVIDIA DGX Cloud
The DGX Cloud on AWS presents a fully managed AI training platform that enables organizations to rapidly deploy generative and agentic AI solutions. With features such as flexible access to large GPU clusters and optimized training times, it makes AI development more efficient from the get-go. Available through the AWS Marketplace, this service is a strategic alliance between NVIDIA and AWS that offers businesses cutting-edge tools, including the latest NVIDIA architectures like the Amazon EC2 P6e-GB200 UltraServer. This setup ensures that enterprises leverage superior technology while enjoying expert support around the clock.
Seamless Integration with Amazon Bedrock Custom Model Import
Alongside DGX Cloud, Amazon Bedrock provides a fully managed service that’s pivotal for developing generative AI applications. Its robust features offer various high-performance foundation models from leading AI firms—all through a single API. By using Amazon Bedrock Custom Model Import, organizations can easily tailor foundation models with their own data and deploy them for serverless inference, further streamlining the AI development process.
Architecture Overview of DGX Cloud on AWS
At its core, DGX Cloud utilizes p5.48xlarge instances, each equipped with eight H100 GPUs, substantial NVMe storage, and an impressive network setup configured for lower latency and optimal performance. This architecture organizes node instances in a manner that maximizes GPU utilization, creating a seamless experience for AI/ML workloads.
Additionally, the platform leverages Amazon Elastic Kubernetes Service (EKS) and NVIDIA’s specialized tools to ensure seamless deployment and scalability. This infrastructure, coupled with Amazon FSx for Lustre for high-performance storage, provides organizations the capacity to manage and manipulate large datasets effectively.
Setting Up Your DGX Cloud Environment
After gaining access to your DGX Cloud cluster, the first step involves establishing departments and projects for workload execution. The initial setup can include a default department, while project-specific arrangements allow for refined quota management. User allocation becomes simple and efficient, enabling quick commencement of AI workloads.
Fine-tuning the Llama 3.1 Model
Once the environment is operational, developers can dive into fine-tuning the Llama 3.1-70b model. This process involves using a Jupyter notebook workspace to preprocess data and manage code effectively. With at least eight GPUs and sufficient storage allocated via Amazon FSx for Lustre, teams can make significant strides in model training.
By accessing the Hugging Face model repository and leveraging tools like the NVIDIA NeMo framework, developers can fine-tune the model to respond precisely to user-generated instructions. The "daring-anteater" dataset provides a wide-ranging basis for instruction tuning, enhancing the model’s adaptability.
Launching the Training Job
To kick off the fine-tuning process, users initiate a training job via the NeMo-Run script utilizing specified resources within the cluster. With four H100 nodes in action, it’s possible to track GPU and memory utilization, gaining insights into performance metrics seamlessly. This comprehensive overview empowers developers to refine their approach as needed.
Importing the Custom Model to Amazon Bedrock
After completing the tuning process, the next step is to import the custom model into Amazon Bedrock. This streamlined method begins with navigating to the Bedrock console, where users can define model parameters and import settings, including specifying an S3 bucket for their model files.
By following a few straightforward steps within the console, users can efficiently manage their custom models, ensuring they leverage the full capabilities of Bedrock—ranging from encryption options through AWS KMS to IAM role assignments for comprehensive security and data management.
Running Inference through Amazon Bedrock
The true power of an AI model shines during inference, and Amazon Bedrock’s playground provides an interactive interface for testing and fine-tuning configurations. Users can select their imported custom model, submit prompts, and receive outputs in real-time, allowing for iterative development and validation.
Efficient Resource Management
Cleaning up resources post-experimentation is crucial in maintaining lean operational expenditures. Users can simply delete their imported models and related infrastructure to prevent ongoing costs. This functionality underscores the efficiency of utilizing AWS services, ensuring organizations remain agile and responsive to changing needs.
In summary, NVIDIA DGX Cloud on AWS, paired with Amazon Bedrock, establishes a powerful ecosystem for organizations aiming to advance their AI capabilities. This integrated platform not only streamlines the development workflow but also enables rapid deployment, fostering an environment ripe for exploration and innovation in AI solutions.