Unlocking Efficient GPU Utilization with NVIDIA Multi-Instance GPU in Amazon SageMaker HyperPod

Revolutionizing Workloads with GPU Partitioning

Amazon SageMaker HyperPod now supports GPU partitioning using NVIDIA Multi-Instance GPU (MIG), allowing multiple tasks to run concurrently on a single GPU, optimizing resource utilization and improving efficiency in machine learning workloads.

Overview of MIG Capabilities in SageMaker HyperPod

Discover how to maximize GPU efficiency with MIG, enabling isolated task execution while reducing development cycle times and enhancing resource governance across your machine learning projects. Get ready to dive into practical setups and best practices!

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Unlocking the Power of GPU Partitioning with Amazon SageMaker HyperPod and NVIDIA MIG

We are thrilled to announce the general availability of GPU partitioning with Amazon SageMaker HyperPod, utilizing NVIDIA Multi-Instance GPU (MIG). This innovative capability allows multiple tasks to run concurrently on a single GPU, dramatically reducing wasted compute and memory resources. By enabling more users and tasks to simultaneously access GPU resources, organizations can shorten development and deployment cycle times while effectively managing a diverse array of workloads.

The Need for GPU Partitioning

Data scientists frequently engage in various lightweight tasks that do not require the full capabilities of a GPU—whether for serving language models, researching new paradigms, or experimenting in Jupyter notebooks. Cluster administrators face the challenge of enabling diverse teams—data scientists, ML engineers, and infrastructure teams—to run multiple workloads in parallel on the same GPU while ensuring performance isolation and maximizing utilization.

With NVIDIA’s MIG on Amazon SageMaker HyperPod, organizations can allocate GPU resources efficiently, allowing tasks ranging from inference to model prototyping to execute on the same physical hardware concurrently.

Key Benefits of MIG on SageMaker HyperPod

1. Resource Optimization: Simplifies management while maximizing GPU use. Powerful GPUs can be partitioned to cater to smaller workloads without dedicating full resources to tasks that don’t require them.
2. Workload Isolation: Run and manage multiple tasks simultaneously with guaranteed performance, allowing teams to work independently on the same GPU hardware.
3. Cost Efficiency: Rather than dedicating entire GPUs to smaller tasks, organizations can run concurrent workloads, maximizing infrastructure investments.
4. Real-time Observability: Track performance metrics and resource utilization in real time, optimizing the efficiency of GPU partitions.
5. Fine-grained Quota Management: Allocates compute quotas across teams, enhancing resource distribution.

Real-World Applications

Arthur Hussey, from Orbital Materials, shared their experience:

“Using SageMaker HyperPod for inference has significantly increased the efficiency of our cluster by maximizing the number of tasks we can run in parallel. It’s really helped us unlock the full potential of SageMaker HyperPod.”

MIG is especially useful for organizations aiming to allocate high-powered instance resources across various isolated environments. Separate teams can execute models concurrently on the same GPU while ensuring individual resource allocation.

Use Cases Example

1. Resource-Guided Model Serving: Different versions of models can be matched to their appropriately sized MIG instances to deliver optimized performance.
2. Mixed Workloads: Data science teams can efficiently run Jupyter notebooks alongside batch inference pipelines, easily accommodating diverse resource demands.
3. Development and Testing Efficiency: CI/CD pipelines for ML models benefit from isolated testing environments—MIG facilitates quick and efficient testing without requiring additional hardware resources.

How to Set Up MIG on SageMaker HyperPod

Architecture Overview

MIG offers distinct advantages in inference scenarios with predictable latency and cost efficiency. When deploying MIG on a SageMaker HyperPod EKS cluster of 16 ml.p5en.48xlarge instances, administrators can partition GPUs for optimal resource distribution.

MIG Deployment Steps

1. Cluster Setup: Ensure you have a SageMaker HyperPod cluster with Amazon EKS as the orchestrator.
2. MIG Configuration: Using the managed experience, configure the MIG settings through the AWS Management Console or Custom Labels.
3. Incorporate Monitoring: Use HyperPod’s built-in observability features to track GPU utilization and performance metrics during task execution.

Creating MIG Profiles on Kubernetes

For effective migration to MIG, administrators can utilize structured configurations via Kubernetes Custom Resource Definitions (CRDs) like JumpStartModel or DynamoGraphDeployment, depending on their needs. This facilitates optimized multi-model deployment seamlessly, given the diverse workloads running concurrently on the GPU.

Example Workloads

1. Concurrent Inference Workloads: Using SageMaker HyperPod Inference Operator, organizations can smoothly deploy multiple inference workloads that leverage available GPU partitions.
2. Static Deployment for Internal Users: Utilizing specific MIG profiles, organizations can create static deployments that provide compute-heavy tasks and memory-intensive tasks through optimized resource utilization.
3. Interactive Workloads in Jupyter Notebooks: By partitioning resources efficiently, data scientists can conduct experiments using Jupyter notebooks on assigned MIG partitions, preserving isolation and resource efficiency.

Conclusion

The introduction of Multi-Instance GPU (MIG) support with Amazon SageMaker HyperPod facilitates organizations in optimizing their GPU resources while maintaining workload performance. By enabling simultaneous task execution on a single GPU, organizations can significantly reduce infrastructure costs while enhancing overall resource utilization, promoting a collaborative yet isolated workflow.

Begin your journey with MIG on SageMaker HyperPod by diving into the SageMaker HyperPod documentation. Through this innovative technology, unlock the true potential of your GPU resources and foster a more efficient machine learning environment.

Explore more and get started with SageMaker HyperPod today!