Terrill Dicki January 24, 2025 14:36
Explore Nvidia’s approach to horizontal automation of NIM microservices on Kubernetes and leverage custom metrics for efficient resource management.
Nvidia has introduced a comprehensive approach to horizontally automating NIM microservices in Kubernetes, as detailed by Juana Nakfour on the Nvidia Developer blog. This method leverages Kubernetes Horizontal Pod Autoscaling (HPA) to dynamically adjust resources based on custom metrics to optimize compute and memory usage.
Understanding nvidia nim microservices
The nvidia nim microservice serves as a model inference container that can be deployed on Kubernetes, which is important for managing large-scale machine learning models. These microservices require a clear understanding of their compute and memory profiles in production environments to ensure efficient automation.
Autofocus setup
The process begins with setting up a Kubernetes cluster equipped with key components such as Kubernetes Metrics Server, Prometheus, Prometheus Adapter, and Grafana. These tools are essential for reducing and displaying the metrics required for HPA services.
Kubernetes Metrics Server collects resource metrics from Kubelets and exposes them through the Kubernetes API server. Prometheus and Grafana are employed to scrape metrics from pods and create dashboards, while the Prometheus adapter allows HPAs to leverage custom metrics for scaling strategies.
Deploying NIM microservices
NVIDIA provides a detailed guide for deploying NIM microservices, specifically using the LLMS model of NIM. This involves setting up the necessary infrastructure and ensuring that the LLMS microservice’s NIM is ready to scale based on GPU cache usage metrics.
Grafana dashboards visualize these custom metrics and facilitate monitoring and adjusting resource allocation based on traffic and workload demands. The deployment process involves generating traffic using tools such as Genai-PERF. This is useful for evaluating the impact of different concurrency levels on resource utilization.
Implementing horizontal pod automation
To implement HPA, NVIDIA will demonstrate the creation of HPA resources focused on the GPU_CACHE_USAGE_PERC metric. By running load tests at different concurrency levels, HPA automatically adjusts the number of pods to maintain optimal performance and shows its effectiveness in handling varying workloads.
Future outlook
Nvidia’s approach paves the way for further investigation, including scaling based on multiple metrics such as request latency and GPU compute utilization. Additionally, leverage Prometheus Query Language (PROMQL) to create new metrics to enhance your automation capabilities.
For more detailed insights, visit the NVIDIA developer blog.
Image source: ShutterStock
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