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VOOZH | about |
As cloud-native applications grow in scale and sophistication, the ability to dynamically allocate computing resources becomes critical—especially for workloads that require GPUs. Kubernetes provides built-in autoscaling, but it often falls short when you need to scale based on external or custom metrics. This is where KEDA (Kubernetes Event-Driven Autoscaling) excels. KEDA allows your Kubernetes workloads to scale based on real-time metrics from sources like Prometheus.
In this tutorial, we’ll walk through how to autoscale an AMD GPU-based workload running on DigitalOcean Kubernetes (DOKS) using Prometheus and KEDA. This setup allows you to react to live metrics and optimize GPU utilization efficiently and cost-effectively.
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I help Businesses scale with AI x SEO x (authentic) Content that revives traffic and keeps leads flowing | 3,000,000+ Average monthly readers on Medium | Sr Technical Writer(Team Lead) @ DigitalOcean | Ex-Cloud Consultant @ AMEX | Ex-Site Reliability Engineer(DevOps)@Nutanix
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