Using HPA for Autoscaling
Overview of Horizontal Pod Autoscaler (HPA) scaling options in Seldon Core 2
Given Seldon Core 2 is predominantly for serving ML in Kubernetes, it is possible to leverage HorizontalPodAutoscaler or HPA to define scaling logic that automatically scales up and down Kubernetes resources. HPA targets Kubernetes or custom metrics to trigger scale-up or scale-down events for specified resources. Using HPA is recommnended if custom Scaling Metrics are required. These would be exposed using Prometheus, and Prometheus Adapter or similar tools for explosing metrics to HPA. If these tools cause conflicts, autoscaling functionality native to Core 2 that does not require exposing custom metrics is recommended.
Seldon Core 2 provides two main approaches to leveraging Kubernetes Horizontal Pod Autoscaler (HPA) for autoscaling. It is important to remember that since in Core 2 Models and Servers are separate, autoscaling of both Models and Servers, in a coordinated way, needs to be accounted for when implementing autoscaling. In order to implement either approach, metrics first need to be exposed - this is explained in the HPA Setup guide which explains the fundamental requirements and configuration needed to enable HPA-based scaling in Seldon Core 2.
1. Model Autoscaling with HPA
The Model Autoscaling with HPA approach enables users to scale Models based on custom metrics. This approach, along with Server Autoscaling, enables users to customize the scaling logic for models, and automate the scaling of Servers based on the needs of the Models hosted on them.
2. Model and Server Autoscaling with HPA
The Model and Server Autoscaling with HPA approach leverages HPA to autoscale for Models and Servers in a coordinated way. This requires a 1-1 Mapping of Models and Servers (no Multi-Model Serving). In this case, setting up HPA can be set up for a Model and its associated Server, targetting the same custom metric (this is possible for Kubernetes-native metrics).
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