Seldon Core Model server not scaling
HPA (Horizontal Pod Autoscaler) misconfiguration or missing metrics.
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What is
Seldon Core Model server not scaling
?
Understanding Seldon Core
Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It provides a robust infrastructure for scaling, monitoring, and managing machine learning models in production environments. By leveraging Kubernetes, Seldon Core allows for seamless scaling of model servers based on demand, ensuring efficient resource utilization and high availability.
Identifying the Symptom: Model Server Not Scaling
One common issue users encounter is the model server not scaling as expected. This can manifest as a lack of additional pods being created under increased load, leading to performance bottlenecks and degraded service quality. Users may notice that despite increased traffic, the number of replicas remains constant.
Exploring the Issue: HPA Misconfiguration or Missing Metrics
The root cause of the model server not scaling is often linked to the Horizontal Pod Autoscaler (HPA) misconfiguration or missing metrics. The HPA is responsible for automatically adjusting the number of pods in a deployment based on observed CPU utilization or other select metrics. If the HPA is not configured correctly or if the necessary metrics are unavailable, scaling will not occur.
Common Misconfigurations
- Incorrect target metrics or thresholds set in the HPA configuration.
- Metrics server not installed or not functioning properly.
- Resource requests and limits not defined, leading to inaccurate CPU utilization metrics.
Steps to Fix the Issue
To resolve the issue of the model server not scaling, follow these steps:
Step 1: Verify HPA Configuration
Check the HPA configuration to ensure it is set up correctly. Use the following command to view the current HPA settings:
kubectl get hpa -n <namespace>
Ensure that the target metrics and thresholds align with your scaling requirements.
Step 2: Ensure Metrics Server is Running
The metrics server must be running for the HPA to function. Verify its status with:
kubectl get pods -n kube-system | grep metrics-server
If the metrics server is not running, install it using the following guide: Metrics Server Installation.
Step 3: Define Resource Requests and Limits
Ensure that your deployments have resource requests and limits defined. This allows the HPA to accurately measure CPU utilization. Update your deployment YAML files as needed:
resources:
requests:
cpu: "100m"
limits:
cpu: "500m"
Step 4: Monitor and Test Scaling
After making the necessary changes, monitor the scaling behavior under load. You can use tools like k6 to simulate traffic and observe how the HPA responds.
Conclusion
By ensuring the correct configuration of the HPA and the availability of necessary metrics, you can effectively resolve issues related to model server scaling in Seldon Core. Regular monitoring and testing will help maintain optimal performance and resource utilization in your Kubernetes environment.
Attached error:
Seldon Core Model server not scaling
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