Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It allows data scientists and developers to manage, scale, and monitor their models in production environments. Seldon Core supports a wide range of machine learning frameworks and provides features such as A/B testing, canary deployments, and advanced metrics.
One common issue encountered when using Seldon Core is the failure of model server health checks. This symptom is typically observed when the Kubernetes readiness or liveness probes fail, leading to the pod being marked as unhealthy. This can cause disruptions in service availability and affect the overall reliability of the deployment.
The root cause of health check failures often lies in the misconfiguration of the health check endpoint or the model server being unresponsive. Seldon Core uses HTTP endpoints to perform health checks, and if these endpoints are not correctly configured or if the server is not responding as expected, the health checks will fail.
For more information on configuring health checks in Kubernetes, refer to the Kubernetes documentation on probes.
Ensure that the health check endpoints are correctly configured in your SeldonDeployment YAML file. The readiness and liveness probes should point to the correct HTTP paths that your model server exposes for health checks.
readinessProbe:
httpGet:
path: /health
port: 8000
livenessProbe:
httpGet:
path: /health
port: 8000
Verify that the model server is running and responsive. You can use tools like curl to manually check the health endpoint:
curl http://<model-server-ip>:8000/health
If the server is unresponsive, check the server logs for any errors or exceptions that might indicate why it is not responding.
If the endpoints are correctly configured and the server is responsive, ensure that your Seldon Core version is up to date. Sometimes, bugs in older versions can cause unexpected behavior. You can update Seldon Core by following the instructions in the Seldon Core installation guide.
After making the necessary changes, monitor the health of your model server to ensure that the issue is resolved. You can use Prometheus and Grafana for monitoring and visualizing metrics related to your deployment.
By ensuring that your health check endpoints are correctly configured and that your model server is responsive, you can resolve health check failures in Seldon Core. Regular monitoring and updates can help maintain the reliability and availability of your machine learning deployments.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)
