Seldon Core Model versioning not working

What is Seldon Core Model versioning not working

Understanding Seldon Core

Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It allows data scientists and engineers to manage, scale, and monitor machine learning models in production environments. Seldon Core supports various machine learning frameworks and provides advanced features like model versioning, canary deployments, and A/B testing.

Identifying the Symptom

When working with Seldon Core, you might encounter an issue where model versioning does not work as expected. This symptom is typically observed when deploying multiple versions of a model, but the system fails to differentiate between them, leading to incorrect routing of requests or serving outdated models.

Exploring the Issue

Root Cause Analysis

The primary cause of this issue is often related to incorrect version labels or annotations in the SeldonDeployment YAML configuration. These labels and annotations are crucial for Seldon Core to identify and manage different versions of a model.

Common Mistakes

Common mistakes include missing version labels, incorrect syntax, or misconfigured annotations that do not align with the expected format. These errors prevent Seldon Core from recognizing and routing traffic to the correct model version.

Steps to Fix the Issue

Verify Version Labels

Ensure that each model version in your SeldonDeployment YAML has a unique version label. For example:

apiVersion: machinelearning.seldon.io/v1kind: SeldonDeploymentmetadata: name: my-modelspec: predictors: - name: default replicas: 1 componentSpecs: - spec: containers: - name: classifier image: mymodel:1.0 env: - name: MODEL_VERSION value: "1.0" labels: version: "1.0"

Ensure that the version label is correctly set for each model version.

Check Annotations

Annotations can also be used to specify model versions. Verify that annotations are correctly configured:

metadata: annotations: seldon.io/engine: "1.0"

Ensure that the annotation values match the intended model version.

Deploy and Test

After updating the YAML configuration, apply the changes using the following command:

kubectl apply -f seldondeployment.yaml

Monitor the deployment to ensure that the correct model version is being served. You can use Seldon Core Analytics to verify the traffic routing and model performance.

Conclusion

By ensuring that version labels and annotations are correctly set in your SeldonDeployment YAML, you can effectively manage and deploy multiple versions of your machine learning models using Seldon Core. For more detailed information, refer to the Seldon Core Documentation.