Seldon Core Canary deployment not functioning

What is Seldon Core Canary deployment not functioning

Understanding Seldon Core

Seldon Core is an open-source platform designed to deploy machine learning models on Kubernetes. It provides a robust framework for managing, scaling, and monitoring machine learning models in production environments. One of its key features is the ability to perform canary deployments, which allows you to test new versions of a model with a small percentage of traffic before a full rollout.

Identifying the Symptom

When a canary deployment is not functioning as expected in Seldon Core, you might observe that the traffic is not being split between the main and canary models. This can result in the canary model not receiving any traffic, making it impossible to test its performance effectively.

Exploring the Issue

The primary issue with a non-functioning canary deployment often lies in the traffic split configuration or the absence of a proper canary definition. In Seldon Core, traffic splitting is managed through the traffic field in the SeldonDeployment resource. If this field is not configured correctly, the traffic will not be routed as intended.

Common Misconfigurations

Incorrect percentage values in the traffic split configuration. Omission of the canary model definition in the deployment YAML.

Checking the Deployment YAML

Ensure that the canary model is defined correctly in the SeldonDeployment YAML file. The canary model should be listed under the predictors section with a specified traffic percentage.

Steps to Fix the Issue

Step 1: Verify Traffic Split Configuration

Check the traffic field in your SeldonDeployment YAML file. Ensure that the sum of traffic percentages for all models equals 100. For example:

apiVersion: machinelearning.seldon.io/v1kind: SeldonDeploymentmetadata: name: my-modelspec: predictors: - name: main-model graph: name: classifier implementation: SKLEARN_SERVER modelUri: gs://my-bucket/model traffic: 80 - name: canary-model graph: name: classifier implementation: SKLEARN_SERVER modelUri: gs://my-bucket/canary-model traffic: 20

Step 2: Ensure Canary Definition

Make sure that the canary model is properly defined in the predictors section. Each model should have a unique name and a valid model URI.

Step 3: Apply the Configuration

After verifying and correcting the YAML configuration, apply the changes using the following command:

kubectl apply -f my-seldondeployment.yaml

Step 4: Monitor the Deployment

Use Seldon Core's monitoring tools to ensure that traffic is being split as expected. You can check the logs and metrics to verify that the canary model is receiving the correct percentage of traffic.

Additional Resources

For more detailed information on Seldon Core and canary deployments, refer to the following resources:

Seldon Core Documentation Kubernetes Concepts