Seldon Core is an open-source platform designed to deploy, scale, and manage machine learning models on Kubernetes. It provides a robust infrastructure for serving models in production environments, ensuring that they are scalable and reliable. By leveraging Kubernetes, Seldon Core allows for seamless integration and management of machine learning workflows.
When deploying models with Seldon Core, you might encounter issues where the model server fails to start, or the deployment does not behave as expected. Common symptoms include:
One of the primary causes of deployment issues in Seldon Core is incorrect configuration settings. This can include errors in the YAML configuration files, incorrect resource allocations, or missing environment variables. These misconfigurations can prevent the model server from initializing correctly.
To resolve deployment issues in Seldon Core, follow these steps:
kubectl logs <pod-name>
to view the logs of the affected pods. Look for error messages that indicate what might be going wrong.kubectl get nodes
and kubectl get pods
to check the status of your cluster.kubectl apply -f <your-config-file>.yaml
.For more detailed troubleshooting steps and community support, consider visiting the Seldon Core GitHub Issues page or the Seldon Community Forum.
(Perfect for DevOps & SREs)
(Perfect for DevOps & SREs)