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Troubleshooting

Common issues and solutions when working with Kubeflow.

Common Issues​

1. Pipeline Execution Fails​

Problem: Pipeline fails with "ImagePullBackOff"

Solution:

# Check pod status
kubectl get pods -n kubeflow

# Describe the failing pod
kubectl describe pod <pod-name> -n kubeflow

# Ensure image exists and is accessible
# For private registries, create image pull secret
kubectl create secret docker-registry regcred \
  --docker-server=<registry-url> \
  --docker-username=<username> \
  --docker-password=<password> \
  -n kubeflow

2. Model Serving Issues​

Problem: InferenceService not ready

Solution:

# Check InferenceService status
kubectl get inferenceservice -n ml-serving

# Check predictor pods
kubectl get pods -n ml-serving -l serving.kserve.io/inferenceservice=churn-predictor

# View logs
kubectl logs -n ml-serving <predictor-pod-name>

# Describe InferenceService for events
kubectl describe inferenceservice churn-predictor -n ml-serving

3. Out of Resources​

Problem: Pods stuck in "Pending" state

Solution:

# Check resource availability
kubectl describe node

# Check resource requests
kubectl get pods -n kubeflow -o jsonpath='{range .items[*]}{.metadata.name}{"\t"}{.spec.containers[*].resources.requests}{"\n"}{end}'

# Scale cluster or adjust resource requests

Next Steps​

After completing this tutorial, explore:

  • Advanced Pipelines: Build complex pipelines with conditional execution and loops
  • Custom Training Operators: Create operators for custom frameworks
  • Multi-Model Serving: Deploy multiple models in a single service
  • AutoML Integration: Integrate AutoML tools like AutoKeras or Auto-sklearn
  • Edge Deployment: Deploy models to edge devices using KubeEdge
  • Model Explainability: Add SHAP or LIME for model interpretation
  • Continuous Training: Set up automated retraining pipelines

Resources​

Conclusion​

Kubeflow provides a comprehensive platform for building production-ready ML systems on Kubernetes. By following this tutorial, you've learned how to:

  • Set up Kubeflow on different platforms
  • Build end-to-end ML pipelines
  • Deploy models for serving
  • Monitor and manage ML workloads
  • Apply MLOps best practices

Start small with basic pipelines and gradually incorporate more advanced features as your needs grow. The key to success with Kubeflow is treating ML workflows as code, enabling reproducibility, collaboration, and continuous improvement.