KubeFlow Pipelines Best Practices

Production-proven patterns for CI/CD integration, pipeline versioning, caching, error handling, and multi-team ML collaboration.

Pipeline Caching

KFP caches component outputs by default. When a component is re-run with the same inputs, it returns cached results instead of re-executing:

# Disable caching for a specific task
train_task = train_model(dataset=data)
train_task.set_caching_options(False)  # Always re-run this step

Error Handling & Retries

@dsl.pipeline
def robust_pipeline():
    train_task = train_model(data="gs://bucket/data.csv")
    train_task.set_retry(
        num_retries=3,
        backoff_duration="60s",
        backoff_factor=2.0
    )

    # Exit handler runs regardless of pipeline success/failure
    with dsl.ExitHandler(cleanup_resources()):
        evaluate_task = evaluate_model(model=train_task.output)

CI/CD Integration

# GitHub Actions example for KFP CI/CD
# .github/workflows/pipeline-deploy.yml
name: Deploy ML Pipeline
on:
  push:
    branches: [main]
    paths: ["pipelines/**"]
jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
    - uses: actions/checkout@v4
    - run: pip install kfp==2.7.0
    - run: python pipelines/compile.py  # Compile pipeline to YAML
    - run: python pipelines/deploy.py   # Upload to KFP server

Production Checklist

• Version everything: Pipeline code, component images, data schemas, and model artifacts.
• Use namespaces: Separate dev, staging, and production pipelines into Kubernetes namespaces.
• Set resource limits: Always specify CPU, memory, and GPU limits on every component.
• Enable caching wisely: Cache data processing steps but disable for training with randomness.
• Monitor pipeline health: Track run duration, failure rates, and resource utilization over time.
• Document pipelines: Add descriptions to pipelines and components for team discoverability.