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https://github.com/ghndrx/kubeflow-pipelines.git
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Greg Hendrickson
4ff491f847
- Switch to self-hosted runner on compute-01 for faster builds - Replace PyTDC with curated DDI dataset (no heavy deps) - 60+ real drug interaction patterns based on clinical guidelines - Generates up to 10K training samples with text variations - Maintains 5-level severity classification
Kubeflow Pipelines - GitOps Repository
This repository contains ML pipeline definitions managed via ArgoCD.
Structure
.
├── pipelines/ # Pipeline Python definitions
│ └── examples/ # Example pipelines
├── components/ # Reusable pipeline components
├── experiments/ # Experiment configurations
├── runs/ # Scheduled/triggered runs
└── manifests/ # K8s manifests for ArgoCD
Usage
- Add a pipeline: Create a Python file in
pipelines/ - Push to main: ArgoCD auto-deploys
- Monitor: Check Kubeflow UI at <KUBEFLOW_URL>
Quick Start
from kfp import dsl
@dsl.component
def hello_world() -> str:
return "Hello from Kubeflow!"
@dsl.pipeline(name="hello-pipeline")
def hello_pipeline():
hello_world()
Environment
- Kubeflow: <KUBEFLOW_URL>
- MinIO: <MINIO_URL>
- ArgoCD: <ARGOCD_URL>