Kubeflow Kale

April 27, 2026 · View on GitHub

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Turn Jupyter Notebooks into ML Pipelines — In One Click

No pipeline code. No SDK learning curve. Just tag your cells and deploy.

What is Kale?

KALE (Kubeflow Automated pipeLines Engine) is a tool designed to simplify the deployment of Kubeflow Pipelines workflows.

You've built an amazing ML model in a Jupyter notebook. Now you need to run it in production, schedule it, or scale it up. Usually that means rewriting everything as a Kubeflow Pipeline — learning the SDK, restructuring your code, and debugging YAML.

Kale eliminates all of that.

Tag your notebook cells with simple labels like imports, step, or skip. Kale analyzes your code, detects dependencies between steps, and generates a production-ready Kubeflow Pipeline. Your notebook stays exactly as it is.

Kale JupyterLab Extension

Why Data Scientists Love Kale

✅ push-button pipeline generation (tag cells, click "compile")
✅ automatic dependency detection
✅ same notebook for dev and production
✅ create pipelines directly in notebook without looking at YAML
✅ requires no direct knowledge of KFP SDK
✅ no rewriting code into pipeline components

📖 Documentation: https://kale.kubeflow.org

Python 3.11+
Kubeflow Pipelines v2.16.0+ (install guide)
Kubernetes cluster (minikube, kind, or any K8s cluster)

Quick Install

# Clone and set up (v2.0 coming soon to PyPI)
git clone https://github.com/kubeflow-kale/kale.git
cd kale && make dev

# Launch JupyterLab with Kale
make jupyter

Your First Pipeline in 60 Seconds

Open any notebook from examples
Click the Kale icon in the left sidebar
Toggle Enable to see your notebook as a pipeline graph
Click Compile and Run — that's it!

# Or use the CLI
kale --nb examples/base/candies_sharing.ipynb --kfp_host http://localhost:8080 --run_pipeline

How It Works

Tag your notebook cells to define pipeline structure:

Cell Tag	What It Does
`imports`	Libraries needed by all steps (pandas, sklearn, etc.)
`functions`	Helper functions available to all steps
`pipeline-parameters`	Variables users can tune between runs
`pipeline-metrics`	Metrics to track in the KFP UI
`step:step_name`	A pipeline step (Kale auto-detects dependencies!)
`skip`	Exploratory code to exclude from the pipeline

Learn more about cell types in Kale documentation.

Pro tip: Kale automatically detects which variables flow between steps. You don't need to specify inputs and outputs — just write natural notebook code.

Check out the example notebooks to see these in action.

Installation

Make it sure that Kubeflow Pipelines is running and acessible to Kale

Install from PyPi:

pip install "jupyterlab>=4.0.0" kubeflow-kale[jupyter]
jupyter lab

Local Development

You can install Kale directly from the sources:

git clone https://github.com/kubeflow-kale/kale.git
make clean && make dev && make jupyter-kfp

See CONTRIBUTING.md for detailed setup instructions.

Local Development with Docker

Run Kale locally with Docker:

make docker-build   # Build the image
make docker-run     # JupyterLab at http://localhost:8889

Connect to a real KFP cluster:

make kfp-serve                                              # Serve dev wheel
kubectl port-forward -n kubeflow svc/ml-pipeline 8080:8888  # Forward KFP API
make docker-run                                             # Start container