Below are the list of samples that are currently running end to end taking the compiled Tekton yaml and deploying on a Tekton cluster directly. If you are interested more in the larger list of pipelines samples we are testing for whether they can be 'compiled to Tekton' format, please look at the corresponding status page
KFP Tekton User Guide is a guideline for the possible ways to develop and consume Kubeflow Pipeline with Tekton. It's recommended to go over at least one of the methods in the user guide before heading into the KFP Tekton Samples.
- Install Kubeflow 1.0.2+ and connect the cluster to the current shell with
kubectl - Install Tekton 0.16.3
- For KFP, we shouldn't be modifying the default work directory for any component. Therefore, please run the below command to disable the home and work directories overwrite from Tekton default.
kubectl patch cm feature-flags -n tekton-pipelines -p '{"data":{"disable-home-env-overwrite":"true","disable-working-directory-overwrite":"true"}}'
- For KFP, we shouldn't be modifying the default work directory for any component. Therefore, please run the below command to disable the home and work directories overwrite from Tekton default.
- Install Kubeflow pipeline with Tekton backend
- Install kfp-tekton SDK
# Set up the python virtual environment python3 -m venv .venv source .venv/bin/activate # Install the kfp-tekton SDK pip install kfp-tekton
- MNIST End to End example with Kubeflow components
- Hyperparameter tuning using Katib
- Trusted AI Pipeline with AI Fairness 360 and Adversarial Robustness 360 components
- Training and Serving Models with Watson Machine Learning
- Lightweight python components example
- The flip-coin pipeline
- Nested pipeline example