March 2022: This repository is no longer maintained

The code for this project was merged into the https://github.com/elyra-ai/elyra repository.

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kfp-notebook implements Kubeflow Pipelines operator NotebookOp that supports processing of notebooks, Python scripts, and R scripts in pipelines.

Building kfp-notebook

bash make clean install

Usage

The example below can easily be added to a python script or jupyter notebook for testing purposes.

```python import os import kfp from kfp_notebook.pipeline import NotebookOp from kubernetes.client.models import V1EnvVar

KubeFlow Pipelines API Endpoint

kfp_url = 'http://dataplatform.ibm.com:32488/pipeline'

S3 Object Storage

cos_endpoint = 'http://s3.us-south.cloud-object-storage.appdomain.cloud' cos_bucket = 'test-bucket' cos_username = 'test' cos_password = 'test123' cos_directory = 'test-directory' cos_dependencies_archive = 'test-archive.tar.gz'

Inputs and Outputs

inputs = [] outputs = []

Container Image

image = 'tensorflow/tensorflow:latest'

def run_notebook_op(op_name, notebook_path):

notebook_op = NotebookOp(name=op_name,
                         notebook=notebook_path,
                         cos_endpoint=cos_endpoint,
                         cos_bucket=cos_bucket,
                         cos_directory=cos_directory,
                         cos_dependencies_archive=cos_dependencies_archive,
                         pipeline_outputs=outputs,
                         pipeline_inputs=inputs,
                         image=image)

notebook_op.container.add_env_variable(V1EnvVar(name='AWS_ACCESS_KEY_ID', value=cos_username))
notebook_op.container.add_env_variable(V1EnvVar(name='AWS_SECRET_ACCESS_KEY', value=cos_password))
notebook_op.container.set_image_pull_policy('Always')

return op

def demo_pipeline(): stats_op = run_notebook_op('stats', 'generate-community-overview') contributions_op = run_notebook_op('contributions', 'generate-community-contributions') run_notebook_op('overview', 'overview').after(stats_op, contributions_op)

Compile the new pipeline

kfp.compiler.Compiler().compile(demo_pipeline,'pipelines/pipeline.tar.gz')

Upload the compiled pipeline

client = kfp.Client(host=kfp_url) pipeline_info = client.upload_pipeline('pipelines/pipeline.tar.gz',pipeline_name='pipeline-demo')

Create a new experiment

experiment = client.create_experiment(name='demo-experiment')

Create a new run associated with experiment and our uploaded pipeline

run = client.run_pipeline(experiment.id, 'demo-run', pipeline_id=pipeline_info.id)

```

Generated Kubeflow Pipelines