pipelines/components/gcp/dataproc/delete_cluster

Name

Component: Data preparation by deleting a cluster in Cloud Dataproc

Label

Cloud Dataproc, Kubeflow

Summary

A Kubeflow pipeline component to delete a cluster in Cloud Dataproc.

Intended use

Use this component at the start of a Kubeflow pipeline to delete a temporary Cloud Dataproc cluster when running Cloud Dataproc jobs as steps in the pipeline. This component is usually used with an exit handler to run at the end of a pipeline.

Facets

Use case:

Technique:

Input data type:

ML workflow:

Runtime arguments

Argument	Description	Optional	Data type	Accepted values	Default
project_id	The Google Cloud Platform (GCP) project ID that the cluster belongs to.	No	GCPProjectID	-	-
region	The Cloud Dataproc region in which to handle the request.	No	GCPRegion	-	-
name	The name of the cluster to delete.	No	String	-	-
wait_interval	The number of seconds to pause between polling the operation.	Yes	Integer	-	30

Cautions & requirements

To use the component, you must:

• Set up a GCP project by following this guide.
• The component can authenticate to GCP. Refer to Authenticating Pipelines to GCP for details.
• Grant the Kubeflow user service account the role, roles/dataproc.editor, on the project.

Detailed description

This component deletes a Dataproc cluster by using Dataproc delete cluster REST API.

Follow these steps to use the component in a pipeline:

1. Install the Kubeflow pipeline's SDK:

   %%capture --no-stderr
   
   !pip3 install kfp --upgrade
   

2. Load the component using the Kubeflow pipeline's SDK:

   import kfp.components as comp
   
   dataproc_delete_cluster_op = comp.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/1.4.0-rc.1/components/gcp/dataproc/delete_cluster/component.yaml')
   help(dataproc_delete_cluster_op)
   

Sample

The following sample code works in an IPython notebook or directly in Python code. See the sample code below to learn how to execute the template.

Prerequisites

Create a Dataproc cluster before running the sample code.

Set sample parameters

PROJECT_ID = '<Put your project ID here>'
CLUSTER_NAME = '<Put your existing cluster name here>'

REGION = 'us-central1'
EXPERIMENT_NAME = 'Dataproc - Delete Cluster'

Example pipeline that uses the component

import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='Dataproc delete cluster pipeline',
    description='Dataproc delete cluster pipeline'
)
def dataproc_delete_cluster_pipeline(
    project_id = PROJECT_ID, 
    region = REGION,
    name = CLUSTER_NAME
):
    dataproc_delete_cluster_op(
        project_id=project_id, 
        region=region, 
        name=name)

Compile the pipeline

pipeline_func = dataproc_delete_cluster_pipeline
pipeline_filename = pipeline_func.__name__ + '.zip'
import kfp.compiler as compiler
compiler.Compiler().compile(pipeline_func, pipeline_filename)

Submit the pipeline for execution

#Specify values for the pipeline's arguments
arguments = {}

#Get or create an experiment
import kfp
client = kfp.Client()
experiment = client.create_experiment(EXPERIMENT_NAME)

#Submit a pipeline run
run_name = pipeline_func.__name__ + ' run'
run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)

References

• Component Python code
• Component Docker file
• Sample notebook
• Dataproc delete cluster REST API

License

By deploying or using this software you agree to comply with the AI Hub Terms of Service and the Google APIs Terms of Service. To the extent of a direct conflict of terms, the AI Hub Terms of Service will control.