Name

Component: Data processing by creating a cluster in Cloud Dataproc

Label

Cloud Dataproc, Kubeflow

Facets

Use case: Other

Technique: Other

Input data type: Tabular

ML workflow: Data preparation

Summary

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

Details

Intended use

Use this component at the start of a Kubeflow pipeline to create a temporary Cloud Dataproc cluster to run Cloud Dataproc jobs as steps in the pipeline.

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 to create the cluster in.	No	GCPRegion
name	The name of the cluster. Cluster names within a project must be unique. You can reuse the names of deleted clusters.	Yes	String		None
name_prefix	The prefix of the cluster name.	Yes	String		None
initialization_actions	A list of Cloud Storage URIs identifying the executables on each node after the configuration is completed. By default, executables are run on the master and all the worker nodes.	Yes	List		None
config_bucket	The Cloud Storage bucket to use to stage the job dependencies, the configuration files, and the job driver console’s output.	Yes	GCSPath		None
image_version	The version of the software inside the cluster.	Yes	String		None
cluster	The full cluster configuration.	Yes	Dict		None
wait_interval	The number of seconds to pause before polling the operation.	Yes	Integer		30

Output

Name	Description	Type
cluster_name	The name of the cluster.	String

Note: You can recycle the cluster by using the Dataproc delete cluster component.

Cautions & requirements

To use the component, you must:

• Set up the GCP project by following these steps.
• The component can authenticate to GCP. Refer to Authenticating Pipelines to GCP for details.
• Grant the following types of access to the Kubeflow user service account:
  • Read access to the Cloud Storage buckets which contain the initialization action files.
  • The role, roles/dataproc.editor, on the project.

Detailed description

This component creates a new Dataproc cluster by using the Dataproc create 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_create_cluster_op = comp.load_component_from_url('https://raw.githubusercontent.com/kubeflow/pipelines/1.4.1/components/gcp/dataproc/create_cluster/component.yaml')
   help(dataproc_create_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.

Set sample parameters

# Required parameters
PROJECT_ID = '<Put your project ID here>'

# Optional parameters
EXPERIMENT_NAME = 'Dataproc - Create Cluster'

Example pipeline that uses the component

import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='Dataproc create cluster pipeline',
    description='Dataproc create cluster pipeline'
)
def dataproc_create_cluster_pipeline(
    project_id = PROJECT_ID, 
    region = 'us-central1', 
    name='', 
    name_prefix='',
    initialization_actions='', 
    config_bucket='', 
    image_version='', 
    cluster='', 
    wait_interval='30'
):
    dataproc_create_cluster_op(
        project_id=project_id, 
        region=region, 
        name=name, 
        name_prefix=name_prefix, 
        initialization_actions=initialization_actions, 
        config_bucket=config_bucket, 
        image_version=image_version, 
        cluster=cluster, 
        wait_interval=wait_interval)

Compile the pipeline

#Compile the pipeline
pipeline_func = dataproc_create_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

• Kubernetes Engine for Kubeflow
• Component Python code
• Component Docker file
• Sample notebook
• Dataproc create 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.