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Name

Batch prediction using Cloud Machine Learning Engine

Label

Cloud Storage, Cloud ML Engine, Kubeflow, Pipeline, Component

Summary

A Kubeflow Pipeline component to submit a batch prediction job against a deployed model on Cloud ML Engine.

Details

Intended use

Use the component to run a batch prediction job against a deployed model on Cloud ML Engine. The prediction output is stored in a Cloud Storage bucket.

Runtime arguments

Argument Description Optional Data type Accepted values Default
project_id The ID of the Google Cloud Platform (GCP) project of the job. No GCPProjectID
model_path The path to the model. It can be one of the following:
  • projects/[PROJECT_ID]/models/[MODEL_ID]
  • projects/[PROJECT_ID]/models/[MODEL_ID]/versions/[VERSION_ID]
  • The path to a Cloud Storage location containing a model file.
 No GCSPath
input_paths The path to the Cloud Storage location containing the input data files. It can contain wildcards, for example, gs://foo/*.csv No List GCSPath
input_data_format The format of the input data files. See REST Resource: projects.jobs for more details. No String DataFormat
output_path The path to the Cloud Storage location for the output data. No GCSPath
region The Compute Engine region where the prediction job is run. No GCPRegion
output_data_format The format of the output data files. See REST Resource: projects.jobs for more details. Yes String DataFormat JSON
prediction_input The JSON input parameters to create a prediction job. See PredictionInput for more information. Yes Dict None
job_id_prefix The prefix of the generated job id. Yes String None
wait_interval The number of seconds to wait in case the operation has a long run time. Yes 30

Input data schema

The component accepts the following as input:

  • A trained model: It can be a model file in Cloud Storage, a deployed model, or a version in Cloud ML Engine. Specify the path to the model in the model_pathruntime argument.
  • Input data: The data used to make predictions against the trained model. The data can be in multiple formats. The data path is specified by input_paths and the format is specified by input_data_format.

Output

Name Description Type
job_id The ID of the created batch job. String
output_path The output path of the batch prediction job GCSPath

Cautions & requirements

To use the component, you must:

  • Set up a cloud environment by following this guide.
  • 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 contains the input data.
    • Write access to the Cloud Storage bucket of the output directory.

Detailed description

Follow these steps to use the component in a pipeline:

  1. Install the Kubeflow Pipeline SDK:




In [ ]:



    


%%capture --no-stderr

KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.14/kfp.tar.gz'
!pip3 install $KFP_PACKAGE --upgrade
  1. Load the component using KFP SDK




In [ ]:



    


import kfp.components as comp

mlengine_batch_predict_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/01a23ae8672d3b18e88adf3036071496aca3552d/components/gcp/ml_engine/batch_predict/component.yaml')
help(mlengine_batch_predict_op)

Sample Code

Note: The following sample code works in an IPython notebook or directly in Python code.

In this sample, you batch predict against a pre-built trained model from gs://ml-pipeline-playground/samples/ml_engine/census/trained_model/ and use the test data from gs://ml-pipeline-playground/samples/ml_engine/census/test.json.

Inspect the test data





In [ ]:



    


!gsutil cat gs://ml-pipeline-playground/samples/ml_engine/census/test.json

Set sample parameters





In [ ]:



    


# Required Parameters
PROJECT_ID = '<Please put your project ID here>'
GCS_WORKING_DIR = 'gs://<Please put your GCS path here>' # No ending slash



    











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# Optional Parameters
EXPERIMENT_NAME = 'CLOUDML - Batch Predict'
OUTPUT_GCS_PATH = GCS_WORKING_DIR + '/batch_predict/output/'

Example pipeline that uses the component





In [ ]:



    


import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='CloudML batch predict pipeline',
    description='CloudML batch predict pipeline'
)
def pipeline(
    project_id = PROJECT_ID, 
    model_path = 'gs://ml-pipeline-playground/samples/ml_engine/census/trained_model/', 
    input_paths = '["gs://ml-pipeline-playground/samples/ml_engine/census/test.json"]', 
    input_data_format = 'JSON', 
    output_path = OUTPUT_GCS_PATH, 
    region = 'us-central1', 
    output_data_format='', 
    prediction_input = json.dumps({
        'runtimeVersion': '1.10'
    }), 
    job_id_prefix='',
    wait_interval='30'):
        mlengine_batch_predict_op(
            project_id=project_id, 
            model_path=model_path, 
            input_paths=input_paths, 
            input_data_format=input_data_format, 
            output_path=output_path, 
            region=region, 
            output_data_format=output_data_format, 
            prediction_input=prediction_input, 
            job_id_prefix=job_id_prefix,
            wait_interval=wait_interval)

Compile the pipeline





In [ ]:



    


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

Submit the pipeline for execution





In [ ]:



    


#Specify pipeline argument values
arguments = {}

#Get or create an experiment and submit a pipeline run
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)

Inspect prediction results





In [ ]:



    


OUTPUT_FILES_PATTERN = OUTPUT_GCS_PATH + '*'
!gsutil cat OUTPUT_FILES_PATTERN

References

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.

Content source: kubeflow/kfp-tekton-backend

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