pipelines/components/gcp/ml_engine/deploy

Name

Deploying a trained model to Cloud Machine Learning Engine

Label

Cloud Storage, Cloud ML Engine, Kubeflow, Pipeline

Summary

A Kubeflow Pipeline component to deploy a trained model from a Cloud Storage location to Cloud ML Engine.

Details

Intended use

Use the component to deploy a trained model to Cloud ML Engine. The deployed model can serve online or batch predictions in a Kubeflow Pipeline.

Runtime arguments

Argument	Description	Optional	Data type	Accepted values	Default
model_uri	The URI of a Cloud Storage directory that contains a trained model file. Or An Estimator export base directory that contains a list of subdirectories named by timestamp. The directory with the latest timestamp is used to load the trained model file.	No	GCSPath
project_id	The ID of the Google Cloud Platform (GCP) project of the serving model.	No	GCPProjectID
model_id	The name of the trained model.	Yes	String		None
version_id	The name of the version of the model. If it is not provided, the operation uses a random name.	Yes	String		None
runtime_version	The Cloud ML Engine runtime version to use for this deployment. If it is not provided, the default stable version, 1.0, is used.	Yes	String		None
python_version	The version of Python used in the prediction. If it is not provided, version 2.7 is used. You can use Python 3.5 if runtime_version is set to 1.4 or above. Python 2.7 works with all supported runtime versions.	Yes	String		2.7
model	The JSON payload of the new model.	Yes	Dict		None
version	The new version of the trained model.	Yes	Dict		None
replace_existing_version	Indicates whether to replace the existing version in case of a conflict (if the same version number is found.)	Yes	Boolean		FALSE
set_default	Indicates whether to set the new version as the default version in the model.	Yes	Boolean		FALSE
wait_interval	The number of seconds to wait in case the operation has a long run time.	Yes	Integer		30

Input data schema

The component looks for a trained model in the location specified by the model_uri runtime argument. The accepted trained models are:

• Tensorflow SavedModel
• Scikit-learn & XGBoost model

The accepted file formats are:

• *.pb
• *.pbtext
• model.bst
• model.joblib
• model.pkl

model_uri can also be an Estimator export base directory, which contains a list of subdirectories named by timestamp. The directory with the latest timestamp is used to load the trained model file.

Output

Name	Description	Type
model_uri	The Cloud Storage URI of the trained model.	GCSPath
model_name	The name of the deployed model.	String
version_name	The name of the deployed version.	String

Cautions & requirements

To use the component, you must:

• Set up the cloud environment.
• The component can authenticate to GCP. Refer to Authenticating Pipelines to GCP for details.
• Grant read access to the Cloud Storage bucket that contains the trained model to the Kubeflow user service account.

Detailed description

Use the component to:

• Locate the trained model at the Cloud Storage location you specify.
• Create a new model if a model provided by you doesn’t exist.
• Delete the existing model version if replace_existing_version is enabled.
• Create a new version of the model from the trained model.
• Set the new version as the default version of the model if set_default is enabled.

Follow these steps to use the component in a pipeline:

1. Install the Kubeflow Pipeline SDK:

%%capture --no-stderr

!pip3 install kfp --upgrade

2. Load the component using KFP SDK

import kfp.components as comp

mlengine_deploy_op = comp.load_component_from_url(
    'https://raw.githubusercontent.com/kubeflow/pipelines/1.6.0/components/gcp/ml_engine/deploy/component.yaml')
help(mlengine_deploy_op)

Sample

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

In this sample, you deploy a pre-built trained model from gs://ml-pipeline-playground/samples/ml_engine/census/trained_model/ to Cloud ML Engine. The deployed model is kfp_sample_model. A new version is created every time the sample is run, and the latest version is set as the default version of the deployed model.

Set sample parameters

# Required Parameters
PROJECT_ID = '<Please put your project ID here>'

# Optional Parameters
EXPERIMENT_NAME = 'CLOUDML - Deploy'
TRAINED_MODEL_PATH = 'gs://ml-pipeline-playground/samples/ml_engine/census/trained_model/'

Example pipeline that uses the component

import kfp.dsl as dsl
import json
@dsl.pipeline(
    name='CloudML deploy pipeline',
    description='CloudML deploy pipeline'
)
def pipeline(
    model_uri = 'gs://ml-pipeline-playground/samples/ml_engine/census/trained_model/',
    project_id = PROJECT_ID,
    model_id = 'kfp_sample_model',
    version_id = '',
    runtime_version = '1.10',
    python_version = '',
    version = {},
    replace_existing_version = 'False',
    set_default = 'True',
    wait_interval = '30'):
    task = mlengine_deploy_op(
        model_uri=model_uri, 
        project_id=project_id, 
        model_id=model_id, 
        version_id=version_id, 
        runtime_version=runtime_version, 
        python_version=python_version,
        version=version, 
        replace_existing_version=replace_existing_version, 
        set_default=set_default, 
        wait_interval=wait_interval)

Compile the pipeline

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

#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)

References

• Component python code
• Component docker file
• Sample notebook
• Cloud Machine Learning Engine Model REST API
• Cloud Machine Learning Engine Version 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.