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README.md
SageMaker Model Explainability Job Definition Kubeflow Pipelines component v2
Overview
Component to create the definition for a model explainability job.
The component can be used with Monitoring Schedule component to create a monitoring schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
See the SageMaker Components for Kubeflow Pipelines versions section in SageMaker Components for Kubeflow Pipelines to learn about the differences between the version 1 and version 2 components.
Kubeflow Pipelines backend compatibility
SageMaker components are currently supported with Kubeflow pipelines backend v1. This means, you will have to use KFP sdk 1.8.x to create your pipelines.
Getting Started
Follow this guide to setup the prerequisites for ModelExplainabilityJobDefinition depending on your deployment.
Input Parameters
Find the high level component input parameters and their description in the component's input specification. The parameters with JsonObject
or JsonArray
type inputs have nested fields, you will have to refer to the ModelExplainabilityJobDefinition CRD specification for the respective structure and pass the input in JSON format.
A quick way to see the converted JSON style input is to copy the sample ModelExplainabilityJobDefinition spec and convert it to JSON using a YAML to JSON converter like this website.
For example, modelExplainabilityAppSpecification
is of type object
and has the following structure:
modelExplainabilityAppSpecification:
configURI: string
environment: {}
imageURI: string
The JSON style input for the above parameter would be:
model_explainability_app_specification = {
"imageURI": "<account-number>.dkr.ecr.<region>.amazonaws.com/sagemaker-clarify-processing:1.0",
"configURI": "s3://<path-to-file>/analysis_config.json",
}
For a more detailed explanation of parameters, please refer to the AWS SageMaker API Documentation for CreateModelExplainabilityJobDefinition.