42 lines
3.2 KiB
Markdown
42 lines
3.2 KiB
Markdown
# SageMaker Model Explainability Job Definition Kubeflow Pipelines component v2
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## Overview
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Component to create the definition for a model explainability job.
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The component can be used with [Monitoring Schedule component](../MonitoringSchedule) to create a monitoring schedule that regularly starts Amazon SageMaker Processing Jobs to monitor the data captured for an Amazon SageMaker Endpoint.
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See the SageMaker Components for Kubeflow Pipelines versions section in [SageMaker Components for Kubeflow Pipelines](https://docs.aws.amazon.com/sagemaker/latest/dg/kubernetes-sagemaker-components-for-kubeflow-pipelines.html#kubeflow-pipeline-components) to learn about the differences between the version 1 and version 2 components.
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### Kubeflow Pipelines backend compatibility
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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.
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## Getting Started
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Follow [this guide](https://github.com/kubeflow/pipelines/tree/master/samples/contrib/aws-samples#prerequisites) to setup the prerequisites for ModelExplainabilityJobDefinition depending on your deployment.
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## Input Parameters
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Find the high level component input parameters and their description in the [component's input specification](./component.yaml). The parameters with `JsonObject` or `JsonArray` type inputs have nested fields, you will have to refer to the [ModelExplainabilityJobDefinition CRD specification](https://aws-controllers-k8s.github.io/community/reference/sagemaker/v1alpha1/modelexplainabilityjobdefinition/) for the respective structure and pass the input in JSON format.
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A quick way to see the converted JSON style input is to copy the [sample ModelExplainabilityJobDefinition spec](https://aws-controllers-k8s.github.io/community/reference/sagemaker/v1alpha1/modelexplainabilityjobdefinition/#spec) and convert it to JSON using a YAML to JSON converter like [this website](https://jsonformatter.org/yaml-to-json).
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For example, `modelExplainabilityAppSpecification` is of type `object` and has the following structure:
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```
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modelExplainabilityAppSpecification:
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configURI: string
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environment: {}
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imageURI: string
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```
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The JSON style input for the above parameter would be:
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```
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model_explainability_app_specification = {
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"imageURI": "<account-number>.dkr.ecr.<region>.amazonaws.com/sagemaker-clarify-processing:1.0",
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"configURI": "s3://<path-to-file>/analysis_config.json",
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}
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```
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For a more detailed explanation of parameters, please refer to the [AWS SageMaker API Documentation for CreateModelExplainabilityJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModelExplainabilityJobDefinition.html).
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## References
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- [Monitor models for data and model quality, bias, and explainability](https://docs.aws.amazon.com/sagemaker/latest/dg/model-monitor.html)
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- [Model Explainability Job Definition CRD specification](https://aws-controllers-k8s.github.io/community/reference/sagemaker/v1alpha1/modelexplainabilityjobdefinition/)
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- [AWS SageMaker API Documentation for CreateModelExplainabilityJobDefinition](https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateModelExplainabilityJobDefinition.html). |