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CHANGELOG.md

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# Changelog
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## v2.118.0 (2022-12-01)
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### Features
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* Update boto3 version to 1.26.20
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* support table format option for create feature group.
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* Support Amazon SageMaker Model Cards
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* support monitoring alerts api
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* Support Amazon SageMaker AutoMLStep
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### Bug Fixes and Other Changes
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* integration test in anticipate of ProfilerConfig API changes
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* Add more integ test logic for AutoMLStep
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* update get_execution_role_arn to use role from DefaultSpaceSettings
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* bug on AutoMLInput to allow PipelineVariable
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* FinalMetricDataList is missing from the training job search resu…
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* add integration tests for Model Card
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* update AutoMLStep with cache improvement
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### Documentation Changes
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* automlstep doc update
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## v2.117.0 (2022-11-15)
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### Features

VERSION

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2.117.1.dev0
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2.118.1.dev0

doc/amazon_sagemaker_model_building_pipeline.rst

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prefix=model_prefix
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)
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AutoMLStep
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`````````````
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Referable Property List:
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- `DescribeAutoMLJob`_
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- `BestCandidateProperties.ModelInsightsJsonReportPath`_
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- `BestCandidateProperties.ExplainabilityJsonReportPath`_
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.. _DescribeAutoMLJob: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob
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.. _BestCandidateProperties.ModelInsightsJsonReportPath: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CandidateArtifactLocations.html#sagemaker-Type-CandidateArtifactLocations-ModelInsights
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.. _BestCandidateProperties.ExplainabilityJsonReportPath: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CandidateArtifactLocations.html#sagemaker-Type-CandidateArtifactLocations-Explainability
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Example:
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.. code-block:: python
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step_automl = AutoMLStep(...)
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auto_ml_model = step_automl.get_best_model(<role>)
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model_metrics = ModelMetrics(
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model_statistics=MetricsSource(
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s3_uri=auto_ml_step.properties.BestCandidateProperties.ModelInsightsJsonReportPath,
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content_type="application/json",
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),
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explainability=MetricsSource(
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s3_uri=auto_ml_step.properties.BestCandidateProperties.ExplainabilityJsonReportPath,
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content_type="application/json",
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)
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)
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step_args_register_model = auto_ml_model.register(
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content_types=["text/csv"],
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response_types=["text/csv"],
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inference_instances=["ml.t2.medium", "ml.m5.large"],
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transform_instances=["ml.m5.large"],
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model_package_group_name="My model package group name",
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approval_status="PendingManualApproval",
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model_metrics=model_metrics,
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)
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step_register_model = ModelStep(
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name="auto-ml-model-register",
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step_args=step_args_register_model,
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)
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CreateModelStep
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````````````````
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Referable Property List:

doc/api/governance/index.rst

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###############
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Governance APIs
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###############
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.. toctree::
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:maxdepth: 1
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:glob:
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*

doc/api/governance/model_card.rst

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Amazon SageMaker Model Cards
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----------------------------
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For more information about using model cards with the SageMaker Python SDK,
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see `Amazon SageMaker Model Cards <https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html>`__ in the *Amazon SageMaker Developer Guide*.
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.. currentmodule:: sagemaker.model_card
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.. autoclass:: AdditionalInformation
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:show-inheritance:
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.. autoclass:: Environment
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:show-inheritance:
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.. autoclass:: EvaluationJob
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:show-inheritance:
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.. autoclass:: Function
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:show-inheritance:
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.. autoclass:: IntendedUses
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:show-inheritance:
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.. autoclass:: Metric
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:show-inheritance:
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.. autoclass:: MetricGroup
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:show-inheritance:
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.. autoclass:: ModelCard
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:show-inheritance:
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.. autoclass:: ModelOverview
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:show-inheritance:
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.. autoclass:: ObjectiveFunction
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:show-inheritance:
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.. autoclass:: TrainingDetails
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:show-inheritance:
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.. autoclass:: TrainingJobDetails
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:show-inheritance:

doc/api/index.rst

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training/index
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training/distributed
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inference/index
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governance/index
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utility/index

doc/workflows/pipelines/sagemaker.workflow.pipelines.rst

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.. autoclass:: sagemaker.workflow.clarify_check_step.ClarifyCheckStep
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.. autoclass:: sagemaker.workflow.fail_step.FailStep
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.. autoclass:: sagemaker.workflow.automl_step.AutoMLStep

setup.py

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# Declare minimal set for installation
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required_packages = [
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"attrs>=20.3.0,<23",
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"boto3>=1.20.21,<2.0",
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"boto3>=1.26.20,<2.0",
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"google-pasta",
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"numpy>=1.9.0,<2.0",
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"protobuf>=3.1,<4.0",

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