Merge branch 'master' into add-new-regions-to-jsonfile

Author: kenny-ezirim

CHANGELOG.md

@@ -1,5 +1,57 @@
 # Changelog
 
+## v2.119.0 (2022-12-03)
+
+### Features
+
+ * Add Code Owners file
+ * Added transform with monitoring pipeline step in transformer
+ * Update TF 2.9 and TF 2.10 inference DLCs
+ * make estimator accept json file as modelparallel config
+ * SageMaker Training Compiler does not support p4de instances
+ * Add support for SparkML v3.3
+
+### Bug Fixes and Other Changes
+
+ * Fix bug forcing uploaded tar to be named sourcedir
+ * Update local_requirements.txt PyYAML version
+ * refactoring : using with statement
+ * Allow Py 3.7 for MMS Test Docker env
+ * fix PySparkProcessor __init__ params type
+ * type hint of PySparkProcessor __init__
+ * Return ARM XGB/SKLearn tags if `image_scope` is `inference_graviton`
+ * Update scipy to 1.7.3 to support M1 development envs
+ * Fixing type hints for Spark processor that has instance type/count params in reverse order
+ * Add DeepAR ap-northeast-3 repository.
+ * Fix AsyncInferenceConfig documentation typo
+ * fix ml_inf to ml_inf1 in Neo multi-version support
+ * Fix type annotations
+ * add neo mvp region accounts
+
+## v2.118.0 (2022-12-01)
+
+### Features
+
+ * Update boto3 version to 1.26.20
+ * support table format option for create feature group.
+ * Support Amazon SageMaker Model Cards
+ * support monitoring alerts api
+ * Support Amazon SageMaker AutoMLStep
+
+### Bug Fixes and Other Changes
+
+ * integration test in anticipate of ProfilerConfig API changes
+ * Add more integ test logic for AutoMLStep
+ * update get_execution_role_arn to use role from DefaultSpaceSettings
+ * bug on AutoMLInput to allow PipelineVariable
+ * FinalMetricDataList is missing from the training job search resu…
+ * add integration tests for Model Card
+ * update AutoMLStep with cache improvement
+
+### Documentation Changes
+
+ * automlstep doc update
+
 ## v2.117.0 (2022-11-15)
 
 ### Features

CODEOWNERS

@@ -0,0 +1 @@
+* @aws/sagemaker-ml-frameworks

README.rst

@@ -214,7 +214,7 @@ In order to host a SparkML model in SageMaker, it should be serialized with ``ML
 
 For more information on MLeap, see https://github.com/combust/mleap .
 
-Supported major version of Spark: 2.4 (MLeap version - 0.9.6)
+Supported major version of Spark: 3.3 (MLeap version - 0.20.0)
 
 Here is an example on how to create an instance of  ``SparkMLModel`` class and use ``deploy()`` method to create an
 endpoint which can be used to perform prediction against your trained SparkML Model.

VERSION

@@ -1 +1 @@
-2.117.1.dev0
+2.119.1.dev0

doc/amazon_sagemaker_model_building_pipeline.rst

@@ -362,6 +362,52 @@ Example:
         prefix=model_prefix
     )
 
+AutoMLStep
+`````````````
+Referable Property List:
+
+- `DescribeAutoMLJob`_
+- `BestCandidateProperties.ModelInsightsJsonReportPath`_
+- `BestCandidateProperties.ExplainabilityJsonReportPath`_
+
+.. _DescribeAutoMLJob: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_DescribeAutoMLJob
+.. _BestCandidateProperties.ModelInsightsJsonReportPath: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CandidateArtifactLocations.html#sagemaker-Type-CandidateArtifactLocations-ModelInsights
+.. _BestCandidateProperties.ExplainabilityJsonReportPath: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CandidateArtifactLocations.html#sagemaker-Type-CandidateArtifactLocations-Explainability
+
+Example:
+
+.. code-block:: python
+
+    step_automl = AutoMLStep(...)
+
+    auto_ml_model = step_automl.get_best_model(<role>)
+
+    model_metrics = ModelMetrics(
+        model_statistics=MetricsSource(
+            s3_uri=auto_ml_step.properties.BestCandidateProperties.ModelInsightsJsonReportPath,
+            content_type="application/json",
+        ),
+        explainability=MetricsSource(
+            s3_uri=auto_ml_step.properties.BestCandidateProperties.ExplainabilityJsonReportPath,
+            content_type="application/json",
+        )
+    )
+
+    step_args_register_model = auto_ml_model.register(
+    content_types=["text/csv"],
+    response_types=["text/csv"],
+    inference_instances=["ml.t2.medium", "ml.m5.large"],
+    transform_instances=["ml.m5.large"],
+    model_package_group_name="My model package group name",
+    approval_status="PendingManualApproval",
+    model_metrics=model_metrics,
+    )
+
+    step_register_model = ModelStep(
+        name="auto-ml-model-register",
+        step_args=step_args_register_model,
+    )
+
 CreateModelStep
 ````````````````
 Referable Property List:

doc/api/governance/index.rst

@@ -0,0 +1,9 @@
+###############
+Governance APIs
+###############
+
+.. toctree::
+   :maxdepth: 1
+   :glob:
+
+   *

doc/api/governance/model_card.rst

@@ -0,0 +1,43 @@
+Amazon SageMaker Model Cards
+----------------------------
+
+For more information about using model cards with the SageMaker Python SDK,
+see `Amazon SageMaker Model Cards <https://docs.aws.amazon.com/sagemaker/latest/dg/model-cards.html>`__ in the *Amazon SageMaker Developer Guide*.
+
+.. currentmodule:: sagemaker.model_card
+
+.. autoclass:: AdditionalInformation
+    :show-inheritance:
+
+.. autoclass:: Environment
+    :show-inheritance:
+
+.. autoclass:: EvaluationJob
+    :show-inheritance:
+
+.. autoclass:: Function
+    :show-inheritance:
+
+.. autoclass:: IntendedUses
+    :show-inheritance:
+
+.. autoclass:: Metric
+    :show-inheritance:
+
+.. autoclass:: MetricGroup
+    :show-inheritance:
+
+.. autoclass:: ModelCard
+    :show-inheritance:
+
+.. autoclass:: ModelOverview
+    :show-inheritance:
+
+.. autoclass:: ObjectiveFunction
+    :show-inheritance:
+
+.. autoclass:: TrainingDetails
+    :show-inheritance:
+
+.. autoclass:: TrainingJobDetails
+    :show-inheritance:

doc/api/index.rst

@@ -11,4 +11,5 @@ The SageMaker Python SDK consists of a variety classes for preparing data, train
     training/index
     training/distributed
     inference/index
+    governance/index
     utility/index

doc/overview.rst

@@ -1171,7 +1171,7 @@ be ``s3://sagemaker-{REGION}-{ACCOUNTID}/async-endpoint-outputs/{UNIQUE-JOB-NAME
     from sagemaker.async_inference import AsyncInferenceConfig
 
     # Create an empty AsyncInferenceConfig object to use default values
-    async_config = new AsyncInferenceConfig()
+    async_config = AsyncInferenceConfig()
 
 Or you can specify configurations in ``AsyncInferenceConfig`` as you like. All of those configuration parameters
 are optional but if you don’t specify the ``output_path``, Amazon SageMaker will use the default ``S3OutputPath``
@@ -1180,7 +1180,7 @@ mentioned above (example shown below):
 .. code:: python
 
     # Specify S3OutputPath, MaxConcurrentInvocationsPerInstance and NotificationConfig in the async config object
-    async_config = new AsyncInferenceConfig(
+    async_config = AsyncInferenceConfig(
         output_path="s3://{s3_bucket}/{bucket_prefix}/output",
         max_concurrent_invocations_per_instance=10,
         notification_config = {

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

@@ -168,3 +168,5 @@ Steps
 .. autoclass:: sagemaker.workflow.clarify_check_step.ClarifyCheckStep
 
 .. autoclass:: sagemaker.workflow.fail_step.FailStep
+
+.. autoclass:: sagemaker.workflow.automl_step.AutoMLStep

requirements/extras/scipy_requirements.txt

@@ -1 +1 @@
-scipy==1.7.2
+scipy==1.7.3

setup.py

@@ -48,7 +48,7 @@ def read_requirements(filename):
 # Declare minimal set for installation
 required_packages = [
     "attrs>=20.3.0,<23",
-    "boto3>=1.20.21,<2.0",
+    "boto3>=1.26.20,<2.0",
     "google-pasta",
     "numpy>=1.9.0,<2.0",
     "protobuf>=3.1,<4.0",