Merge branch 'master' into fix/fw-processor-normargs

Author: jeniyat

CHANGELOG.md

@@ -1,5 +1,65 @@
 # Changelog
 
+## v2.67.0 (2021-11-01)
+
+### Deprecations and Removals
+
+ * deprecate Serverless Lambda model-predictor
+
+### Features
+
+ * add joinsource to DataConfig
+ * Add support for Partial Dependence Plots(PDP) in SageMaker Clarify
+
+### Bug Fixes and Other Changes
+
+ * localmode subprocess parent process not sending SIGTERM to child
+ * remove buildspec from repo
+
+## v2.66.2.post0 (2021-10-28)
+
+### Documentation Changes
+
+ * Update estimator docstrings to add Fast File Mode
+
+## v2.66.2 (2021-10-27)
+
+### Bug Fixes and Other Changes
+
+ * expose num_clusters parameter for clarify shap in shapconfig
+ * Update cron job to run hourly
+
+## v2.66.1 (2021-10-26)
+
+### Bug Fixes and Other Changes
+
+ * HuggingFace image_uri generation for inference
+ * Update '_' and '/' with '-' in filename creation
+
+## v2.66.0 (2021-10-25)
+
+### Features
+
+ * Add image_uris.retrieve() support for AutoGluon
+
+### Documentation Changes
+
+ * fix documentation for input types in estimator.fit
+ * Add JsonGet v2 deprecation
+
+## v2.65.0 (2021-10-21)
+
+### Features
+
+ * modify RLEstimator to use newly generated Ray image (1.6.0)
+ * network isolation mode for xgboost
+ * update clarify imageURI for PDT
+
+### Bug Fixes and Other Changes
+
+ * retry downstream_trials test
+ * Add retries to pipeline execution
+
 ## v2.64.0 (2021-10-20)
 
 ### Deprecations and Removals

VERSION

@@ -1 +1 @@
-2.64.1.dev0
+2.67.1.dev0

buildspec-deploy.yml

@@ -13,7 +13,9 @@
 from __future__ import absolute_import
 
 import os
+import re
 import time
+
 import boto3
 
 account = boto3.client(
@@ -23,9 +25,11 @@
 
 
 def queue_build():
-    build_id = os.environ.get("CODEBUILD_BUILD_ID", "CODEBUILD-BUILD-ID")
-    source_version = os.environ.get("CODEBUILD_SOURCE_VERSION", "CODEBUILD-SOURCE-VERSION").replace(
-        "/", "-"
+    build_id = re.sub("[_/]", "-", os.environ.get("CODEBUILD_BUILD_ID", "CODEBUILD-BUILD-ID"))
+    source_version = re.sub(
+        "[_/]",
+        "-",
+        os.environ.get("CODEBUILD_SOURCE_VERSION", "CODEBUILD-SOURCE-VERSION"),
     )
     ticket_number = int(1000 * time.time())
     filename = "%s_%s_%s" % (ticket_number, build_id, source_version)

buildspec-localmodetests.yml

@@ -16,7 +16,3 @@ Model
     :undoc-members:
     :show-inheritance:
 
-.. autoclass:: sagemaker.serverless.model.LambdaModel
-    :members:
-    :undoc-members:
-    :show-inheritance:

buildspec-notebooktests.yml

@@ -7,8 +7,3 @@ Make real-time predictions against SageMaker endpoints with Python objects
     :members:
     :undoc-members:
     :show-inheritance:
-
-.. autoclass:: sagemaker.serverless.predictor.LambdaPredictor
-    :members:
-    :undoc-members:
-    :show-inheritance:

buildspec-release.yml

@@ -1063,50 +1063,6 @@ You can also find these notebooks in the **Advanced Functionality** section of t
 For information about using sample notebooks in a SageMaker notebook instance, see `Use Example Notebooks <https://docs.aws.amazon.com/sagemaker/latest/dg/howitworks-nbexamples.html>`__
 in the AWS documentation.
 
-********************
-Serverless Inference
-********************
-
-You can use the SageMaker Python SDK to perform serverless inference on Lambda.
-
-To deploy models to Lambda, you must complete the following prerequisites:
-
-- `Package your model and inference code as a container image. <https://docs.aws.amazon.com/lambda/latest/dg/images-create.html>`_
-- `Create a role that lists Lambda as a trusted entity. <https://docs.aws.amazon.com/lambda/latest/dg/lambda-intro-execution-role.html#permissions-executionrole-console>`_
-
-After completing the prerequisites, you can deploy your model to Lambda using
-the `LambdaModel`_ class.
-
-.. code:: python
-
-   from sagemaker.serverless import LambdaModel
-
-   image_uri = "123456789012.dkr.ecr.us-west-2.amazonaws.com/my-lambda-repository:latest"
-   role = "arn:aws:iam::123456789012:role/MyLambdaExecutionRole"
-
-   model = LambdaModel(image_uri=image_uri, role=role)
-   predictor = model.deploy("my-lambda-function", timeout=20, memory_size=4092)
-
-The ``deploy`` method returns a `LambdaPredictor`_  instance. Use the
-`LambdaPredictor`_ ``predict`` method to perform inference on Lambda.
-
-.. code:: python
-
-   url = "https://example.com/cat.jpeg"
-   predictor.predict({"url": url})  # {'class': 'tabby'}
-
-Once you are done performing inference on Lambda, free the `LambdaModel`_ and
-`LambdaPredictor`_ resources using the ``delete_model`` and ``delete_predictor``
-methods.
-
-.. code:: python
-
-   model.delete_model()
-   predictor.delete_predictor()
-
-.. _LambdaModel : https://sagemaker.readthedocs.io/en/stable/api/inference/model.html#sagemaker.serverless.model.LambdaModel
-.. _LambdaPredictor : https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html#sagemaker.serverless.predictor.LambdaPredictor
-
 ******************
 SageMaker Workflow
 ******************

buildspec-slowtests.yml

@@ -265,6 +265,12 @@ Please use :func:`sagemaker.s3.parse_s3_url` instead.
 
 The class ``sagemaker.session.ModelContainer`` has been deprecated, as it is not needed for creating inference pipelines.
 
+``sagemaker.workflow.condition_step.JsonGet``
+---------------------------------------------
+
+The class ``sagemaker.workflow.condition_step.JsonGet`` has been deprecated.
+Please use :class:`sagemaker.workflow.functions.JsonGet` instead.
+
 Parameter and Class Name Changes
 ================================