Merge branch 'master' into deepar-kix

Author: jaheba

.githooks/pre-push

@@ -12,5 +12,5 @@ start_time=`date +%s`
 tox -e sphinx,doc8 --parallel all
 ./ci-scripts/displaytime.sh 'sphinx,doc8' $start_time
 start_time=`date +%s`
-tox -e py36,py37,py38,py39 --parallel all -- tests/unit
-./ci-scripts/displaytime.sh 'py36,py37,py38,py39 unit' $start_time
+tox -e py37,py38,py39 --parallel all -- tests/unit
+./ci-scripts/displaytime.sh 'py37,py38,py39 unit' $start_time

CHANGELOG.md

@@ -1,5 +1,84 @@
 # Changelog
 
+## v2.97.0 (2022-06-28)
+
+### Deprecations and Removals
+
+ * remove support for python 3.6
+
+### Features
+
+ * update prebuilt models documentation
+
+### Bug Fixes and Other Changes
+
+ * Skipping test_candidate_estimator_default_rerun_and_deploy
+ * Update model name from 'compiled.pt' to 'model.pth' for neo
+ * update pytest, skip hf integ temp
+ * Add override_pipeline_parameter_var decorator to give grace period to update invalid pipeline var args
+
+## v2.96.0 (2022-06-20)
+
+### Features
+
+ * Add helper method to generate pipeline adjacency list
+
+### Bug Fixes and Other Changes
+
+ * changing trcomp integ tests to be able to run in all regions
+
+## v2.95.0 (2022-06-16)
+
+### Features
+
+ * Adding Training Compiler support for TensorFlow estimator starting TF 2.9
+ * Add support for TF 2.9 training
+
+### Bug Fixes and Other Changes
+
+ * integs fallback from p3 to p2 instance
+ * bucket exists check for session.default_bucket
+ * make instance type fields as optional
+
+### Documentation Changes
+
+ * improvements on the docstring of ModelStep
+ * Add XGBoostProcessor
+
+## v2.94.0 (2022-06-07)
+
+### Features
+
+ * AutoGluon 0.4.2 image_uris support
+
+## v2.93.1 (2022-06-06)
+
+### Bug Fixes and Other Changes
+
+ * add input parameterization tests for workflow job steps
+ * add parameterized tests to transformer
+
+## v2.93.0 (2022-06-03)
+
+### Features
+
+ * MxNet 1.9 support
+
+### Bug Fixes and Other Changes
+
+ * bump importlib-metadata version upperbound to support TF2.9
+ * fix pipeline doc code example where process.run only accepts argument
+ * Fix Tensorflow default model_dir generation when output_path is pipeline variable
+ * Support transformer data parameterization
+
+## v2.92.2 (2022-05-31)
+
+### Bug Fixes and Other Changes
+
+ * turn off Pipeline Parameter inheritance from python primitives
+ * Add more validations for pipeline step new interfaces
+ * Changed method description per AWS request
+
 ## v2.92.1 (2022-05-26)
 
 ### Bug Fixes and Other Changes

README.rst

@@ -87,7 +87,6 @@ Supported Python Versions
 
 SageMaker Python SDK is tested on:
 
-- Python 3.6
 - Python 3.7
 - Python 3.8
 - Python 3.9

VERSION

@@ -1 +1 @@
-2.92.2.dev0
+2.97.1.dev0

doc/amazon_sagemaker_model_building_pipeline.rst

@@ -239,7 +239,7 @@ Referable Property List:
                 ProcessingOutput(output_name="train", source="/opt/ml/processing/train"),
             ],
             code="./local/preprocess.py",
-            job_arguments=["--input-data", "s3://my-input"]
+            arguments=["--input-data", "s3://my-input"]
         ),
     )
 
@@ -496,7 +496,7 @@ A :class:`sagemaker.workflow.properties.PropertyFile` is designed to store infor
             ProcessingOutput(output_name="hyperparam", source="/opt/ml/processing/evaluation"),
         ],
         code="./local/preprocess.py",
-        job_arguments=["--input-data", "s3://my-input"],
+        arguments=["--input-data", "s3://my-input"],
     )
 
     hyperparam_report = PropertyFile(
@@ -577,7 +577,7 @@ There are eight types of condition are supported, they are:
             ProcessingOutput(output_name="hyperparam", source="/opt/ml/processing/evaluation"),
         ],
         code="./local/preprocess.py",
-        job_arguments=["--input-data", "s3://my-input"],
+        arguments=["--input-data", "s3://my-input"],
     )
 
     eval_report = PropertyFile(
@@ -668,7 +668,7 @@ Use :class:`sagemaker.workflow.functions.JsonGet` to extract a Json property fro
             ProcessingOutput(output_name="hyperparam", source="/opt/ml/processing/evaluation"),
         ],
         code="./local/preprocess.py",
-        job_arguments=["--input-data", "s3://my-input"],
+        arguments=["--input-data", "s3://my-input"],
     )
 
     hyperparam_report = PropertyFile(

doc/doc_utils/jumpstart_doc_utils.py

@@ -143,20 +143,26 @@ def create_jumpstart_model_table():
     file_content.append(".. |external-link| raw:: html\n\n")
     file_content.append('   <i class="fa fa-external-link"></i>\n\n')
 
-    file_content.append("==================================\n")
-    file_content.append("JumpStart Available Model Table\n")
-    file_content.append("==================================\n")
+    file_content.append("================================================\n")
+    file_content.append("Built-in Algorithms with pre-trained Model Table\n")
+    file_content.append("================================================\n")
     file_content.append(
         """
-    JumpStart for the SageMaker Python SDK uses model IDs and model versions to access the necessary
-    utilities. This table serves to provide the core material plus some extra information that can be useful
-    in selecting the correct model ID and corresponding parameters.\n"""
+    The SageMaker Python SDK uses model IDs and model versions to access the necessary
+    utilities for pre-trained models. This table serves to provide the core material plus
+    some extra information that can be useful in selecting the correct model ID and
+    corresponding parameters.\n"""
     )
     file_content.append(
         """
     If you want to automatically use the latest version of the model, use "*" for the `model_version` attribute.
     We highly suggest pinning an exact model version however.\n"""
     )
+    file_content.append(
+        """
+    These models are also available through the
+    `JumpStart UI in SageMaker Studio <https://docs.aws.amazon.com/sagemaker/latest/dg/studio-jumpstart.html>`__\n"""
+    )
     file_content.append("\n")
     file_content.append(".. list-table:: Available Models\n")
     file_content.append("   :widths: 50 20 20 20 30 20\n")
@@ -183,5 +189,6 @@ def create_jumpstart_model_table():
             "     - `{} <{}>`__ |external-link|\n".format(model_source, model_spec["url"])
         )
 
-    f = open("doc_utils/jumpstart.rst", "w")
+    f = open("doc_utils/pretrainedmodels.rst", "w")
     f.writelines(file_content)
+    f.close()

doc/doc_utils/jumpstart.rst renamed to doc/doc_utils/pretrainedmodels.rst

@@ -10,6 +10,14 @@ TensorFlow Estimator
     :undoc-members:
     :show-inheritance:
 
+TensorFlow Training Compiler Configuration
+------------------------------------------
+
+.. autoclass:: sagemaker.tensorflow.TrainingCompilerConfig
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 TensorFlow Serving Model
 ------------------------
 

doc/frameworks/tensorflow/sagemaker.tensorflow.rst

@@ -759,7 +759,7 @@ Create Python Scripts for Custom Input and Output Formats
 ---------------------------------------------------------
 
 You can add your customized Python code to process your input and output data.
-This customized Python code must be named ``inference.py`` and specified through the ``entry_point`` parameter:
+This customized Python code must be named ``inference.py`` and is specified through the ``entry_point`` parameter:
 
 .. code::
 
@@ -769,6 +769,8 @@ This customized Python code must be named ``inference.py`` and specified through
                             model_data='s3://mybucket/model.tar.gz',
                             role='MySageMakerRole')
 
+In the example above, ``inference.py`` is assumed to be a file inside ``model.tar.gz``. If you want to use a local file instead, you must add the ``source_dir`` argument. See the documentation on `TensorFlowModel <https://sagemaker.readthedocs.io/en/stable/frameworks/tensorflow/sagemaker.tensorflow.html#sagemaker.tensorflow.model.TensorFlowModel>`_.
+
 How to implement the pre- and/or post-processing handler(s)
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 

doc/frameworks/tensorflow/using_tf.rst

@@ -465,6 +465,7 @@ For information about the SageMaker Python SDK XGBoost classes, see the followin
 * :class:`sagemaker.xgboost.estimator.XGBoost`
 * :class:`sagemaker.xgboost.model.XGBoostModel`
 * :class:`sagemaker.xgboost.model.XGBoostPredictor`
+* :class:`sagemaker.xgboost.processing.XGBoostProcessor`
 
 ***********************************
 SageMaker XGBoost Docker Containers

doc/frameworks/xgboost/using_xgboost.rst

@@ -16,3 +16,8 @@ The Amazon SageMaker XGBoost open source framework algorithm.
     :members:
     :undoc-members:
     :show-inheritance:
+
+.. autoclass:: sagemaker.xgboost.processing.XGBoostProcessor
+    :members:
+    :undoc-members:
+    :show-inheritance: