Merge branch 'master' into airflow-training-config

Author: chuyang-deng

CHANGELOG.md

@@ -1,5 +1,21 @@
 # Changelog
 
+## v1.62.0 (2020-06-11)
+
+### Features
+
+ * Support for multi variant endpoint invocation with target variant param
+
+### Bug Fixes and Other Changes
+
+ * Revert "feature: Support for multi variant endpoint invocation with target variant param (#1571)"
+ * make instance_type optional for prepare_container_def
+ * docs: workflows navigation
+
+### Documentation Changes
+
+ * fix typo in MXNet documentation
+
 ## v1.61.0 (2020-06-09)
 
 ### Features

VERSION

@@ -1 +1 @@
-1.61.1.dev0
+1.62.1.dev0

doc/frameworks/mxnet/using_mxnet.rst

@@ -176,7 +176,7 @@ The default serialization system generates three files:
 -  ``model-symbol.json``: The MXNet ``Module`` ``Symbol`` serialization,
    produced by invoking ``save`` on the ``symbol`` property of the
    ``Module`` being saved.
--  ``modle.params``: The MXNet ``Module`` parameters, produced by
+-  ``model.params``: The MXNet ``Module`` parameters, produced by
    invoking ``save_params`` on the ``Module`` being saved.
 
 You can provide your own save function. This is useful if you are not working with the ``Module`` API or you need special processing.

src/sagemaker/chainer/estimator.py

@@ -105,9 +105,8 @@ def __init__(
             py_version (str): Python version you want to use for executing your
                 model training code (default: 'py2'). One of 'py2' or 'py3'.
             framework_version (str): Chainer version you want to use for
-                executing your model training code. List of supported versions
-                https://github.com/aws/sagemaker-python-sdk#chainer-sagemaker-estimators.
-                If not specified, this will default to 4.1.
+                executing your model training code. If not specified, this will
+                default to 4.1.
             image_name (str): If specified, the estimator will use this image
                 for training and hosting, instead of selecting the appropriate
                 SageMaker official image based on framework_version and

src/sagemaker/pytorch/estimator.py

@@ -83,9 +83,8 @@ def __init__(
             py_version (str): Python version you want to use for executing your
                 model training code (default: 'py3'). One of 'py2' or 'py3'.
             framework_version (str): PyTorch version you want to use for
-                executing your model training code. List of supported versions
-                https://github.com/aws/sagemaker-python-sdk#pytorch-sagemaker-estimators.
-                If not specified, this will default to 0.4.
+                executing your model training code. If not specified, this will default
+                to 0.4.
             image_name (str): If specified, the estimator will use this image
                 for training and hosting, instead of selecting the appropriate
                 SageMaker official image based on framework_version and

src/sagemaker/s3.py

@@ -57,8 +57,9 @@ def upload(local_path, desired_s3_uri, kms_key=None, session=None):
         """Static method that uploads a given file or directory to S3.
 
         Args:
-            local_path (str): A local path to a file or directory.
-            desired_s3_uri (str): The desired S3 uri to upload to.
+            local_path (str): Path (absolute or relative) of local file or directory to upload.
+            desired_s3_uri (str): The desired S3 location to upload to. It is the prefix to
+                which the local filename will be added.
             kms_key (str): The KMS key to use to encrypt the files.
             session (sagemaker.session.Session): Session object which
                 manages interactions with Amazon SageMaker APIs and any other

src/sagemaker/sklearn/estimator.py

@@ -68,8 +68,7 @@ def __init__(
                 If ``source_dir`` is specified, then ``entry_point``
                 must point to a file located at the root of ``source_dir``.
             framework_version (str): Scikit-learn version you want to use for
-                executing your model training code. List of supported versions
-                https://github.com/aws/sagemaker-python-sdk#sklearn-sagemaker-estimators
+                executing your model training code.
             source_dir (str): Path (absolute, relative or an S3 URI) to a directory
                 with any other training source code dependencies aside from the entry
                 point file (default: None). If ``source_dir`` is an S3 URI, it must

src/sagemaker/tensorflow/estimator.py

@@ -237,9 +237,7 @@ def __init__(
             py_version (str): Python version you want to use for executing your model training
                 code (default: 'py2').
             framework_version (str): TensorFlow version you want to use for executing your model
-                training code. List of supported versions
-                https://github.com/aws/sagemaker-python-sdk#tensorflow-sagemaker-estimators.
-                If not specified, this will default to 1.11.
+                training code. If not specified, this will default to 1.11.
             model_dir (str): S3 location where the checkpoint data and models can be exported to
                 during training (default: None). It will be passed in the training script as one of
                 the command line arguments. If not specified, one is provided based on

src/sagemaker/xgboost/estimator.py

@@ -73,8 +73,7 @@ def __init__(
                 be executed as the entry point to training.  If ``source_dir`` is specified,
                 then ``entry_point`` must point to a file located at the root of ``source_dir``.
             framework_version (str): XGBoost version you want to use for executing your model
-                training code.  List of supported versions
-                https://github.com/aws/sagemaker-python-sdk#xgboost-sagemaker-estimators
+                training code.
             source_dir (str): Path (absolute, relative or an S3 URI) to a directory
                 with any other training source code dependencies aside from the entry
                 point file (default: None). If ``source_dir`` is an S3 URI, it must