doc: specify S3 source_dir needs to point to a tar file

src/sagemaker/estimator.py

@@ -1478,12 +1478,14 @@ def __init__(
                     >>>         |----- test.py
 
                     You can assign entry_point='src/train.py'.
-            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). Structure within this directory are
-                preserved when training on Amazon SageMaker. If 'git_config' is
-                provided, 'source_dir' should be a relative location to a
-                directory in the Git repo. .. admonition:: Example
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker. If 'git_config' is provided,
+                'source_dir' should be a relative location to a directory in the Git
+                repo.
+                .. admonition:: Example
 
                     With the following GitHub repo directory structure:
 

src/sagemaker/model.py

@@ -659,13 +659,14 @@ def __init__(
                     >>>         |----- test.py
 
                     You can assign entry_point='src/inference.py'.
-            source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from the entry
-                point file (default: None). Structure within this directory will
-                be preserved when training on SageMaker. If 'git_config' is
-                provided, 'source_dir' should be a relative location to a
-                directory in the Git repo. If the directory points to S3, no
-                code will be uploaded and the S3 location will be used instead.
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker. If 'git_config' is provided,
+                'source_dir' should be a relative location to a directory in the Git repo.
+                If the directory points to S3, no code will be uploaded and the S3 location
+                will be used instead.
                 .. admonition:: Example
 
                     With the following GitHub repo directory structure:

src/sagemaker/mxnet/estimator.py

@@ -71,10 +71,11 @@ def __init__(
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
                 This should be compatible with either Python 2.7 or Python 3.5.
-            source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from the entry
-                point file (default: None). Structure within this directory are
-                preserved when training on Amazon SageMaker.
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for
                 training (default: None). The hyperparameters are made
                 accessible as a dict[str, str] to the training code on

src/sagemaker/pytorch/estimator.py

@@ -68,10 +68,11 @@ def __init__(
             entry_point (str): Path (absolute or relative) to the Python source
                 file which should be executed as the entry point to training.
                 This should be compatible with either Python 2.7 or Python 3.5.
-            source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from the entry
-                point file (default: None). Structure within this directory are
-                preserved when training on Amazon SageMaker.
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for
                 training (default: None). The hyperparameters are made
                 accessible as a dict[str, str] to the training code on

src/sagemaker/rl/estimator.py

@@ -109,10 +109,11 @@ def __init__(
             framework (sagemaker.rl.RLFramework): Framework (MXNet or
                 TensorFlow) you want to be used as a toolkit backed for
                 reinforcement learning training.
-            source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from the entry
-                point file (default: None). Structure within this directory is
-                preserved when training on Amazon SageMaker.
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for
                 training (default: None). The hyperparameters are made
                 accessible as a dict[str, str] to the training code on

src/sagemaker/sklearn/estimator.py

@@ -69,10 +69,11 @@ def __init__(
             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
-            source_dir (str): Path (absolute or relative) to a directory with
-                any other training source code dependencies aside from the entry
-                point file (default: None). Structure within this directory are
-                preserved when training on Amazon SageMaker.
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for
                 training (default: None). The hyperparameters are made
                 accessible as a dict[str, str] to the training code on

src/sagemaker/tensorflow/estimator.py

@@ -567,11 +567,12 @@ def create_model(
                 should be executed as the entry point to training. If not specified and
                 ``endpoint_type`` is 'tensorflow-serving', no entry point is used. If
                 ``endpoint_type`` is also ``None``, then the training entry point is used.
-            source_dir (str): Path (absolute or relative) to a directory with any other serving
-                source code dependencies aside from the entry point file. If not specified and
-                ``endpoint_type`` is 'tensorflow-serving', no source_dir is used. If
-                ``endpoint_type`` is also ``None``, then the model source directory from training
-                is used.
+            source_dir (str): Path (absolute or relative or an S3 URI ) to a directory with any
+                other serving source code dependencies aside from the entry point file. If
+                ``source_dir`` is an S3 URI, it must point to a tar.gz file. If not specified
+                and ``endpoint_type`` is 'tensorflow-serving', no source_dir is used. If
+                ``endpoint_type`` is also ``None``, then the model source directory from
+                training is used.
             dependencies (list[str]): A list of paths to directories (absolute or relative) with
                 any additional libraries that will be exported to the container.
                 If not specified and ``endpoint_type`` is 'tensorflow-serving', ``dependencies`` is

src/sagemaker/xgboost/estimator.py

@@ -75,9 +75,11 @@ def __init__(
             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
-            source_dir (str): Path (absolute or relative) to a directory with any other training
-                source code dependencies aside from the entry point file (default: None).
-                Structure within this directory are preserved when training on Amazon SageMaker.
+            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
+                point to a tar.gz file. Structure within this directory are preserved
+                when training on Amazon SageMaker.
             hyperparameters (dict): Hyperparameters that will be used for training (default: None).
                 The hyperparameters are made accessible as a dict[str, str] to the training code
                 on SageMaker. For convenience, this accepts other types for keys and values, but