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

src/sagemaker/estimator.py

@@ -1478,12 +1478,13 @@ 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 points to a
+                tar.gz) 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
 
                     With the following GitHub repo directory structure:
 

src/sagemaker/model.py

@@ -659,12 +659,12 @@ 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
+            source_dir (str): Path (absolute or relative) to a directory or S3 URI
+                points to a tar.gz 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.
                 .. admonition:: Example
 

src/sagemaker/mxnet/estimator.py

@@ -71,10 +71,10 @@ 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 or relative) to a directory or S3 URI
+                points to a tar.gz. 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.
             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 +68,10 @@ 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 or relative) to a directory or S3 URI
+                points to a tar.gz. 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.
             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 +109,10 @@ 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 or relative) to a directory or S3 URI
+                points to a tar.gz. 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.
             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 +69,10 @@ 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 or relative) to a directory or S3 URI
+                points to a tar.gz. 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.
             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/xgboost/estimator.py

@@ -75,9 +75,10 @@ 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 or relative) to a directory or S3 URI points to a
+                tar.gz. 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.
             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