Fix sourcedir.tar.gz filenames in docstrings

src/sagemaker/estimator.py

@@ -387,8 +387,8 @@ def __init__(
             source_dir (str or PipelineVariable): The absolute, relative, or S3 URI Path 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. The structure within this directory is preserved
-                when training on Amazon SageMaker. If 'git_config' is provided,
+                point to a file with name ``sourcedir.tar.gz``. The structure within this directory
+                is 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.
                 With the following GitHub repo directory structure:
@@ -3421,8 +3421,8 @@ def __init__(
             source_dir (str or PipelineVariable): 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,
+                point to a file with name ``sourcedir.tar.gz``. 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.
 

src/sagemaker/fw_utils.py

@@ -252,7 +252,7 @@ def validate_source_code_input_against_pipeline_variables(
         logger.warning(
             "The source_dir is a pipeline variable: %s. During pipeline execution, "
             "the interpreted value of source_dir has to be an S3 URI and "
-            "must point to a tar.gz file",
+            "must point to a file with name ``sourcedir.tar.gz``",
             type(source_dir),
         )
 

src/sagemaker/huggingface/estimator.py

@@ -84,8 +84,8 @@ def __init__(
             source_dir (str or PipelineVariable): 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.
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory are
+                preserved when training on Amazon SageMaker.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]): 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/jumpstart/estimator.py

@@ -350,8 +350,8 @@ def __init__(
             source_dir (Optional[Union[str, PipelineVariable]]): The absolute, relative, or
                 S3 URI Path to a directory with any other training source code dependencies
                 aside from the entry point file. If ``source_dir`` is an S3 URI, it must
-                point to a tar.gz file. Structure within this directory is preserved
-                when training on Amazon SageMaker. If 'git_config' is provided,
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory
+                is 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.
                 (Default: None).
@@ -947,8 +947,8 @@ def deploy(
             source_dir (Optional[str]): The absolute, relative, or S3 URI Path 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 is preserved
-                when training on Amazon SageMaker. If 'git_config' is provided,
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory is
+                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 is uploaded and the S3 location
                 is used instead. (Default: None).

src/sagemaker/jumpstart/model.py

@@ -178,8 +178,8 @@ def __init__(
             source_dir (Optional[str]): The absolute, relative, or S3 URI Path 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 is preserved
-                when training on Amazon SageMaker. If 'git_config' is provided,
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory is
+                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 is uploaded and the S3 location
                 is used instead. (Default: None).

src/sagemaker/model.py

@@ -215,8 +215,8 @@ def __init__(
             source_dir (str): The absolute, relative, or S3 URI Path 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 is preserved
-                when training on Amazon SageMaker. If 'git_config' is provided,
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory
+                is 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 is uploaded and the S3 location
                 is used instead.
@@ -1996,11 +1996,11 @@ def __init__(
             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.
+                point to a file with name ``sourcedir.tar.gz``. 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
 

src/sagemaker/mxnet/estimator.py

@@ -84,8 +84,8 @@ def __init__(
             source_dir (str or PipelineVariable): 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.
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory
+                are preserved when training on Amazon SageMaker.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]): 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

@@ -182,8 +182,8 @@ def __init__(
                 unless ``image_uri`` is provided.
             source_dir (str or PipelineVariable): 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
+                point file (default: None). If ``source_dir`` is an S3 URI, it must point to a
+                file with name ``sourcedir.tar.gz``. Structure within this directory are preserved
                 when training on Amazon SageMaker. Must be a local path when using training_recipe.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]): Hyperparameters
                 that will be used for training (default: None). The hyperparameters are made

src/sagemaker/rl/estimator.py

@@ -120,8 +120,8 @@ def __init__(
             source_dir (str or PipelineVariable): 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.
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory
+                are preserved when training on Amazon SageMaker.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]): 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

@@ -83,8 +83,8 @@ def __init__(
             source_dir (str or PipelineVariable): 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.
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory
+                are preserved when training on Amazon SageMaker.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]): 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

@@ -78,8 +78,8 @@ def __init__(
             source_dir (str or PipelineVariable): 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.
+                point to a file with name ``sourcedir.tar.gz``. Structure within this directory
+                are preserved when training on Amazon SageMaker.
             hyperparameters (dict[str, str] or dict[str, PipelineVariable]): Hyperparameters
                 that will be used for training (default: None).
                 The hyperparameters are made accessible as a dict[str, str] to the training code