documentation: Jumpstart doc strings and added new sections

doc/api/utility/environment_variables.rst

@@ -0,0 +1,7 @@
+Environment Variables
+---------------------
+
+.. automodule:: sagemaker.environment_variables
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/utility/hyperparameters.rst

@@ -0,0 +1,7 @@
+Hyperparameters
+---------------
+
+.. automodule:: sagemaker.hyperparameters
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/utility/model_uris.rst

@@ -0,0 +1,7 @@
+Model URIs
+----------
+
+.. automodule:: sagemaker.model_uris
+    :members:
+    :undoc-members:
+    :show-inheritance:

doc/api/utility/script_uris.rst

@@ -0,0 +1,7 @@
+Script URIs
+-----------
+
+.. automodule:: sagemaker.script_uris
+    :members:
+    :undoc-members:
+    :show-inheritance:

src/sagemaker/environment_variables.py

@@ -31,14 +31,14 @@ def retrieve_default(
     """Retrieves the default container environment variables for the model matching the arguments.
 
     Args:
-        region (str): Optional. Region for which to retrieve default environment variables.
-            (Default: None).
-        model_id (str): Optional. Model ID of the model for which to
+        region (str): Optional. The AWS Region for which to retrieve the default environment
+             variables. (Default: None).
+        model_id (str): Optional. The model ID of the model for which to
             retrieve the default environment variables. (Default: None).
-        model_version (str): Optional. Version of the model for which to retrieve the
+        model_version (str): Optional. The version of the model for which to retrieve the
             default environment variables. (Default: None).
     Returns:
-        dict: the variables to use for the model.
+        dict: The variables to use for the model.
 
     Raises:
         ValueError: If the combination of arguments specified is not supported.

src/sagemaker/hyperparameters.py

@@ -34,20 +34,21 @@ def retrieve_default(
     """Retrieves the default training hyperparameters for the model matching the given arguments.
 
     Args:
-        region (str): Region for which to retrieve default hyperparameters. (Default: None).
-        model_id (str): Model ID of the model for which to
+        region (str): The AWS Region for which to retrieve the default hyperparameters.
+            Defaults to ``None``.
+        model_id (str): The model ID of the model for which to
             retrieve the default hyperparameters. (Default: None).
-        model_version (str): Version of the model for which to retrieve the
+        model_version (str): The version of the model for which to retrieve the
             default hyperparameters. (Default: None).
-        include_container_hyperparameters (bool): True if container hyperparameters
-            should be returned as well. Container hyperparameters are not used to tune
-            the specific algorithm, but rather by SageMaker Training to setup
+        include_container_hyperparameters (bool): ``True`` if the container hyperparameters
+            should be returned. Container hyperparameters are not used to tune
+            the specific algorithm. They are used by SageMaker Training jobs to set up
             the training container environment. For example, there is a container hyperparameter
             that indicates the entrypoint script to use. These hyperparameters may be required
             when creating a training job with boto3, however the ``Estimator`` classes
-            should take care of adding container hyperparameters to the job. (Default: False).
+            add required container hyperparameters to the job. (Default: False).
     Returns:
-        dict: the hyperparameters to use for the model.
+        dict: The hyperparameters to use for the model.
 
     Raises:
         ValueError: If the combination of arguments specified is not supported.
@@ -69,22 +70,22 @@ def validate(
     hyperparameters: Optional[dict] = None,
     validation_mode: Optional[HyperparameterValidationMode] = None,
 ) -> None:
-    """Validate hyperparameters for models.
+    """Validates hyperparameters for models.
 
     Args:
-        region (str): Region for which to validate hyperparameters. (Default: None).
-        model_id (str): Model ID of the model for which to validate hyperparameters.
-            (Default: None)
-        model_version (str): Version of the model for which to validate hyperparameters.
-            (Default: None)
+        region (str): The AWS Region for which to validate hyperparameters. (Default: None).
+        model_id (str): The model ID of the model for which to validate hyperparameters.
+            (Default: None).
+        model_version (str): The version of the model for which to validate hyperparameters.
+            (Default: None).
         hyperparameters (dict): Hyperparameters to validate.
-            (Default: None)
+            (Default: None).
         validation_mode (HyperparameterValidationMode): Method of validation to use with
           hyperparameters. If set to ``VALIDATE_PROVIDED``, only hyperparameters provided
           to this function will be validated, the missing hyperparameters will be ignored.
           If set to``VALIDATE_ALGORITHM``, all algorithm hyperparameters will be validated.
           If set to ``VALIDATE_ALL``, all hyperparameters for the model will be validated.
-          (Default: None)
+          (Default: None).
 
     Raises:
         JumpStartHyperparametersError: If the hyperparameter is not formatted correctly,

src/sagemaker/image_uris.py

@@ -77,20 +77,20 @@ def retrieve(
         training_compiler_config (:class:`~sagemaker.training_compiler.TrainingCompilerConfig`):
             A configuration class for the SageMaker Training Compiler
             (default: None).
-        model_id (str): JumpStart model ID for which to retrieve image URI
+        model_id (str): The JumpStart model ID for which to retrieve the image URI
             (default: None).
-        model_version (str): Version of the JumpStart model for which to retrieve the
+        model_version (str): The version of the JumpStart model for which to retrieve the
             image URI (default: None).
-        tolerate_vulnerable_model (bool): True if vulnerable versions of model specifications
-            should be tolerated (exception not raised). If False, raises an exception if
+        tolerate_vulnerable_model (bool): ``True`` if vulnerable versions of model specifications
+            should be tolerated without an exception raised. If ``False``, raises an exception if
             the script used by this version of the model has dependencies with known security
             vulnerabilities. (Default: False).
         tolerate_deprecated_model (bool): True if deprecated versions of model specifications
-            should be tolerated (exception not raised). If False, raises an exception
+            should be tolerated without an exception raised. If False, raises an exception
             if the version of the model is deprecated. (Default: False).
 
     Returns:
-        str: the ECR URI for the corresponding SageMaker Docker image.
+        str: The ECR URI for the corresponding SageMaker Docker image.
 
     Raises:
         NotImplementedError: If the scope is not supported.
@@ -404,26 +404,26 @@ def get_training_image_uri(
     pytorch_version=None,
     instance_type=None,
 ) -> str:
-    """Retrieve image uri for training.
+    """Retrieves the image URI for training.
 
     Args:
-        region (str): AWS region to use for image URI.
+        region (str): The AWS region to use for image URI.
         framework (str): The framework for which to retrieve an image URI.
         framework_version (str): The framework version for which to retrieve an
             image URI (default: None).
         py_version (str): The python version to use for the image (default: None).
-        image_uri (str): If an image URI is supplied, it will be returned (default: None).
+        image_uri (str): If an image URI is supplied, it is returned (default: None).
         distribution (dict): A dictionary with information on how to run distributed
             training (default: None).
         compiler_config (:class:`~sagemaker.training_compiler.TrainingCompilerConfig`):
             A configuration class for the SageMaker Training Compiler
             (default: None).
-        tensorflow_version (str): Version of tensorflow to use. (default: None)
-        pytorch_version (str): Version of pytorch to use. (default: None)
-        instance_type (str): Instance type fo use. (default: None)
+        tensorflow_version (str): The version of TensorFlow to use. (default: None)
+        pytorch_version (str): The version of PyTorch to use. (default: None)
+        instance_type (str): The instance type to use. (default: None)
 
     Returns:
-        str: the image URI string.
+        str: The image URI string.
     """
 
     if image_uri:

src/sagemaker/model.py

@@ -135,14 +135,14 @@ def __init__(
                 model container is pulled from ECR, or private registry in your
                 VPC. By default it is set to pull model container image from
                 ECR. (default: None).
-            source_dir (str): Path (absolute, relative or an S3 URI) to a directory
+            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 are preserved
+                point to a tar.gz file. 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 will be uploaded and the S3 location
-                will be used instead.
+                If the directory points to S3, no code is uploaded and the S3 location
+                is used instead.
 
                 .. admonition:: Example
 
@@ -155,14 +155,14 @@ def __init__(
 
                     You can assign entry_point='inference.py', source_dir='src'.
             code_location (str): Name of the S3 bucket where custom code is
-                uploaded (default: None). If not specified, default bucket
+                uploaded (default: None). If not specified, the default bucket
                 created by ``sagemaker.session.Session`` is used.
-            entry_point (str): Path (absolute or relative) to the Python source
-                file which should be executed as the entry point to model
-                hosting (default: None). If ``source_dir`` is specified,
-                then ``entry_point`` must point to a file located at the root of
-                ``source_dir``. If 'git_config' is provided, 'entry_point' should
-                be a relative location to the Python source file in the Git repo.
+            entry_point (str): The absolute or relative path to the local Python
+                source file that should be executed as the entry point to
+                model hosting. (Default: None). If ``source_dir`` is specified, then ``entry_point``
+                must point to a file located at the root of ``source_dir``.
+                If 'git_config' is provided, 'entry_point' should be
+                a relative location to the Python source file in the Git repo.
 
                 Example:
                     With the following GitHub repo directory structure:
@@ -176,9 +176,9 @@ def __init__(
             container_log_level (int): Log level to use within the container
                 (default: logging.INFO). Valid values are defined in the Python
                 logging module.
-            dependencies (list[str]): A list of paths to directories (absolute
-                or relative) with any additional libraries that will be exported
-                to the container (default: []). The library folders will be
+            dependencies (list[str]): A list of absolute or relative paths to directories
+                with any additional libraries that should be exported
+                to the container (default: []). The library folders are
                 copied to SageMaker in the same folder where the entrypoint is
                 copied. If 'git_config' is provided, 'dependencies' should be a
                 list of relative locations to directories with any additional
@@ -193,7 +193,7 @@ def __init__(
                     >>> Model(entry_point='inference.py',
                     ...       dependencies=['my/libs/common', 'virtual-env'])
 
-                    results in the following inside the container:
+                    results in the following structure inside the container:
 
                     >>> $ ls
 
@@ -210,7 +210,9 @@ def __init__(
                 ``repo`` specifies the Git repository where your training script
                 is stored. If you don't provide ``branch``, the default value
                 'master' is used. If you don't provide ``commit``, the latest
-                commit in the specified branch is used. .. admonition:: Example
+                commit in the specified branch is used.
+
+                .. admonition:: Example
 
                     The following config:
 
@@ -219,7 +221,7 @@ def __init__(
                     >>>               'commit': '329bfcf884482002c05ff7f44f62599ebc9f445a'}
 
                     results in cloning the repo specified in 'repo', then
-                    checkout the 'master' branch, and checkout the specified
+                    checking out the 'master' branch, and checking out the specified
                     commit.
 
                 ``2FA_enabled``, ``username``, ``password`` and ``token`` are
@@ -232,26 +234,25 @@ def __init__(
                 repositories.
 
                 For GitHub and other Git repos, when SSH URLs are provided, it
-                doesn't matter whether 2FA is enabled or disabled; you should
-                either have no passphrase for the SSH key pairs, or have the
-                ssh-agent configured so that you will not be prompted for SSH
-                passphrase when you do 'git clone' command with SSH URLs. When
-                HTTPS URLs are provided: if 2FA is disabled, then either token
-                or username+password will be used for authentication if provided
-                (token prioritized); if 2FA is enabled, only token will be used
+                doesn't matter whether 2FA is enabled or disabled. You should
+                either have no passphrase for the SSH key pairs or have the
+                ssh-agent configured so that you will not be prompted for the SSH
+                passphrase when you run the 'git clone' command with SSH URLs. When
+                HTTPS URLs are provided, if 2FA is disabled, then either ``token``
+                or ``username`` and ``password`` are be used for authentication if provided.
+                ``Token`` is prioritized. If 2FA is enabled, only ``token`` is used
                 for authentication if provided. If required authentication info
-                is not provided, python SDK will try to use local credentials
-                storage to authenticate. If that fails either, an error message
-                will be thrown.
+                is not provided, the SageMaker Python SDK attempts to use local credentials
+                to authenticate. If that fails, an error message is thrown.
 
-                For CodeCommit repos, 2FA is not supported, so '2FA_enabled'
+                For CodeCommit repos, 2FA is not supported, so ``2FA_enabled``
                 should not be provided. There is no token in CodeCommit, so
-                'token' should not be provided too. When 'repo' is an SSH URL,
-                the requirements are the same as GitHub-like repos. When 'repo'
-                is an HTTPS URL, username+password will be used for
-                authentication if they are provided; otherwise, python SDK will
-                try to use either CodeCommit credential helper or local
-                credential storage for authentication.
+                ``token`` should also not be provided. When ``repo`` is an SSH URL,
+                the requirements are the same as GitHub  repos. When ``repo``
+                is an HTTPS URL, ``username`` and ``password`` are used for
+                authentication if they are provided. If they are not provided,
+                the SageMaker Python SDK attempts to use either the CodeCommit
+                credential helper or local credential storage for authentication.
 
         """
         self.model_data = model_data
@@ -1313,7 +1314,9 @@ def __init__(
                 ``repo`` specifies the Git repository where your training script
                 is stored. If you don't provide ``branch``, the default value
                 'master' is used. If you don't provide ``commit``, the latest
-                commit in the specified branch is used. .. admonition:: Example
+                commit in the specified branch is used.
+
+                .. admonition:: Example
 
                     The following config:
 

src/sagemaker/model_uris.py

@@ -10,7 +10,7 @@
 # distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
-"""Accessors to retrieve the model artifact S3 URI of pretrained ML models."""
+"""Accessors to retrieve the model artifact S3 URI of pretrained machine learning models."""
 from __future__ import absolute_import
 
 import logging
@@ -31,25 +31,25 @@ def retrieve(
     tolerate_vulnerable_model: bool = False,
     tolerate_deprecated_model: bool = False,
 ) -> str:
-    """Retrieves the model artifact S3 URI for the model matching the given arguments.
+    """Retrieves the model artifact Amazon S3 URI for the model matching the given arguments.
 
     Args:
-        region (str): Region for which to retrieve model S3 URI.
-        model_id (str): JumpStart model ID of the JumpStart model for which to retrieve
+        region (str): The AWS Region for which to retrieve the Jumpstart model S3 URI.
+        model_id (str): The model ID of the JumpStart model for which to retrieve
             the model artifact S3 URI.
-        model_version (str): Version of the JumpStart model for which to retrieve
+        model_version (str): The version of the JumpStart model for which to retrieve
             the model artifact S3 URI.
-        model_scope (str): The model type, i.e. what it is used for.
+        model_scope (str): The model type.
             Valid values: "training" and "inference".
-        tolerate_vulnerable_model (bool): True if vulnerable versions of model
-            specifications should be tolerated (exception not raised). If False, raises an
+        tolerate_vulnerable_model (bool): ``True`` if vulnerable versions of model
+            specifications should be tolerated without raising an exception. If ``False``, raises an
             exception if the script used by this version of the model has dependencies with known
             security vulnerabilities. (Default: False).
-        tolerate_deprecated_model (bool): True if deprecated versions of model
-            specifications should be tolerated (exception not raised). If False, raises
+        tolerate_deprecated_model (bool): ``True`` if deprecated versions of model
+            specifications should be tolerated without raising an exception. If ``False``, raises
             an exception if the version of the model is deprecated. (Default: False).
     Returns:
-        str: the model artifact S3 URI for the corresponding model.
+        str: The model artifact S3 URI for the corresponding model.
 
     Raises:
         NotImplementedError: If the scope is not supported.