fix: fixed conflicts to comply with latest version

Author: ahsan-z-khan

src/sagemaker/algorithm.py

@@ -495,6 +495,7 @@ def _parse_hyperparameters(self):
         return definitions
 
     def _hyperparameter_range_and_class(self, parameter_type, hyperparameter):
+        """Placeholder docstring."""
         if parameter_type in self._hyperpameters_with_range:
             range_name = parameter_type + "ParameterRangeSpecification"
 

src/sagemaker/amazon/knn.py

@@ -235,7 +235,8 @@ def __init__(
 class KNNModel(Model):
     """Reference S3 model data created by KNN estimator.
 
-    Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint and returns :class:`KNNPredictor`.
+    Calling :meth:`~sagemaker.model.Model.deploy` creates an Endpoint
+    and returns :class:`KNNPredictor`.
     """
 
     def __init__(self, model_data, role, sagemaker_session=None, **kwargs):

src/sagemaker/cli/compatibility/v2/files.py

@@ -29,7 +29,7 @@ class FileUpdater(object):
     """An abstract class for updating files."""
 
     def __init__(self, input_path, output_path):
-        """Creates a ``FileUpdater`` for updating a file to be compatible with version 2.0 and later.
+        """Creates ``FileUpdater`` for updating a file to be compatible with version 2.0 and later.
 
         Args:
             input_path (str): Location of the input file.

src/sagemaker/cli/compatibility/v2/modifiers/modifier.py

@@ -17,7 +17,7 @@
 
 
 class Modifier(object):
-    """Abstract class to check if an AST node needs modification, and potentially modify the node."""
+    """Abstract class to check if an AST node needs modification, potentially modify the node."""
 
     def check_and_modify_node(self, node):
         """Check an AST node, and modify, replace, or remove it if applicable."""

src/sagemaker/git_utils.py

@@ -307,7 +307,7 @@ def _insert_token_to_repo_url(url, token):
 
 
 def _insert_username_and_password_to_repo_url(url, username, password):
-    """Insert username and password to the Git repo url, to make a component of the git clone command.
+    """Insert username and password to the Git repo url to make a component of git clone command.
 
     This method can only be called when repo_url is an https url.
 

src/sagemaker/local/data.py

@@ -379,6 +379,7 @@ def pad(self, file, size=6):
 
 
 def _payload_size_within_limit(payload, size):
+    """Placeholder docstring."""
     size_in_bytes = size * 1024 * 1024
     if size == 0:
         return True

src/sagemaker/local/entities.py

@@ -54,6 +54,7 @@ def __init__(self, container):
         self.end_time = None
 
     def start(self, input_data_config, output_data_config, hyperparameters, job_name):
+        """Placeholder docstring."""
         for channel in input_data_config:
             if channel["DataSource"] and "S3DataSource" in channel["DataSource"]:
                 data_distribution = channel["DataSource"]["S3DataSource"]["S3DataDistributionType"]
@@ -466,6 +467,7 @@ def describe(self):
 
 
 def _wait_for_serving_container(serving_port):
+    """Placeholder docstring."""
     i = 0
     http = urllib3.PoolManager()
 
@@ -486,6 +488,7 @@ def _wait_for_serving_container(serving_port):
 
 
 def _perform_request(endpoint_url, pool_manager=None):
+    """Placeholder docstring."""
     http = pool_manager or urllib3.PoolManager()
     try:
         r = http.request("GET", endpoint_url)

src/sagemaker/model.py

@@ -246,7 +246,7 @@ def _get_model_package_args(
         return model_package_args
 
     def _init_sagemaker_session_if_does_not_exist(self, instance_type):
-        """Set ``self.sagemaker_session`` to be a ``LocalSession`` or ``Session`` if it is not already.
+        """Set ``self.sagemaker_session`` to ``LocalSession`` or ``Session`` if it's not already.
 
         The type of session object is determined by the instance type.
         """
@@ -1060,8 +1060,9 @@ def __init__(
         self.repacked_model_data = None
 
     def prepare_container_def(self, instance_type=None, accelerator_type=None):
-        """Return a container definition with framework configuration set in model environment variables.
+        """Return a container definition with framework configuration.
 
+        Framework configuration is set in model environment variables.
         This also uploads user-supplied code to S3.
 
         Args:

src/sagemaker/model_monitor/cron_expression_generator.py

@@ -19,7 +19,7 @@
 
 
 class CronExpressionGenerator(object):
-    """Generates cron expression strings for use with the SageMaker Model Monitoring Schedule API."""
+    """Generates cron expression strings for the SageMaker Model Monitoring Schedule API."""
 
     @staticmethod
     def hourly():

src/sagemaker/mxnet/model.py

@@ -211,7 +211,9 @@ def register(
         )
 
     def prepare_container_def(self, instance_type=None, accelerator_type=None):
-        """Return a container definition with framework configuration set in model environment variables.
+        """Return a container definition with framework configuration.
+
+        Framework configuration is set in model environment variables.
 
         Args:
             instance_type (str): The EC2 instance type to deploy this Model to.
@@ -273,7 +275,7 @@ def serving_image_uri(self, region_name, instance_type, accelerator_type=None):
         )
 
     def _is_mms_version(self):
-        """Whether the framework version corresponds to an inference image using the Multi-Model Server.
+        """Whether the framework version corresponds to an inference image using the MMS.
 
         MMS Server: (https://github.com/awslabs/multi-model-server).
 

src/sagemaker/predictor.py

@@ -126,13 +126,13 @@ def predict(self, data, initial_args=None, target_model=None, target_variant=Non
         return self._handle_response(response)
 
     def _handle_response(self, response):
-        "Placeholder docstring"
+        """Placeholder docstring"""
         response_body = response["Body"]
         content_type = response.get("ContentType", "application/octet-stream")
         return self.deserializer.deserialize(response_body, content_type)
 
     def _create_request_args(self, data, initial_args=None, target_model=None, target_variant=None):
-        "Placeholder docstring"
+        """Placeholder docstring"""
         args = dict(initial_args) if initial_args else {}
 
         if "EndpointName" not in args:

src/sagemaker/rl/estimator.py

@@ -344,6 +344,7 @@ def hyperparameters(self):
 
     @classmethod
     def _toolkit_and_version_from_tag(cls, image_tag):
+        """Placeholder docstring."""
         tag_pattern = re.compile(
             "^([A-Z]*|[a-z]*)(\d.*)-(cpu|gpu)-(py2|py3)$"  # noqa: W605,E501 pylint: disable=anomalous-backslash-in-string
         )
@@ -354,6 +355,7 @@ def _toolkit_and_version_from_tag(cls, image_tag):
 
     @classmethod
     def _validate_framework_format(cls, framework):
+        """Placeholder docstring."""
         if framework and framework not in list(RLFramework):
             raise ValueError(
                 "Invalid type: {}, valid RL frameworks types are: {}".format(
@@ -363,13 +365,15 @@ def _validate_framework_format(cls, framework):
 
     @classmethod
     def _validate_toolkit_format(cls, toolkit):
+        """Placeholder docstring."""
         if toolkit and toolkit not in list(RLToolkit):
             raise ValueError(
                 "Invalid type: {}, valid RL toolkits types are: {}".format(toolkit, list(RLToolkit))
             )
 
     @classmethod
     def _validate_images_args(cls, toolkit, toolkit_version, framework, image_uri):
+        """Placeholder docstring."""
         cls._validate_toolkit_format(toolkit)
         cls._validate_framework_format(framework)
 
@@ -404,6 +408,7 @@ def _validate_images_args(cls, toolkit, toolkit_version, framework, image_uri):
 
     @classmethod
     def _is_combination_supported(cls, toolkit, toolkit_version, framework):
+        """Placeholder docstring."""
         supported_versions = TOOLKIT_FRAMEWORK_VERSION_MAP.get(toolkit, None)
         if supported_versions:
             supported_frameworks = supported_versions.get(toolkit_version, None)
@@ -413,6 +418,7 @@ def _is_combination_supported(cls, toolkit, toolkit_version, framework):
 
     @classmethod
     def _validate_toolkit_support(cls, toolkit, toolkit_version, framework):
+        """Placeholder docstring."""
         if not cls._is_combination_supported(toolkit, toolkit_version, framework):
             raise AttributeError(
                 "Provided `{}-{}` and `{}` combination is not supported.".format(

src/sagemaker/session.py

@@ -1353,7 +1353,7 @@ def delete_monitoring_schedule(self, monitoring_schedule_name):
         )
 
     def describe_monitoring_schedule(self, monitoring_schedule_name):
-        """Calls the DescribeMonitoringSchedule API for given monitoring schedule name and returns the response.
+        """Calls the DescribeMonitoringSchedule API for given name and returns the response.
 
         Args:
             monitoring_schedule_name (str): The name of the processing job to describe.
@@ -1427,7 +1427,9 @@ def list_monitoring_schedules(
         return response
 
     def was_processing_job_successful(self, job_name):
-        """Calls the DescribeProcessingJob API for the given job name and returns True if job was successful.
+        """Calls the DescribeProcessingJob API for the given job name.
+
+        It returns True if job was successful.
 
         Args:
             job_name (str): The name of the processing job to describe.
@@ -1614,7 +1616,7 @@ def wait_for_auto_ml_job(self, job, poll=5):
     def logs_for_auto_ml_job(  # noqa: C901 - suppress complexity warning for this method
         self, job_name, wait=False, poll=10
     ):
-        """Display the logs for a given AutoML job, optionally tailing them until the job is complete.
+        """Display logs for a given AutoML job, optionally tailing them until job is complete.
 
         If the output is a tty or a Jupyter cell, it will be color-coded
         based on which instance the log entry is from.
@@ -1943,7 +1945,7 @@ def create_tuning_job(
 
     def describe_tuning_job(self, job_name):
         """
-        Calls the DescribeHyperParameterTuningJob API for the given job name and returns the response.
+        Calls the DescribeHyperParameterTuningJob API for the given job name, returns the response.
 
         Args:
             job_name (str): The name of the hyperparameter tuning job to describe.
@@ -2861,7 +2863,7 @@ def create_endpoint_config_from_existing(
         self.sagemaker_client.create_endpoint_config(**request)
 
     def create_endpoint(self, endpoint_name, config_name, tags=None, wait=True):
-        """Create an Amazon SageMaker ``Endpoint`` according to the endpoint configuration in the request.
+        """Create an Amazon SageMaker ``Endpoint`` according to the configuration in the request.
 
         Once the ``Endpoint`` is created, client applications can send requests to obtain
         inferences. The endpoint configuration is created using the ``CreateEndpointConfig`` API.
@@ -2888,9 +2890,9 @@ def create_endpoint(self, endpoint_name, config_name, tags=None, wait=True):
         return endpoint_name
 
     def update_endpoint(self, endpoint_name, endpoint_config_name, wait=True):
-        """Update an Amazon SageMaker ``Endpoint`` according to the endpoint configuration in the request.
+        """Update an Amazon SageMaker ``Endpoint`` , Raise an error endpoint_name does not exist.
 
-        Raise an error if endpoint with endpoint_name does not exist.
+        Update happens according to the endpoint configuration in the request.
 
         Args:
             endpoint_name (str): Name of the Amazon SageMaker ``Endpoint`` to update.
@@ -3436,7 +3438,7 @@ def get_caller_identity_arn(self):
     def logs_for_job(  # noqa: C901 - suppress complexity warning for this method
         self, job_name, wait=False, poll=10, log_type="All"
     ):
-        """Display the logs for a given training job, optionally tailing them until the job is complete.
+        """Display the for a given training job, optionally tailing them until job is complete.
 
         If the output is a tty or a Jupyter cell, it will be color-coded
         based on which instance the log entry is from.
@@ -3555,7 +3557,7 @@ def logs_for_job(  # noqa: C901 - suppress complexity warning for this method
                     print("Managed Spot Training savings: {:.1f}%".format(saving))
 
     def logs_for_processing_job(self, job_name, wait=False, poll=10):
-        """Display the logs for a given processing job, optionally tailing them until the job is complete.
+        """Display logs for a given processing job, optionally tailing them until the is complete.
 
         Args:
             job_name (str): Name of the processing job to display the logs for.
@@ -3633,7 +3635,7 @@ def logs_for_processing_job(self, job_name, wait=False, poll=10):
                 print()
 
     def logs_for_transform_job(self, job_name, wait=False, poll=10):
-        """Display the logs for a given transform job, optionally tailing them until the job is complete.
+        """Display logs for a given transform job, optionally tailing them until job is complete.
 
         If the output is a tty or a Jupyter cell, it will be color-coded
         based on which instance the log entry is from.

src/sagemaker/sklearn/estimator.py

@@ -260,7 +260,7 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
 
 
 def _validate_not_gpu_instance_type(training_instance_type):
-
+    """Placeholder docstring."""
     gpu_instance_types = [
         "ml.p2.xlarge",
         "ml.p2.8xlarge",

src/sagemaker/tuner.py

@@ -1247,13 +1247,14 @@ def create(
     ):
         """Factory method to create a ``HyperparameterTuner`` instance.
 
-        It takes one or more estimators to obtain configuration information for training jobs that are
-        created as the result of a hyperparameter tuning job. The estimators are provided through a dictionary
-        (i.e. ``estimator_dict``) with unique estimator names as the keys. For individual
-        estimators separate objective metric names and hyperparameter ranges should be provided in
-        two dictionaries, i.e. ``objective_metric_name_dict`` and ``hyperparameter_ranges_dict``,
-        with the same estimator names as the keys. Optional metrics definitions could also be
-        provided for individual estimators via another dictionary ``metric_definitions_dict``.
+        It takes one or more estimators to obtain configuration information for training jobs
+        that are created as the result of a hyperparameter tuning job. The estimators are provided
+        through a  dictionary (i.e. ``estimator_dict``) with unique estimator names as the keys.
+        For  individual estimators separate objective metric names and hyperparameter ranges
+        should be provided in two dictionaries, i.e. ``objective_metric_name_dict`` and
+        ``hyperparameter_ranges_dict``, with the same estimator names as the keys. Optional
+        metrics definitions could also be provided for individual estimators via another dictionary
+        ``metric_definitions_dict``.
 
         Args:
             estimator_dict (dict[str, sagemaker.estimator.EstimatorBase]): Dictionary of estimator

src/sagemaker/utils.py

@@ -340,8 +340,9 @@ def create_tar_file(source_files, target=None):
 
 @contextlib.contextmanager
 def _tmpdir(suffix="", prefix="tmp"):
-    """Create a temporary directory with a context manager. The file is deleted when the context exits.
+    """Create a temporary directory with a context manager.
 
+    The file is deleted when the context exits.
     The prefix, suffix, and dir arguments are the same as for mkstemp().
 
     Args: