feature: Add autotune for HyperparameterTuner (#3892)

* feature: Add autotune for HyperparameterTuner

* change: Rename keep_static, make comments more clean

* change: further clean comments relaetd to Autotune

---------

Co-authored-by: Xingchen Ma <xgchenma@amazon.com>

Author: maxc01

src/sagemaker/session.py

@@ -2521,6 +2521,8 @@ def tune(  # noqa: C901
         random_seed=None,
         environment=None,
         hpo_resource_config=None,
+        autotune=False,
+        auto_parameters=None,
     ):
         """Create an Amazon SageMaker hyperparameter tuning job.
 
@@ -2626,6 +2628,11 @@ def tune(  # noqa: C901
                 * volume_kms_key_id: The AWS Key Management Service (AWS KMS) key
                 that Amazon SageMaker uses to encrypt data on the storage
                 volume attached to the ML compute instance(s) that run the training job.
+            autotune (bool): Whether the parameter ranges or other unset settings of a tuning job
+                should be chosen automatically (default: False).
+            auto_parameters (dict[str, str]): Dictionary of auto parameters. The keys are names
+                of auto parameters and values are example values of auto parameters
+                (default: ``None``).
         """
 
         tune_request = {
@@ -2642,6 +2649,7 @@ def tune(  # noqa: C901
                 random_seed=random_seed,
                 strategy_config=strategy_config,
                 completion_criteria_config=completion_criteria_config,
+                auto_parameters=auto_parameters,
             ),
             "TrainingJobDefinition": self._map_training_config(
                 static_hyperparameters=static_hyperparameters,
@@ -2668,6 +2676,9 @@ def tune(  # noqa: C901
         if warm_start_config is not None:
             tune_request["WarmStartConfig"] = warm_start_config
 
+        if autotune:
+            tune_request["Autotune"] = {"Mode": "Enabled"}
+
         tags = _append_project_tags(tags)
         if tags is not None:
             tune_request["Tags"] = tags
@@ -2684,6 +2695,7 @@ def create_tuning_job(
         training_config_list=None,
         warm_start_config=None,
         tags=None,
+        autotune=False,
     ):
         """Create an Amazon SageMaker hyperparameter tuning job.
 
@@ -2703,6 +2715,8 @@ def create_tuning_job(
                 other required configurations.
             tags (list[dict]): List of tags for labeling the tuning job. For more, see
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+            autotune (bool): Whether the parameter ranges or other unset settings of a tuning job
+                should be chosen automatically.
         """
 
         if training_config is None and training_config_list is None:
@@ -2719,6 +2733,7 @@ def create_tuning_job(
             training_config_list=training_config_list,
             warm_start_config=warm_start_config,
             tags=tags,
+            autotune=autotune,
         )
 
         def submit(request):
@@ -2736,6 +2751,7 @@ def _get_tuning_request(
         training_config_list=None,
         warm_start_config=None,
         tags=None,
+        autotune=False,
     ):
         """Construct CreateHyperParameterTuningJob request
 
@@ -2751,13 +2767,17 @@ def _get_tuning_request(
                 other required configurations.
             tags (list[dict]): List of tags for labeling the tuning job. For more, see
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
+            autotune (bool): Whether the parameter ranges or other unset settings of a tuning job
+                should be chosen automatically.
         Returns:
             dict: A dictionary for CreateHyperParameterTuningJob request
         """
         tune_request = {
             "HyperParameterTuningJobName": job_name,
             "HyperParameterTuningJobConfig": self._map_tuning_config(**tuning_config),
         }
+        if autotune:
+            tune_request["Autotune"] = {"Mode": "Enabled"}
 
         if training_config is not None:
             tune_request["TrainingJobDefinition"] = self._map_training_config(**training_config)
@@ -2803,6 +2823,7 @@ def _map_tuning_config(
         random_seed=None,
         strategy_config=None,
         completion_criteria_config=None,
+        auto_parameters=None,
     ):
         """Construct tuning job configuration dictionary.
 
@@ -2829,6 +2850,8 @@ def _map_tuning_config(
                 strategy.
             completion_criteria_config (dict): A configuration
                 for the completion criteria.
+            auto_parameters (dict): Dictionary of auto parameters. The keys are names of auto
+                parameters and valeus are example values of auto parameters.
 
         Returns:
             A dictionary of tuning job configuration. For format details, please refer to
@@ -2858,6 +2881,13 @@ def _map_tuning_config(
         if parameter_ranges is not None:
             tuning_config["ParameterRanges"] = parameter_ranges
 
+        if auto_parameters is not None:
+            if parameter_ranges is None:
+                tuning_config["ParameterRanges"] = {}
+            tuning_config["ParameterRanges"]["AutoParameters"] = [
+                {"Name": name, "ValueHint": value} for name, value in auto_parameters.items()
+            ]
+
         if strategy_config is not None:
             tuning_config["StrategyConfig"] = strategy_config
 
@@ -2919,6 +2949,7 @@ def _map_training_config(
         checkpoint_local_path=None,
         max_retry_attempts=None,
         environment=None,
+        auto_parameters=None,
     ):
         """Construct a dictionary of training job configuration from the arguments.
 
@@ -3039,6 +3070,13 @@ def _map_training_config(
         if parameter_ranges is not None:
             training_job_definition["HyperParameterRanges"] = parameter_ranges
 
+        if auto_parameters is not None:
+            if parameter_ranges is None:
+                training_job_definition["HyperParameterRanges"] = {}
+            training_job_definition["HyperParameterRanges"]["AutoParameters"] = [
+                {"Name": name, "ValueHint": value} for name, value in auto_parameters.items()
+            ]
+
         if max_retry_attempts is not None:
             training_job_definition["RetryStrategy"] = {"MaximumRetryAttempts": max_retry_attempts}