Improvement of the tuner documentation

src/sagemaker/tuner.py

@@ -11,6 +11,7 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 """Placeholder docstring"""
+
 from __future__ import absolute_import
 
 import importlib
@@ -641,8 +642,11 @@ def __init__(
                 extract the metric from the logs. This should be defined only
                 for hyperparameter tuning jobs that don't use an Amazon
                 algorithm.
-            strategy (str or PipelineVariable): Strategy to be used for hyperparameter estimations
-                (default: 'Bayesian').
+            strategy (str or PipelineVariable): Strategy to be used for hyperparameter estimations.
+                More information about different strategies:
+                https://docs.aws.amazon.com/sagemaker/latest/dg/automatic-model-tuning-how-it-works.html.
+                Available options are: 'Bayesian', 'Random', 'Hyperband',
+                'Grid' (default: 'Bayesian')
             objective_type (str or PipelineVariable): The type of the objective metric for
                 evaluating training jobs. This value can be either 'Minimize' or
                 'Maximize' (default: 'Maximize').
@@ -759,7 +763,8 @@ def __init__(
         self.autotune = autotune
 
     def override_resource_config(
-        self, instance_configs: Union[List[InstanceConfig], Dict[str, List[InstanceConfig]]]
+        self,
+        instance_configs: Union[List[InstanceConfig], Dict[str, List[InstanceConfig]]],
     ):
         """Override the instance configuration of the estimators used by the tuner.
 
@@ -966,7 +971,7 @@ def fit(
         include_cls_metadata: Union[bool, Dict[str, bool]] = False,
         estimator_kwargs: Optional[Dict[str, dict]] = None,
         wait: bool = True,
-        **kwargs
+        **kwargs,
     ):
         """Start a hyperparameter tuning job.
 
@@ -1055,7 +1060,7 @@ def _fit_with_estimator_dict(self, inputs, job_name, include_cls_metadata, estim
             allowed_keys=estimator_names,
         )
 
-        for (estimator_name, estimator) in self.estimator_dict.items():
+        for estimator_name, estimator in self.estimator_dict.items():
             ins = inputs.get(estimator_name, None) if inputs is not None else None
             args = estimator_kwargs.get(estimator_name, {}) if estimator_kwargs is not None else {}
             self._prepare_estimator_for_tuning(estimator, ins, job_name, **args)
@@ -1282,7 +1287,7 @@ def _attach_with_training_details_list(cls, sagemaker_session, estimator_cls, jo
             objective_metric_name_dict=objective_metric_name_dict,
             hyperparameter_ranges_dict=hyperparameter_ranges_dict,
             metric_definitions_dict=metric_definitions_dict,
-            **init_params
+            **init_params,
         )
 
     def deploy(
@@ -1297,7 +1302,7 @@ def deploy(
         model_name=None,
         kms_key=None,
         data_capture_config=None,
-        **kwargs
+        **kwargs,
     ):
         """Deploy the best trained or user specified model to an Amazon SageMaker endpoint.
 
@@ -1363,7 +1368,7 @@ def deploy(
             model_name=model_name,
             kms_key=kms_key,
             data_capture_config=data_capture_config,
-            **kwargs
+            **kwargs,
         )
 
     def stop_tuning_job(self):