feature: support of the intelligent stopping in the tuner (aws#3652)

Co-authored-by: Anton Repushko <[email protected]>

Author: 2 people

src/sagemaker/session.py

@@ -2189,7 +2189,9 @@ def tune(  # noqa: C901
         stop_condition,
         tags,
         warm_start_config,
+        max_runtime_in_seconds=None,
         strategy_config=None,
+        completion_criteria_config=None,
         enable_network_isolation=False,
         image_uri=None,
         algorithm_arn=None,
@@ -2256,6 +2258,10 @@ def tune(  # noqa: C901
                 https://docs.aws.amazon.com/sagemaker/latest/dg/API_Tag.html.
             warm_start_config (dict): Configuration defining the type of warm start and
                 other required configurations.
+            max_runtime_in_seconds (int or PipelineVariable): The maximum time in seconds
+                that a training job launched by a hyperparameter tuning job can run.
+            completion_criteria_config (sagemaker.tuner.TuningJobCompletionCriteriaConfig): A
+                configuration for the completion criteria.
             early_stopping_type (str): Specifies whether early stopping is enabled for the job.
                 Can be either 'Auto' or 'Off'. If set to 'Off', early stopping will not be
                 attempted. If set to 'Auto', early stopping of some training jobs may happen, but
@@ -2311,12 +2317,14 @@ def tune(  # noqa: C901
                 strategy=strategy,
                 max_jobs=max_jobs,
                 max_parallel_jobs=max_parallel_jobs,
+                max_runtime_in_seconds=max_runtime_in_seconds,
                 objective_type=objective_type,
                 objective_metric_name=objective_metric_name,
                 parameter_ranges=parameter_ranges,
                 early_stopping_type=early_stopping_type,
                 random_seed=random_seed,
                 strategy_config=strategy_config,
+                completion_criteria_config=completion_criteria_config,
             ),
             "TrainingJobDefinition": self._map_training_config(
                 static_hyperparameters=static_hyperparameters,
@@ -2470,12 +2478,14 @@ def _map_tuning_config(
         strategy,
         max_jobs,
         max_parallel_jobs,
+        max_runtime_in_seconds=None,
         early_stopping_type="Off",
         objective_type=None,
         objective_metric_name=None,
         parameter_ranges=None,
         random_seed=None,
         strategy_config=None,
+        completion_criteria_config=None,
     ):
         """Construct tuning job configuration dictionary.
 
@@ -2484,6 +2494,8 @@ def _map_tuning_config(
             max_jobs (int): Maximum total number of training jobs to start for the hyperparameter
                 tuning job.
             max_parallel_jobs (int): Maximum number of parallel training jobs to start.
+            max_runtime_in_seconds (int or PipelineVariable): The maximum time in seconds
+                that a training job launched by a hyperparameter tuning job can run.
             early_stopping_type (str): Specifies whether early stopping is enabled for the job.
                 Can be either 'Auto' or 'Off'. If set to 'Off', early stopping will not be
                 attempted. If set to 'Auto', early stopping of some training jobs may happen,
@@ -2498,6 +2510,8 @@ def _map_tuning_config(
                 produce more consistent configurations for the same tuning job.
             strategy_config (dict): A configuration for the hyperparameter tuning job optimisation
                 strategy.
+            completion_criteria_config (dict): A configuration
+                for the completion criteria.
 
         Returns:
             A dictionary of tuning job configuration. For format details, please refer to
@@ -2514,6 +2528,9 @@ def _map_tuning_config(
             "TrainingJobEarlyStoppingType": early_stopping_type,
         }
 
+        if max_runtime_in_seconds is not None:
+            tuning_config["ResourceLimits"]["MaxRuntimeInSeconds"] = max_runtime_in_seconds
+
         if random_seed is not None:
             tuning_config["RandomSeed"] = random_seed
 
@@ -2526,6 +2543,9 @@ def _map_tuning_config(
 
         if strategy_config is not None:
             tuning_config["StrategyConfig"] = strategy_config
+
+        if completion_criteria_config is not None:
+            tuning_config["TuningJobCompletionCriteria"] = completion_criteria_config
         return tuning_config
 
     @classmethod