feature: support remote debug for sagemaker training job

src/sagemaker/estimator.py

@@ -178,6 +178,7 @@ def __init__(
         container_entry_point: Optional[List[str]] = None,
         container_arguments: Optional[List[str]] = None,
         disable_output_compression: bool = False,
+        enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
         **kwargs,
     ):
         """Initialize an ``EstimatorBase`` instance.
@@ -540,6 +541,8 @@ def __init__(
                 to Amazon S3 without compression after training finishes.
             enable_infra_check (bool or PipelineVariable): Optional.
                 Specifies whether it is running Sagemaker built-in infra check jobs.
+            enable_remote_debug (bool or PipelineVariable): Optional.
+                Specifies whether RemoteDebug is enabled for the training job
         """
         instance_count = renamed_kwargs(
             "train_instance_count", "instance_count", instance_count, kwargs
@@ -777,6 +780,8 @@ def __init__(
 
         self.tensorboard_app = TensorBoardApp(region=self.sagemaker_session.boto_region_name)
 
+        self._enable_remote_debug = enable_remote_debug
+
     @abstractmethod
     def training_image_uri(self):
         """Return the Docker image to use for training.
@@ -1958,6 +1963,11 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
             max_wait = job_details.get("StoppingCondition", {}).get("MaxWaitTimeInSeconds")
             if max_wait:
                 init_params["max_wait"] = max_wait
+
+        if "RemoteDebugConfig" in job_details:
+            init_params["enable_remote_debug"] = job_details["RemoteDebugConfig"].get(
+                "EnableRemoteDebug"
+            )
         return init_params
 
     def _get_instance_type(self):
@@ -2292,6 +2302,32 @@ def update_profiler(
 
         _TrainingJob.update(self, profiler_rule_configs, profiler_config_request_dict)
 
+    def get_remote_debug_config(self):
+        """dict: Return the configuration of RemoteDebug"""
+        return (
+            None
+            if self._enable_remote_debug is None
+            else {"EnableRemoteDebug": self._enable_remote_debug}
+        )
+
+    def enable_remote_debug(self):
+        """Enable remote debug for a training job."""
+        self._update_remote_debug(True)
+
+    def disable_remote_debug(self):
+        """Disable remote debug for a training job."""
+        self._update_remote_debug(False)
+
+    def _update_remote_debug(self, enable_remote_debug: bool):
+        """Update to enable or disable remote debug for a training job.
+
+        This method updates the ``_enable_remote_debug`` parameter
+        and enables or disables remote debug for a training job
+        """
+        self._ensure_latest_training_job()
+        _TrainingJob.update(self, remote_debug_config={"EnableRemoteDebug": enable_remote_debug})
+        self._enable_remote_debug = enable_remote_debug
+
     def get_app_url(
         self,
         app_type,
@@ -2520,6 +2556,9 @@ def _get_train_args(cls, estimator, inputs, experiment_config):
         if estimator.profiler_config:
             train_args["profiler_config"] = estimator.profiler_config._to_request_dict()
 
+        if estimator.get_remote_debug_config() is not None:
+            train_args["remote_debug_config"] = estimator.get_remote_debug_config()
+
         return train_args
 
     @classmethod
@@ -2549,7 +2588,12 @@ def _is_local_channel(cls, input_uri):
 
     @classmethod
     def update(
-        cls, estimator, profiler_rule_configs=None, profiler_config=None, resource_config=None
+        cls,
+        estimator,
+        profiler_rule_configs=None,
+        profiler_config=None,
+        resource_config=None,
+        remote_debug_config=None,
     ):
         """Update a running Amazon SageMaker training job.
 
@@ -2562,20 +2606,31 @@ def update(
             resource_config (dict): Configuration of the resources for the training job. You can
                 update the keep-alive period if the warm pool status is `Available`. No other fields
                 can be updated. (default: None).
+            remote_debug_config (dict): Configuration for RemoteDebug. (default: ``None``)
+                The dict can contain 'EnableRemoteDebug'(bool).
+                For example,
+
+                .. code:: python
+
+                    remote_debug_config = {
+                        "EnableRemoteDebug": True,
+                    } (default: None).
 
         Returns:
             sagemaker.estimator._TrainingJob: Constructed object that captures
             all information about the updated training job.
         """
         update_args = cls._get_update_args(
-            estimator, profiler_rule_configs, profiler_config, resource_config
+            estimator, profiler_rule_configs, profiler_config, resource_config, remote_debug_config
         )
         estimator.sagemaker_session.update_training_job(**update_args)
 
         return estimator.latest_training_job
 
     @classmethod
-    def _get_update_args(cls, estimator, profiler_rule_configs, profiler_config, resource_config):
+    def _get_update_args(
+        cls, estimator, profiler_rule_configs, profiler_config, resource_config, remote_debug_config
+    ):
         """Constructs a dict of arguments for updating an Amazon SageMaker training job.
 
         Args:
@@ -2596,6 +2651,7 @@ def _get_update_args(cls, estimator, profiler_rule_configs, profiler_config, res
         update_args.update(build_dict("profiler_rule_configs", profiler_rule_configs))
         update_args.update(build_dict("profiler_config", profiler_config))
         update_args.update(build_dict("resource_config", resource_config))
+        update_args.update(build_dict("remote_debug_config", remote_debug_config))
 
         return update_args
 
@@ -2694,6 +2750,7 @@ def __init__(
         container_arguments: Optional[List[str]] = None,
         disable_output_compression: bool = False,
         enable_infra_check: Optional[Union[bool, PipelineVariable]] = None,
+        enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
         **kwargs,
     ):
         """Initialize an ``Estimator`` instance.
@@ -3055,6 +3112,8 @@ def __init__(
                 to Amazon S3 without compression after training finishes.
             enable_infra_check (bool or PipelineVariable): Optional.
                 Specifies whether it is running Sagemaker built-in infra check jobs.
+            enable_remote_debug (bool or PipelineVariable): Optional.
+                Specifies whether RemoteDebug is enabled for the training job
         """
         self.image_uri = image_uri
         self._hyperparameters = hyperparameters.copy() if hyperparameters else {}
@@ -3106,6 +3165,7 @@ def __init__(
             container_entry_point=container_entry_point,
             container_arguments=container_arguments,
             disable_output_compression=disable_output_compression,
+            enable_remote_debug=enable_remote_debug,
             **kwargs,
         )
 

src/sagemaker/jumpstart/estimator.py

@@ -106,6 +106,7 @@ def __init__(
         container_entry_point: Optional[List[str]] = None,
         container_arguments: Optional[List[str]] = None,
         disable_output_compression: Optional[bool] = None,
+        enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
     ):
         """Initializes a ``JumpStartEstimator``.
 
@@ -495,6 +496,8 @@ def __init__(
                 a training job.
             disable_output_compression (Optional[bool]): When set to true, Model is uploaded
                 to Amazon S3 without compression after training finishes.
+            enable_remote_debug (bool or PipelineVariable): Optional.
+                Specifies whether RemoteDebug is enabled for the training job
 
         Raises:
             ValueError: If the model ID is not recognized by JumpStart.
@@ -569,6 +572,7 @@ def _is_valid_model_id_hook():
             container_arguments=container_arguments,
             disable_output_compression=disable_output_compression,
             enable_infra_check=enable_infra_check,
+            enable_remote_debug=enable_remote_debug,
         )
 
         self.model_id = estimator_init_kwargs.model_id

src/sagemaker/jumpstart/factory/estimator.py

@@ -127,6 +127,7 @@ def get_init_kwargs(
     container_arguments: Optional[List[str]] = None,
     disable_output_compression: Optional[bool] = None,
     enable_infra_check: Optional[Union[bool, PipelineVariable]] = None,
+    enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
 ) -> JumpStartEstimatorInitKwargs:
     """Returns kwargs required to instantiate `sagemaker.estimator.Estimator` object."""
 
@@ -183,6 +184,7 @@ def get_init_kwargs(
         container_arguments=container_arguments,
         disable_output_compression=disable_output_compression,
         enable_infra_check=enable_infra_check,
+        enable_remote_debug=enable_remote_debug,
     )
 
     estimator_init_kwargs = _add_model_version_to_kwargs(estimator_init_kwargs)

src/sagemaker/jumpstart/types.py

@@ -1280,6 +1280,7 @@ class JumpStartEstimatorInitKwargs(JumpStartKwargs):
         "container_arguments",
         "disable_output_compression",
         "enable_infra_check",
+        "enable_remote_debug",
     ]
 
     SERIALIZATION_EXCLUSION_SET = {
@@ -1344,6 +1345,7 @@ def __init__(
         container_arguments: Optional[List[str]] = None,
         disable_output_compression: Optional[bool] = None,
         enable_infra_check: Optional[Union[bool, PipelineVariable]] = None,
+        enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
     ) -> None:
         """Instantiates JumpStartEstimatorInitKwargs object."""
 
@@ -1401,6 +1403,7 @@ def __init__(
         self.container_arguments = container_arguments
         self.disable_output_compression = disable_output_compression
         self.enable_infra_check = enable_infra_check
+        self.enable_remote_debug = enable_remote_debug
 
 
 class JumpStartEstimatorFitKwargs(JumpStartKwargs):

src/sagemaker/session.py

@@ -748,6 +748,7 @@ def train(  # noqa: C901
         profiler_config=None,
         environment: Optional[Dict[str, str]] = None,
         retry_strategy=None,
+        remote_debug_config=None,
     ):
         """Create an Amazon SageMaker training job.
 
@@ -858,6 +859,15 @@ def train(  # noqa: C901
                 configurations.src/sagemaker/lineage/artifact.py:285
             profiler_config (dict): Configuration for how profiling information is emitted
                 with SageMaker Profiler. (default: ``None``).
+            remote_debug_config(dict): Configuration for RemoteDebug. (default: ``None``)
+                The dict can contain 'EnableRemoteDebug'(bool).
+                For example,
+
+                .. code:: python
+
+                    remote_debug_config = {
+                        "EnableRemoteDebug": True,
+                    }
             environment (dict[str, str]) : Environment variables to be set for
                 use during training job (default: ``None``)
             retry_strategy(dict): Defines RetryStrategy for InternalServerFailures.
@@ -950,6 +960,7 @@ def train(  # noqa: C901
             enable_sagemaker_metrics=enable_sagemaker_metrics,
             profiler_rule_configs=profiler_rule_configs,
             profiler_config=inferred_profiler_config,
+            remote_debug_config=remote_debug_config,
             environment=environment,
             retry_strategy=retry_strategy,
         )
@@ -992,6 +1003,7 @@ def _get_train_request(  # noqa: C901
         enable_sagemaker_metrics=None,
         profiler_rule_configs=None,
         profiler_config=None,
+        remote_debug_config=None,
         environment=None,
         retry_strategy=None,
     ):
@@ -1103,6 +1115,15 @@ def _get_train_request(  # noqa: C901
             profiler_rule_configs (list[dict]): A list of profiler rule configurations.
             profiler_config(dict): Configuration for how profiling information is emitted with
                 SageMaker Profiler. (default: ``None``).
+            remote_debug_config(dict): Configuration for RemoteDebug. (default: ``None``)
+                The dict can contain 'EnableRemoteDebug'(bool).
+                For example,
+
+                .. code:: python
+
+                    remote_debug_config = {
+                        "EnableRemoteDebug": True,
+                    }
             environment (dict[str, str]) : Environment variables to be set for
                 use during training job (default: ``None``)
             retry_strategy(dict): Defines RetryStrategy for InternalServerFailures.
@@ -1206,6 +1227,9 @@ def _get_train_request(  # noqa: C901
         if profiler_config is not None:
             train_request["ProfilerConfig"] = profiler_config
 
+        if remote_debug_config is not None:
+            train_request["RemoteDebugConfig"] = remote_debug_config
+
         if retry_strategy is not None:
             train_request["RetryStrategy"] = retry_strategy
 
@@ -1217,6 +1241,7 @@ def update_training_job(
         profiler_rule_configs=None,
         profiler_config=None,
         resource_config=None,
+        remote_debug_config=None,
     ):
         """Calls the UpdateTrainingJob API for the given job name and returns the response.
 
@@ -1228,6 +1253,15 @@ def update_training_job(
             resource_config (dict): Configuration of the resources for the training job. You can
                 update the keep-alive period if the warm pool status is `Available`. No other fields
                 can be updated. (default: ``None``).
+            remote_debug_config(dict): Configuration for RemoteDebug. (default: ``None``)
+                The dict can contain 'EnableRemoteDebug'(bool).
+                For example,
+
+                .. code:: python
+
+                    remote_debug_config = {
+                        "EnableRemoteDebug": True,
+                    }
         """
         # No injections from sagemaker_config because the UpdateTrainingJob API's resource_config
         # object accepts fewer parameters than the CreateTrainingJob API, and none that the
@@ -1240,6 +1274,7 @@ def update_training_job(
             profiler_rule_configs=profiler_rule_configs,
             profiler_config=inferred_profiler_config,
             resource_config=resource_config,
+            remote_debug_config=remote_debug_config,
         )
         LOGGER.info("Updating training job with name %s", job_name)
         LOGGER.debug("Update request: %s", json.dumps(update_training_job_request, indent=4))
@@ -1251,6 +1286,7 @@ def _get_update_training_job_request(
         profiler_rule_configs=None,
         profiler_config=None,
         resource_config=None,
+        remote_debug_config=None,
     ):
         """Constructs a request compatible for updating an Amazon SageMaker training job.
 
@@ -1262,6 +1298,15 @@ def _get_update_training_job_request(
             resource_config (dict): Configuration of the resources for the training job. You can
                 update the keep-alive period if the warm pool status is `Available`. No other fields
                 can be updated. (default: ``None``).
+            remote_debug_config(dict): Configuration for RemoteDebug. (default: ``None``)
+                The dict can contain 'EnableRemoteDebug'(bool).
+                For example,
+
+                .. code:: python
+
+                    remote_debug_config = {
+                        "EnableRemoteDebug": True,
+                    }
 
         Returns:
             Dict: an update training request dict
@@ -1279,6 +1324,9 @@ def _get_update_training_job_request(
         if resource_config is not None:
             update_training_job_request["ResourceConfig"] = resource_config
 
+        if remote_debug_config is not None:
+            update_training_job_request["RemoteDebugConfig"] = remote_debug_config
+
         return update_training_job_request
 
     def process(