feat: Add support for InstancePlacementConfig in Estimator

src/sagemaker/estimator.py

@@ -186,6 +186,7 @@ def __init__(
         enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
         enable_session_tag_chaining: Optional[Union[bool, PipelineVariable]] = None,
         training_plan: Optional[Union[str, PipelineVariable]] = None,
+        instance_placement_config: Optional[Dict] = None,
         **kwargs,
     ):
         """Initialize an ``EstimatorBase`` instance.
@@ -560,6 +561,21 @@ def __init__(
                 Specifies whether SessionTagChaining is enabled for the training job.
             training_plan (str or PipelineVariable): Optional.
                 Specifies which training plan arn to use for the training job
+            instance_placement_config (dict): Optional.
+                Specifies UltraServer placement configuration for the training job
+
+                .. code:: python
+
+                    instance_placement_config={
+                        "EnableMultipleJobs": True,
+                        "PlacementSpecifications":[
+                            {
+                                "UltraServerId": "ultraserver-1",
+                                "InstanceCount": "2"
+                            }
+                        ]
+                    }
+
         """
         instance_count = renamed_kwargs(
             "train_instance_count", "instance_count", instance_count, kwargs
@@ -813,6 +829,8 @@ def __init__(
 
         self.training_plan = training_plan
 
+        self.instance_placement_config = instance_placement_config
+
         # Internal flag
         self._is_output_path_set_from_default_bucket_and_prefix = False
 
@@ -1997,6 +2015,11 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
         if "TrainingPlanArn" in job_details["ResourceConfig"]:
             init_params["training_plan"] = job_details["ResourceConfig"]["TrainingPlanArn"]
 
+        if "InstancePlacementConfig" in job_details["ResourceConfig"]:
+            init_params["instance_placement_config"] = job_details["ResourceConfig"][
+                "InstancePlacementConfig"
+            ]
+
         has_hps = "HyperParameters" in job_details
         init_params["hyperparameters"] = job_details["HyperParameters"] if has_hps else {}
 
@@ -2882,6 +2905,7 @@ def __init__(
         enable_remote_debug: Optional[Union[bool, PipelineVariable]] = None,
         enable_session_tag_chaining: Optional[Union[bool, PipelineVariable]] = None,
         training_plan: Optional[Union[str, PipelineVariable]] = None,
+        instance_placement_config: Optional[Dict] = None,
         **kwargs,
     ):
         """Initialize an ``Estimator`` instance.
@@ -3249,6 +3273,20 @@ def __init__(
                  Specifies whether SessionTagChaining is enabled for the training job
             training_plan (str or PipelineVariable): Optional.
                 Specifies which training plan arn to use for the training job
+            instance_placement_config (dict): Optional.
+                Specifies UltraServer placement configuration for the training job
+
+                .. code:: python
+
+                    instance_placement_config={
+                        "EnableMultipleJobs": True,
+                        "PlacementSpecifications":[
+                            {
+                                "UltraServerId": "ultraserver-1",
+                                "InstanceCount": "2"
+                            }
+                        ]
+                    }
         """
         self.image_uri = image_uri
         self._hyperparameters = hyperparameters.copy() if hyperparameters else {}
@@ -3303,6 +3341,7 @@ def __init__(
             enable_remote_debug=enable_remote_debug,
             enable_session_tag_chaining=enable_session_tag_chaining,
             training_plan=training_plan,
+            instance_placement_config=instance_placement_config,
             **kwargs,
         )
 

src/sagemaker/job.py

@@ -85,6 +85,7 @@ def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
             estimator.volume_kms_key,
             estimator.keep_alive_period_in_seconds,
             estimator.training_plan,
+            estimator.instance_placement_config,
         )
         stop_condition = _Job._prepare_stop_condition(estimator.max_run, estimator.max_wait)
         vpc_config = estimator.get_vpc_config()
@@ -333,6 +334,7 @@ def _prepare_resource_config(
         volume_kms_key,
         keep_alive_period_in_seconds,
         training_plan,
+        instance_placement_config=None,
     ):
         """Placeholder docstring"""
         resource_config = {
@@ -360,6 +362,8 @@ def _prepare_resource_config(
             resource_config["InstanceType"] = instance_type
         if training_plan is not None:
             resource_config["TrainingPlanArn"] = training_plan
+        if instance_placement_config is not None:
+            resource_config["InstancePlacementConfig"] = instance_placement_config
 
         return resource_config
 

src/sagemaker/jumpstart/estimator.py

@@ -119,6 +119,7 @@ def __init__(
         config_name: Optional[str] = None,
         enable_session_tag_chaining: Optional[Union[bool, PipelineVariable]] = None,
         training_plan: Optional[Union[str, PipelineVariable]] = None,
+        instance_placement_config: Optional[Dict] = None,
     ):
         """Initializes a ``JumpStartEstimator``.
 
@@ -517,6 +518,20 @@ def __init__(
                 Specifies whether SessionTagChaining is enabled for the training job
             training_plan (str or PipelineVariable): Optional.
                 Specifies which training plan arn to use for the training job
+            instance_placement_config (dict): Optional.
+                Specifies UltraServer placement configuration for the training job
+
+                .. code:: python
+
+                    instance_placement_config={
+                        "EnableMultipleJobs": True,
+                        "PlacementSpecifications":[
+                            {
+                                "UltraServerId": "ultraserver-1",
+                                "InstanceCount": "2"
+                            }
+                        ]
+                    }
 
         Raises:
             ValueError: If the model ID is not recognized by JumpStart.
@@ -606,6 +621,7 @@ def _validate_model_id_and_get_type_hook():
             config_name=config_name,
             enable_session_tag_chaining=enable_session_tag_chaining,
             training_plan=training_plan,
+            instance_placement_config=instance_placement_config,
         )
 
         self.hub_arn = estimator_init_kwargs.hub_arn

src/sagemaker/jumpstart/factory/estimator.py

@@ -145,6 +145,7 @@ def get_init_kwargs(
     config_name: Optional[str] = None,
     enable_session_tag_chaining: Optional[Union[bool, PipelineVariable]] = None,
     training_plan: Optional[Union[str, PipelineVariable]] = None,
+    instance_placement_config: Optional[Dict] = None,
 ) -> JumpStartEstimatorInitKwargs:
     """Returns kwargs required to instantiate `sagemaker.estimator.Estimator` object."""
 
@@ -207,6 +208,7 @@ def get_init_kwargs(
         config_name=config_name,
         enable_session_tag_chaining=enable_session_tag_chaining,
         training_plan=training_plan,
+        instance_placement_config=instance_placement_config,
     )
 
     estimator_init_kwargs, orig_session = _set_temp_sagemaker_session_if_not_set(

src/sagemaker/jumpstart/types.py

@@ -2445,6 +2445,7 @@ class JumpStartEstimatorInitKwargs(JumpStartKwargs):
         "model_reference_arn",
         "specs",
         "training_plan",
+        "instance_placement_config",
     ]
 
     SERIALIZATION_EXCLUSION_SET = {
@@ -2519,6 +2520,7 @@ def __init__(
         config_name: Optional[str] = None,
         enable_session_tag_chaining: Optional[Union[bool, PipelineVariable]] = None,
         training_plan: Optional[Union[str, PipelineVariable]] = None,
+        instance_placement_config: Optional[Dict] = None,
     ) -> None:
         """Instantiates JumpStartEstimatorInitKwargs object."""
 
@@ -2582,6 +2584,7 @@ def __init__(
         self.config_name = config_name
         self.enable_session_tag_chaining = enable_session_tag_chaining
         self.training_plan = training_plan
+        self.instance_placement_config = instance_placement_config
 
 
 class JumpStartEstimatorFitKwargs(JumpStartKwargs):

tests/unit/test_estimator.py

@@ -76,6 +76,8 @@
 )
 from sagemaker.model_life_cycle import ModelLifeCycle
 
+from tests.unit.test_job import INSTANCE_PLACEMENT_CONFIG
+
 MODEL_DATA = "s3://bucket/model.tar.gz"
 MODEL_IMAGE = "mi"
 ENTRY_POINT = "blah.py"
@@ -879,6 +881,22 @@ def test_framework_with_training_plan(sagemaker_session):
     assert args["resource_config"]["TrainingPlanArn"] == TRAINING_PLAN
 
 
+def test_framework_with_instance_placement(sagemaker_session):
+    f = DummyFramework(
+        entry_point=SCRIPT_PATH,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        instance_type="ml.c4.xlarge",
+        instance_count=2,
+        training_plan=TRAINING_PLAN,
+        instance_placement_config=INSTANCE_PLACEMENT_CONFIG,
+    )
+    f.fit("s3://mydata")
+    sagemaker_session.train.assert_called_once()
+    _, args = sagemaker_session.train.call_args
+    assert args["resource_config"]["InstancePlacementConfig"] == INSTANCE_PLACEMENT_CONFIG
+
+
 def test_framework_with_both_training_repository_config(sagemaker_session):
     f = DummyFramework(
         entry_point=SCRIPT_PATH,

tests/unit/test_job.py

@@ -32,6 +32,10 @@
 INSTANCE_TYPE = "c4.4xlarge"
 KEEP_ALIVE_PERIOD = 1800
 TRAINING_PLAN = "arn:aws:sagemaker:us-west-2:336:training-plan/test_training_plan"
+INSTANCE_PLACEMENT_CONFIG = {
+    "EnableMultipleJobs": True,
+    "PlacementSpecifications": [{"UltraServerId": "us-1", "InstanceCount": "2"}],
+}
 INSTANCE_GROUP = InstanceGroup("group", "ml.c4.xlarge", 1)
 VOLUME_SIZE = 1
 MAX_RUNTIME = 1
@@ -756,6 +760,28 @@ def test_prepare_resource_config_with_training_plan():
     }
 
 
+def test_prepare_resource_config_with_placement_config():
+    resource_config = _Job._prepare_resource_config(
+        INSTANCE_COUNT,
+        INSTANCE_TYPE,
+        None,
+        VOLUME_SIZE,
+        VOLUME_KMS_KEY,
+        None,
+        TRAINING_PLAN,
+        INSTANCE_PLACEMENT_CONFIG,
+    )
+
+    assert resource_config == {
+        "InstanceCount": INSTANCE_COUNT,
+        "InstanceType": INSTANCE_TYPE,
+        "VolumeSizeInGB": VOLUME_SIZE,
+        "VolumeKmsKeyId": VOLUME_KMS_KEY,
+        "TrainingPlanArn": TRAINING_PLAN,
+        "InstancePlacementConfig": INSTANCE_PLACEMENT_CONFIG,
+    }
+
+
 def test_prepare_resource_config_with_keep_alive_period():
     resource_config = _Job._prepare_resource_config(
         INSTANCE_COUNT,