Aligned disable_output_compression for @remote with Estimator

src/sagemaker/remote_function/client.py

@@ -90,6 +90,7 @@ def remote(
     spark_config: SparkConfig = None,
     use_spot_instances=False,
     max_wait_time_in_seconds=None,
+    disable_output_compression: bool = False,
     use_torchrun: bool = False,
     use_mpirun: bool = False,
     nproc_per_node: Optional[int] = None,
@@ -283,13 +284,16 @@ def remote(
           After this amount of time Amazon SageMaker will stop waiting for managed spot training
           job to complete. Defaults to ``None``.
 
+        disable_output_compression (bool): Optional. When set to true, Model is uploaded to
+          Amazon S3 without compression after training finishes.
+
         use_torchrun (bool): Specifies whether to use torchrun for distributed training.
           Defaults to ``False``.
 
         use_mpirun (bool): Specifies whether to use mpirun for distributed training.
           Defaults to ``False``.
 
-        nproc_per_node (Optional int): Specifies the number of processes per node for
+        nproc_per_node (int): Optional. Specifies the number of processes per node for
           distributed training. Defaults to ``None``.
           This is defined automatically configured on the instance type.
     """
@@ -324,6 +328,7 @@ def _remote(func):
             spark_config=spark_config,
             use_spot_instances=use_spot_instances,
             max_wait_time_in_seconds=max_wait_time_in_seconds,
+            disable_output_compression=disable_output_compression,
             use_torchrun=use_torchrun,
             use_mpirun=use_mpirun,
             nproc_per_node=nproc_per_node,
@@ -543,6 +548,7 @@ def __init__(
         spark_config: SparkConfig = None,
         use_spot_instances=False,
         max_wait_time_in_seconds=None,
+        disable_output_compression: bool = False,
         use_torchrun: bool = False,
         use_mpirun: bool = False,
         nproc_per_node: Optional[int] = None,
@@ -736,13 +742,16 @@ def __init__(
               After this amount of time Amazon SageMaker will stop waiting for managed spot training
               job to complete. Defaults to ``None``.
 
+            disable_output_compression (bool): Optional. When set to true, Model is uploaded to
+              Amazon S3 without compression after training finishes.
+
             use_torchrun (bool): Specifies whether to use torchrun for distributed training.
               Defaults to ``False``.
 
             use_mpirun (bool): Specifies whether to use mpirun for distributed training.
               Defaults to ``False``.
 
-            nproc_per_node (Optional int): Specifies the number of processes per node for
+            nproc_per_node (int): Optional. Specifies the number of processes per node for
               distributed training. Defaults to ``None``.
               This is defined automatically configured on the instance type.
         """
@@ -790,6 +799,7 @@ def __init__(
             spark_config=spark_config,
             use_spot_instances=use_spot_instances,
             max_wait_time_in_seconds=max_wait_time_in_seconds,
+            disable_output_compression=disable_output_compression,
             use_torchrun=use_torchrun,
             use_mpirun=use_mpirun,
             nproc_per_node=nproc_per_node,

src/sagemaker/remote_function/job.py

@@ -373,6 +373,7 @@ def __init__(
         spark_config: SparkConfig = None,
         use_spot_instances=False,
         max_wait_time_in_seconds=None,
+        disable_output_compression: bool = False,
         use_torchrun: bool = False,
         use_mpirun: bool = False,
         nproc_per_node: Optional[int] = None,
@@ -558,13 +559,16 @@ def __init__(
               After this amount of time Amazon SageMaker will stop waiting for managed spot
               training job to complete. Defaults to ``None``.
 
+            disable_output_compression (bool): Optional. When set to true, Model is uploaded to
+              Amazon S3 without compression after training finishes.
+
             use_torchrun (bool): Specifies whether to use torchrun for distributed training.
               Defaults to ``False``.
 
             use_mpirun (bool): Specifies whether to use mpirun for distributed training.
               Defaults to ``False``.
 
-            nproc_per_node (Optional int): Specifies the number of processes per node for
+            nproc_per_node (int): Optional. Specifies the number of processes per node for
               distributed training. Defaults to ``None``.
               This is defined automatically configured on the instance type.
         """
@@ -725,6 +729,7 @@ def __init__(
         tags = format_tags(tags)
         self.tags = self.sagemaker_session._append_sagemaker_config_tags(tags, REMOTE_FUNCTION_TAGS)
 
+        self.disable_output_compression = disable_output_compression
         self.use_torchrun = use_torchrun
         self.use_mpirun = use_mpirun
         self.nproc_per_node = nproc_per_node
@@ -954,6 +959,8 @@ def compile(
             output_config = {"S3OutputPath": s3_base_uri}
         if job_settings.s3_kms_key is not None:
             output_config["KmsKeyId"] = job_settings.s3_kms_key
+        if job_settings.disable_output_compression:
+            output_config["CompressionType"] = "NONE"
         request_dict["OutputDataConfig"] = output_config
 
         container_args = ["--s3_base_uri", s3_base_uri]

tests/unit/sagemaker/feature_store/feature_processor/test_feature_scheduler.py

@@ -907,6 +907,7 @@ def test_remote_decorator_fields_consistency(get_execution_role, session):
         "use_spot_instances",
         "max_wait_time_in_seconds",
         "custom_file_filter",
+        "disable_output_compression",
         "use_torchrun",
         "use_mpirun",
         "nproc_per_node",

tests/unit/sagemaker/remote_function/test_client.py

@@ -1504,6 +1504,7 @@ def test_consistency_between_remote_and_step_decorator():
         "s3_kms_key",
         "s3_root_uri",
         "sagemaker_session",
+        "disable_output_compression",
         "use_torchrun",
         "use_mpirun",
         "nproc_per_node",

tests/unit/sagemaker/remote_function/test_job.py

@@ -291,24 +291,47 @@ def mock_get_current_run():
     return current_run
 
 
-def describe_training_job_response(job_status):
-    return {
+def describe_training_job_response(job_status, disable_output_compression=False):
+    job_response = {
         "TrainingJobArn": TRAINING_JOB_ARN,
         "TrainingJobStatus": job_status,
         "ResourceConfig": {
             "InstanceCount": 1,
             "InstanceType": "ml.c4.xlarge",
             "VolumeSizeInGB": 30,
         },
-        "OutputDataConfig": {"S3OutputPath": "s3://sagemaker-123/image_uri/output"},
     }
 
+    if disable_output_compression:
+        output_config = {
+            "S3OutputPath": "s3://sagemaker-123/image_uri/output",
+            "CompressionType": "NONE",
+        }
+    else:
+        output_config = {
+            "S3OutputPath": "s3://sagemaker-123/image_uri/output",
+            "CompressionType": "NONE",
+        }
+
+    job_response["OutputDataConfig"] = output_config
+
+    return job_response
+
 
 COMPLETED_TRAINING_JOB = describe_training_job_response("Completed")
 INPROGRESS_TRAINING_JOB = describe_training_job_response("InProgress")
 CANCELLED_TRAINING_JOB = describe_training_job_response("Stopped")
 FAILED_TRAINING_JOB = describe_training_job_response("Failed")
 
+COMPLETED_TRAINING_JOB_DISABLE_OUTPUT_COMPRESSION = describe_training_job_response(
+    "Completed", True
+)
+INPROGRESS_TRAINING_JOB_DISABLE_OUTPUT_COMPRESSION = describe_training_job_response(
+    "InProgress", True
+)
+CANCELLED_TRAINING_JOB_DISABLE_OUTPUT_COMPRESSION = describe_training_job_response("Stopped", True)
+FAILED_TRAINING_JOB_DISABLE_OUTPUT_COMPRESSION = describe_training_job_response("Failed", True)
+
 
 def mock_session():
     session = Mock()
@@ -1303,6 +1326,27 @@ def test_describe(session, *args):
     session().sagemaker_client.describe_training_job.assert_called_once()
 
 
+@patch("sagemaker.remote_function.job._prepare_and_upload_runtime_scripts")
+@patch("sagemaker.remote_function.job._prepare_and_upload_workspace")
+@patch("sagemaker.remote_function.job.StoredFunction")
+@patch("sagemaker.remote_function.job.Session", return_value=mock_session())
+def test_describe_disable_output_compression(session, *args):
+
+    job_settings = _JobSettings(
+        image_uri=IMAGE,
+        s3_root_uri=S3_URI,
+        role=ROLE_ARN,
+        instance_type="ml.m5.large",
+        disable_output_compression=True,
+    )
+    job = _Job.start(job_settings, job_function, func_args=(1, 2), func_kwargs={"c": 3, "d": 4})
+
+    job.describe()
+    assert job.describe() == COMPLETED_TRAINING_JOB_DISABLE_OUTPUT_COMPRESSION
+
+    session().sagemaker_client.describe_training_job.assert_called_once()
+
+
 @patch("sagemaker.remote_function.job._prepare_and_upload_runtime_scripts")
 @patch("sagemaker.remote_function.job._prepare_and_upload_workspace")
 @patch("sagemaker.remote_function.job.StoredFunction")