@@ -1061,6 +1061,9 @@ def from_local_script(
 accelerator_count: int = 0,
 boot_disk_type: str = "pd-ssd",
 boot_disk_size_gb: int = 100,
+reduction_server_replica_count: int = 0,
+reduction_server_machine_type: Optional[str] = None,
+reduction_server_container_uri: Optional[str] = None,
 base_output_dir: Optional[str] = None,
 project: Optional[str] = None,
 location: Optional[str] = None,
@@ -1127,6 +1130,13 @@ def from_local_script(
 boot_disk_size_gb (int):
 Optional. Size in GB of the boot disk, default is 100GB.
 boot disk size must be within the range of [100, 64000].
+reduction_server_replica_count (int):
+The number of reduction server replicas, default is 0.
+reduction_server_machine_type (str):
+Optional. The type of machine to use for reduction server.
+reduction_server_container_uri (str):
+Optional. The Uri of the reduction server container image.
+See details: https://cloud.google.com/vertex-ai/docs/training/distributed-training#reduce_training_time_with_reduction_server
 base_output_dir (str):
 Optional. GCS output directory of job. If not provided a
 timestamped directory in the staging directory will be used.
@@ -1181,6 +1191,8 @@ def from_local_script(
 accelerator_type=accelerator_type,
 boot_disk_type=boot_disk_type,
 boot_disk_size_gb=boot_disk_size_gb,
+reduction_server_replica_count=reduction_server_replica_count,
+reduction_server_machine_type=reduction_server_machine_type,
 ).pool_specs
 
 python_packager = source_utils._TrainingScriptPythonPackager(
@@ -1191,21 +1203,33 @@ def from_local_script(
 gcs_staging_dir=staging_bucket, project=project, credentials=credentials,
 )
 
-for spec in worker_pool_specs:
-spec["python_package_spec"] = {
-"executor_image_uri": container_uri,
-"python_module": python_packager.module_name,
-"package_uris": [package_gcs_uri],
-}
-
-if args:
-spec["python_package_spec"]["args"] = args
-
-if environment_variables:
-spec["python_package_spec"]["env"] = [
-{"name": key, "value": value}
-for key, value in environment_variables.items()
-]
+for spec_order, spec in enumerate(worker_pool_specs):
+
+if not spec:
+continue
+
+if (
+spec_order == worker_spec_utils._SPEC_ORDERS["server_spec"]
+and reduction_server_replica_count > 0
+):
+spec["container_spec"] = {
+"image_uri": reduction_server_container_uri,
+}
+else:
+spec["python_package_spec"] = {
+"executor_image_uri": container_uri,
+"python_module": python_packager.module_name,
+"package_uris": [package_gcs_uri],
+}
+
+if args:
+spec["python_package_spec"]["args"] = args
+
+if environment_variables:
+spec["python_package_spec"]["env"] = [
+{"name": key, "value": value}
+for key, value in environment_variables.items()
+]
 
 return cls(
 display_name=display_name,