Merge branch 'master' into processing-job-codeartifact-support

Author: akuma12

src/sagemaker/enums.py

@@ -28,3 +28,15 @@ class EndpointType(Enum):
     INFERENCE_COMPONENT_BASED = (
         "InferenceComponentBased"  # Amazon SageMaker Inference Component Based Endpoint
     )
+
+
+class RoutingStrategy(Enum):
+    """Strategy for routing https traffics."""
+
+    RANDOM = "RANDOM"
+    """The endpoint routes each request to a randomly chosen instance.
+    """
+    LEAST_OUTSTANDING_REQUESTS = "LEAST_OUTSTANDING_REQUESTS"
+    """The endpoint routes requests to the specific instances that have
+    more capacity to process them.
+    """

src/sagemaker/huggingface/model.py

@@ -334,6 +334,7 @@ def deploy(
             endpoint_type=kwargs.get("endpoint_type", None),
             resources=kwargs.get("resources", None),
             managed_instance_scaling=kwargs.get("managed_instance_scaling", None),
+            routing_config=kwargs.get("routing_config", None),
         )
 
     def register(

src/sagemaker/image_uri_config/pytorch.json

@@ -1315,7 +1315,8 @@
             "1.13": "1.13.1",
             "2.0": "2.0.1",
             "2.1": "2.1.0",
-            "2.2": "2.2.0"
+            "2.2": "2.2.0",
+            "2.3": "2.3.0"
         },
         "versions": {
             "0.4.0": {
@@ -2288,6 +2289,47 @@
                     "us-west-2": "763104351884"
                 },
                 "repository": "pytorch-training"
+            },
+            "2.3.0": {
+                "py_versions": [
+                    "py311"
+                ],
+                "registries": {
+                    "af-south-1": "626614931356",
+                    "ap-east-1": "871362719292",
+                    "ap-northeast-1": "763104351884",
+                    "ap-northeast-2": "763104351884",
+                    "ap-northeast-3": "364406365360",
+                    "ap-south-1": "763104351884",
+                    "ap-south-2": "772153158452",
+                    "ap-southeast-1": "763104351884",
+                    "ap-southeast-2": "763104351884",
+                    "ap-southeast-3": "907027046896",
+                    "ap-southeast-4": "457447274322",
+                    "ca-central-1": "763104351884",
+                    "ca-west-1": "204538143572",
+                    "cn-north-1": "727897471807",
+                    "cn-northwest-1": "727897471807",
+                    "eu-central-1": "763104351884",
+                    "eu-central-2": "380420809688",
+                    "eu-north-1": "763104351884",
+                    "eu-south-1": "692866216735",
+                    "eu-south-2": "503227376785",
+                    "eu-west-1": "763104351884",
+                    "eu-west-2": "763104351884",
+                    "eu-west-3": "763104351884",
+                    "il-central-1": "780543022126",
+                    "me-central-1": "914824155844",
+                    "me-south-1": "217643126080",
+                    "sa-east-1": "763104351884",
+                    "us-east-1": "763104351884",
+                    "us-east-2": "763104351884",
+                    "us-gov-east-1": "446045086412",
+                    "us-gov-west-1": "442386744353",
+                    "us-west-1": "763104351884",
+                    "us-west-2": "763104351884"
+                },
+                "repository": "pytorch-training"
             }
         }
     }

src/sagemaker/jumpstart/factory/model.py

@@ -555,6 +555,7 @@ def get_deploy_kwargs(
     resources: Optional[ResourceRequirements] = None,
     managed_instance_scaling: Optional[str] = None,
     endpoint_type: Optional[EndpointType] = None,
+    routing_config: Optional[Dict[str, Any]] = None,
 ) -> JumpStartModelDeployKwargs:
     """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
 
@@ -586,6 +587,7 @@ def get_deploy_kwargs(
         accept_eula=accept_eula,
         endpoint_logging=endpoint_logging,
         resources=resources,
+        routing_config=routing_config,
     )
 
     deploy_kwargs = _add_sagemaker_session_to_kwargs(kwargs=deploy_kwargs)

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,7 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from typing import Dict, List, Optional, Union, Any
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -496,6 +496,7 @@ def deploy(
         resources: Optional[ResourceRequirements] = None,
         managed_instance_scaling: Optional[str] = None,
         endpoint_type: EndpointType = EndpointType.MODEL_BASED,
+        routing_config: Optional[Dict[str, Any]] = None,
     ) -> PredictorBase:
         """Creates endpoint by calling base ``Model`` class `deploy` method.
 
@@ -590,6 +591,8 @@ def deploy(
                 endpoint.
             endpoint_type (EndpointType): The type of endpoint used to deploy models.
                 (Default: EndpointType.MODEL_BASED).
+            routing_config (Optional[Dict]): Settings the control how the endpoint routes
+                incoming traffic to the instances that the endpoint hosts.
 
         Raises:
             MarketplaceModelSubscriptionError: If the caller is not subscribed to the model.
@@ -625,6 +628,7 @@ def deploy(
             managed_instance_scaling=managed_instance_scaling,
             endpoint_type=endpoint_type,
             model_type=self.model_type,
+            routing_config=routing_config,
         )
         if (
             self.model_type == JumpStartModelType.PROPRIETARY

src/sagemaker/jumpstart/types.py

@@ -1614,6 +1614,7 @@ class JumpStartModelDeployKwargs(JumpStartKwargs):
         "endpoint_logging",
         "resources",
         "endpoint_type",
+        "routing_config",
     ]
 
     SERIALIZATION_EXCLUSION_SET = {
@@ -1658,6 +1659,7 @@ def __init__(
         endpoint_logging: Optional[bool] = None,
         resources: Optional[ResourceRequirements] = None,
         endpoint_type: Optional[EndpointType] = None,
+        routing_config: Optional[Dict[str, Any]] = None,
     ) -> None:
         """Instantiates JumpStartModelDeployKwargs object."""
 
@@ -1690,6 +1692,7 @@ def __init__(
         self.endpoint_logging = endpoint_logging
         self.resources = resources
         self.endpoint_type = endpoint_type
+        self.routing_config = routing_config
 
 
 class JumpStartEstimatorInitKwargs(JumpStartKwargs):

src/sagemaker/model.py

@@ -20,7 +20,7 @@
 import os
 import re
 import copy
-from typing import List, Dict, Optional, Union
+from typing import List, Dict, Optional, Union, Any
 
 import sagemaker
 from sagemaker import (
@@ -66,6 +66,7 @@
     resolve_nested_dict_value_from_config,
     format_tags,
     Tags,
+    _resolve_routing_config,
 )
 from sagemaker.async_inference import AsyncInferenceConfig
 from sagemaker.predictor_async import AsyncPredictor
@@ -1309,6 +1310,7 @@ def deploy(
         resources: Optional[ResourceRequirements] = None,
         endpoint_type: EndpointType = EndpointType.MODEL_BASED,
         managed_instance_scaling: Optional[str] = None,
+        routing_config: Optional[Dict[str, Any]] = None,
         **kwargs,
     ):
         """Deploy this ``Model`` to an ``Endpoint`` and optionally return a ``Predictor``.
@@ -1406,6 +1408,15 @@ def deploy(
                 Endpoint. (Default: None).
             endpoint_type (Optional[EndpointType]): The type of an endpoint used to deploy models.
                 (Default: EndpointType.MODEL_BASED).
+            routing_config (Optional[Dict[str, Any]): Settings the control how the endpoint routes incoming
+                traffic to the instances that the endpoint hosts.
+                Currently, support dictionary key ``RoutingStrategy``.
+
+                .. code:: python
+
+                    {
+                        "RoutingStrategy":  sagemaker.enums.RoutingStrategy.RANDOM
+                    }
         Raises:
              ValueError: If arguments combination check failed in these circumstances:
                 - If no role is specified or
@@ -1458,6 +1469,8 @@ def deploy(
         if self.role is None:
             raise ValueError("Role can not be null for deploying a model")
 
+        routing_config = _resolve_routing_config(routing_config)
+
         if (
             inference_recommendation_id is not None
             or self.inference_recommender_job_results is not None
@@ -1543,6 +1556,7 @@ def deploy(
                     model_data_download_timeout=model_data_download_timeout,
                     container_startup_health_check_timeout=container_startup_health_check_timeout,
                     managed_instance_scaling=managed_instance_scaling_config,
+                    routing_config=routing_config,
                 )
 
                 self.sagemaker_session.endpoint_from_production_variants(
@@ -1625,6 +1639,7 @@ def deploy(
                 volume_size=volume_size,
                 model_data_download_timeout=model_data_download_timeout,
                 container_startup_health_check_timeout=container_startup_health_check_timeout,
+                routing_config=routing_config,
             )
             if endpoint_name:
                 self.endpoint_name = endpoint_name

src/sagemaker/serve/builder/model_builder.py

@@ -169,7 +169,7 @@ class ModelBuilder(Triton, DJL, JumpStart, TGI, Transformers, TensorflowServing,
             in order for model builder to build the artifacts correctly (according
             to the model server). Possible values for this argument are
             ``TORCHSERVE``, ``MMS``, ``TENSORFLOW_SERVING``, ``DJL_SERVING``,
-            ``TRITON``,``TGI``, and ``TEI``.
+            ``TRITON``, ``TGI``, and ``TEI``.
         model_metadata (Optional[Dict[str, Any]): Dictionary used to override model metadata.
             Currently, ``HF_TASK`` is overridable for HuggingFace model. HF_TASK should be set for
             new models without task metadata in the Hub, adding unsupported task types will throw

src/sagemaker/utils.py

@@ -44,6 +44,7 @@
     _log_sagemaker_config_single_substitution,
     _log_sagemaker_config_merge,
 )
+from sagemaker.enums import RoutingStrategy
 from sagemaker.session_settings import SessionSettings
 from sagemaker.workflow import is_pipeline_variable, is_pipeline_parameter_string
 from sagemaker.workflow.entities import PipelineVariable
@@ -1655,3 +1656,33 @@ def deep_override_dict(
     )
     flattened_dict1.update(flattened_dict2)
     return unflatten_dict(flattened_dict1) if flattened_dict1 else {}
+
+
+def _resolve_routing_config(routing_config: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]:
+    """Resolve Routing Config
+
+    Args:
+        routing_config (Optional[Dict[str, Any]]): The routing config.
+
+    Returns:
+        Optional[Dict[str, Any]]: The resolved routing config.
+
+    Raises:
+        ValueError: If the RoutingStrategy is invalid.
+    """
+
+    if routing_config:
+        routing_strategy = routing_config.get("RoutingStrategy", None)
+        if routing_strategy:
+            if isinstance(routing_strategy, RoutingStrategy):
+                return {"RoutingStrategy": routing_strategy.name}
+            if isinstance(routing_strategy, str) and (
+                routing_strategy.upper() == RoutingStrategy.RANDOM.name
+                or routing_strategy.upper() == RoutingStrategy.LEAST_OUTSTANDING_REQUESTS.name
+            ):
+                return {"RoutingStrategy": routing_strategy.upper()}
+            raise ValueError(
+                "RoutingStrategy must be either RoutingStrategy.RANDOM "
+                "or RoutingStrategy.LEAST_OUTSTANDING_REQUESTS"
+            )
+    return None

tests/unit/sagemaker/model/test_deploy.py

@@ -125,6 +125,7 @@ def test_deploy(name_from_base, prepare_container_def, production_variant, sagem
         volume_size=None,
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
+        routing_config=None,
     )
 
     sagemaker_session.create_model.assert_called_with(
@@ -184,6 +185,7 @@ def test_deploy_accelerator_type(
         volume_size=None,
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
+        routing_config=None,
     )
 
     sagemaker_session.endpoint_from_production_variants.assert_called_with(
@@ -506,6 +508,7 @@ def test_deploy_serverless_inference(production_variant, create_sagemaker_model,
         volume_size=None,
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
+        routing_config=None,
     )
 
     sagemaker_session.endpoint_from_production_variants.assert_called_with(
@@ -938,6 +941,7 @@ def test_deploy_customized_volume_size_and_timeout(
         volume_size=volume_size_gb,
         model_data_download_timeout=model_data_download_timeout_sec,
         container_startup_health_check_timeout=startup_health_check_timeout_sec,
+        routing_config=None,
     )
 
     sagemaker_session.create_model.assert_called_with(
@@ -987,6 +991,7 @@ def test_deploy_with_resources(sagemaker_session, name_from_base, production_var
         volume_size=None,
         model_data_download_timeout=None,
         container_startup_health_check_timeout=None,
+        routing_config=None,
     )
     sagemaker_session.endpoint_from_production_variants.assert_called_with(
         name=name_from_base(MODEL_NAME),

tests/unit/test_utils.py

@@ -30,6 +30,7 @@
 from mock import call, patch, Mock, MagicMock, PropertyMock
 
 import sagemaker
+from sagemaker.enums import RoutingStrategy
 from sagemaker.experiments._run_context import _RunContext
 from sagemaker.session_settings import SessionSettings
 from sagemaker.utils import (
@@ -50,6 +51,7 @@
     _is_bad_link,
     custom_extractall_tarfile,
     can_model_package_source_uri_autopopulate,
+    _resolve_routing_config,
 )
 from tests.unit.sagemaker.workflow.helpers import CustomStep
 from sagemaker.workflow.parameters import ParameterString, ParameterInteger
@@ -1866,3 +1868,30 @@ def test_deep_override_skip_keys(self):
         expected_result = {"a": 1, "b": {"x": 20, "y": 3, "z": 30}, "c": [4, 5]}
 
         self.assertEqual(deep_override_dict(dict1, dict2, skip_keys=["c", "d"]), expected_result)
+
+
+@pytest.mark.parametrize(
+    "routing_config, expected",
+    [
+        ({"RoutingStrategy": RoutingStrategy.RANDOM}, {"RoutingStrategy": "RANDOM"}),
+        ({"RoutingStrategy": "RANDOM"}, {"RoutingStrategy": "RANDOM"}),
+        (
+            {"RoutingStrategy": RoutingStrategy.LEAST_OUTSTANDING_REQUESTS},
+            {"RoutingStrategy": "LEAST_OUTSTANDING_REQUESTS"},
+        ),
+        (
+            {"RoutingStrategy": "LEAST_OUTSTANDING_REQUESTS"},
+            {"RoutingStrategy": "LEAST_OUTSTANDING_REQUESTS"},
+        ),
+        ({"RoutingStrategy": None}, None),
+        (None, None),
+    ],
+)
+def test_resolve_routing_config(routing_config, expected):
+    res = _resolve_routing_config(routing_config)
+
+    assert res == expected
+
+
+def test_resolve_routing_config_ex():
+    pytest.raises(ValueError, lambda: _resolve_routing_config({"RoutingStrategy": "Invalid"}))