feat: optimization technique related validations.

src/sagemaker/jumpstart/factory/model.py

@@ -16,6 +16,7 @@
 
 
 from typing import Any, Dict, List, Optional, Union
+from sagemaker_core.shapes import ModelAccessConfig
 from sagemaker import environment_variables, image_uris, instance_types, model_uris, script_uris
 from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
 from sagemaker.base_deserializers import BaseDeserializer
@@ -53,11 +54,11 @@
     add_hub_content_arn_tags,
     add_jumpstart_model_info_tags,
     get_default_jumpstart_session_with_user_agent_suffix,
-    get_neo_content_bucket,
     get_top_ranked_config_name,
     update_dict_if_key_not_present,
     resolve_model_sagemaker_config_field,
     verify_model_region_and_return_specs,
+    get_draft_model_content_bucket,
 )
 
 from sagemaker.jumpstart.factory.utils import (
@@ -70,7 +71,12 @@
 
 from sagemaker.serverless.serverless_inference_config import ServerlessInferenceConfig
 from sagemaker.session import Session
-from sagemaker.utils import camel_case_to_pascal_case, name_from_base, format_tags, Tags
+from sagemaker.utils import (
+    camel_case_to_pascal_case,
+    name_from_base,
+    format_tags,
+    Tags,
+)
 from sagemaker.workflow.entities import PipelineVariable
 from sagemaker.compute_resource_requirements.resource_requirements import ResourceRequirements
 from sagemaker import resource_requirements
@@ -565,7 +571,9 @@ def _add_additional_model_data_sources_to_kwargs(
     # Append speculative decoding data source from metadata
     speculative_decoding_data_sources = specs.get_speculative_decoding_s3_data_sources()
     for data_source in speculative_decoding_data_sources:
-        data_source.s3_data_source.set_bucket(get_neo_content_bucket(region=kwargs.region))
+        data_source.s3_data_source.set_bucket(
+            get_draft_model_content_bucket(provider=data_source.provider, region=kwargs.region)
+        )
     api_shape_additional_model_data_sources = (
         [
             camel_case_to_pascal_case(data_source.to_json())
@@ -648,6 +656,7 @@ def get_deploy_kwargs(
     training_config_name: Optional[str] = None,
     config_name: Optional[str] = None,
     routing_config: Optional[Dict[str, Any]] = None,
+    model_access_configs: Optional[Dict[str, ModelAccessConfig]] = None,
 ) -> JumpStartModelDeployKwargs:
     """Returns kwargs required to call `deploy` on `sagemaker.estimator.Model` object."""
 
@@ -684,6 +693,7 @@ def get_deploy_kwargs(
         resources=resources,
         config_name=config_name,
         routing_config=routing_config,
+        model_access_configs=model_access_configs,
     )
     deploy_kwargs, orig_session = _set_temp_sagemaker_session_if_not_set(kwargs=deploy_kwargs)
     deploy_kwargs.specs = verify_model_region_and_return_specs(

src/sagemaker/jumpstart/model.py

@@ -18,6 +18,7 @@
 import pandas as pd
 from botocore.exceptions import ClientError
 
+from sagemaker_core.shapes import ModelAccessConfig
 from sagemaker import payloads
 from sagemaker.async_inference.async_inference_config import AsyncInferenceConfig
 from sagemaker.base_deserializers import BaseDeserializer
@@ -51,6 +52,7 @@
     add_instance_rate_stats_to_benchmark_metrics,
     deployment_config_response_data,
     _deployment_config_lru_cache,
+    _add_model_access_configs_to_model_data_sources,
 )
 from sagemaker.jumpstart.constants import DEFAULT_JUMPSTART_SAGEMAKER_SESSION, JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
@@ -540,12 +542,16 @@ def attach(
         inferred_model_id = inferred_model_version = inferred_inference_component_name = None
 
         if inference_component_name is None or model_id is None or model_version is None:
-            inferred_model_id, inferred_model_version, inferred_inference_component_name, _, _ = (
-                get_model_info_from_endpoint(
-                    endpoint_name=endpoint_name,
-                    inference_component_name=inference_component_name,
-                    sagemaker_session=sagemaker_session,
-                )
+            (
+                inferred_model_id,
+                inferred_model_version,
+                inferred_inference_component_name,
+                _,
+                _,
+            ) = get_model_info_from_endpoint(
+                endpoint_name=endpoint_name,
+                inference_component_name=inference_component_name,
+                sagemaker_session=sagemaker_session,
             )
 
         model_id = model_id or inferred_model_id
@@ -659,6 +665,7 @@ def deploy(
         managed_instance_scaling: Optional[str] = None,
         endpoint_type: EndpointType = EndpointType.MODEL_BASED,
         routing_config: Optional[Dict[str, Any]] = None,
+        model_access_configs: Optional[List[ModelAccessConfig]] = None,
     ) -> PredictorBase:
         """Creates endpoint by calling base ``Model`` class `deploy` method.
 
@@ -755,6 +762,11 @@ def deploy(
                 (Default: EndpointType.MODEL_BASED).
             routing_config (Optional[Dict]): Settings the control how the endpoint routes
                 incoming traffic to the instances that the endpoint hosts.
+            model_access_configs (Optional[Dict[str, ModelAccessConfig]]): For models that require
+                ModelAccessConfig, provide a `{ "model_id", ModelAccessConfig(accept_eula=True) }`
+                to indicate whether model terms of use have been accepted. The `accept_eula` value
+                must be explicitly defined as `True` in order to accept the end-user license
+                agreement (EULA) that some. (Default: None)
 
         Raises:
             MarketplaceModelSubscriptionError: If the caller is not subscribed to the model.
@@ -795,6 +807,7 @@ def deploy(
             model_type=self.model_type,
             config_name=self.config_name,
             routing_config=routing_config,
+            model_access_configs=model_access_configs,
         )
         if (
             self.model_type == JumpStartModelType.PROPRIETARY
@@ -804,6 +817,13 @@ def deploy(
                 f"{EndpointType.INFERENCE_COMPONENT_BASED} is not supported for Proprietary models."
             )
 
+        self.additional_model_data_sources = _add_model_access_configs_to_model_data_sources(
+            self.additional_model_data_sources,
+            deploy_kwargs.model_access_configs,
+            deploy_kwargs.model_id,
+            deploy_kwargs.region,
+        )
+
         try:
             predictor = super(JumpStartModel, self).deploy(**deploy_kwargs.to_kwargs_dict())
         except ClientError as e:
@@ -1016,10 +1036,11 @@ def _get_deployment_configs(
                 )
 
             if metadata_config.benchmark_metrics:
-                err, metadata_config.benchmark_metrics = (
-                    add_instance_rate_stats_to_benchmark_metrics(
-                        self.region, metadata_config.benchmark_metrics
-                    )
+                (
+                    err,
+                    metadata_config.benchmark_metrics,
+                ) = add_instance_rate_stats_to_benchmark_metrics(
+                    self.region, metadata_config.benchmark_metrics
                 )
 
             config_components = metadata_config.config_components.get(config_name)

src/sagemaker/jumpstart/types.py

@@ -17,6 +17,7 @@
 from copy import deepcopy
 from enum import Enum
 from typing import Any, Dict, List, Optional, Set, Union
+from sagemaker_core.shapes import ModelAccessConfig as CoreModelAccessConfig
 from sagemaker.model_card.model_card import ModelCard, ModelPackageModelCard
 from sagemaker.utils import (
     S3_PREFIX,
@@ -1081,9 +1082,9 @@ def set_bucket(self, bucket: str) -> None:
 class AdditionalModelDataSource(JumpStartDataHolderType):
     """Data class of additional model data source mirrors CreateModel API."""
 
-    SERIALIZATION_EXCLUSION_SET: Set[str] = set()
+    SERIALIZATION_EXCLUSION_SET = {"provider"}
 
-    __slots__ = ["channel_name", "s3_data_source"]
+    __slots__ = ["channel_name", "s3_data_source", "hosting_eula_key"]
 
     def __init__(self, spec: Dict[str, Any]):
         """Initializes a AdditionalModelDataSource object.
@@ -1101,6 +1102,8 @@ def from_json(self, json_obj: Dict[str, Any]) -> None:
         """
         self.channel_name: str = json_obj["channel_name"]
         self.s3_data_source: S3DataSource = S3DataSource(json_obj["s3_data_source"])
+        self.hosting_eula_key: str = json_obj.get("hosting_eula_key")
+        self.provider: Dict = json_obj.get("provider", {})
 
     def to_json(self, exclude_keys=True) -> Dict[str, Any]:
         """Returns json representation of AdditionalModelDataSource object."""
@@ -1119,7 +1122,9 @@ def to_json(self, exclude_keys=True) -> Dict[str, Any]:
 class JumpStartModelDataSource(AdditionalModelDataSource):
     """Data class JumpStart additional model data source."""
 
-    SERIALIZATION_EXCLUSION_SET = {"artifact_version"}
+    SERIALIZATION_EXCLUSION_SET = {
+        "artifact_version"
+    } | AdditionalModelDataSource.SERIALIZATION_EXCLUSION_SET
 
     __slots__ = list(SERIALIZATION_EXCLUSION_SET) + AdditionalModelDataSource.__slots__
 
@@ -2239,6 +2244,7 @@ class JumpStartModelDeployKwargs(JumpStartKwargs):
         "config_name",
         "routing_config",
         "specs",
+        "model_access_configs",
     ]
 
     SERIALIZATION_EXCLUSION_SET = {
@@ -2252,6 +2258,7 @@ class JumpStartModelDeployKwargs(JumpStartKwargs):
         "sagemaker_session",
         "training_instance_type",
         "config_name",
+        "model_access_configs",
     }
 
     def __init__(
@@ -2290,6 +2297,7 @@ def __init__(
         endpoint_type: Optional[EndpointType] = None,
         config_name: Optional[str] = None,
         routing_config: Optional[Dict[str, Any]] = None,
+        model_access_configs: Optional[Dict[str, CoreModelAccessConfig]] = None,
     ) -> None:
         """Instantiates JumpStartModelDeployKwargs object."""
 
@@ -2327,6 +2335,7 @@ def __init__(
         self.endpoint_type = endpoint_type
         self.config_name = config_name
         self.routing_config = routing_config
+        self.model_access_configs = model_access_configs
 
 
 class JumpStartEstimatorInitKwargs(JumpStartKwargs):

src/sagemaker/jumpstart/utils.py

@@ -12,6 +12,7 @@
 # language governing permissions and limitations under the License.
 """This module contains utilities related to SageMaker JumpStart."""
 from __future__ import absolute_import
+
 from copy import copy
 import logging
 import os
@@ -22,6 +23,7 @@
 from botocore.exceptions import ClientError
 from packaging.version import Version
 import botocore
+from sagemaker_core.shapes import ModelAccessConfig
 import sagemaker
 from sagemaker.config.config_schema import (
     MODEL_ENABLE_NETWORK_ISOLATION_PATH,
@@ -55,6 +57,7 @@
     TagsDict,
     get_instance_rate_per_hour,
     get_domain_for_region,
+    camel_case_to_pascal_case,
 )
 from sagemaker.workflow import is_pipeline_variable
 from sagemaker.user_agent import get_user_agent_extra_suffix
@@ -555,11 +558,18 @@ def get_eula_message(model_specs: JumpStartModelSpecs, region: str) -> str:
     """Returns EULA message to display if one is available, else empty string."""
     if model_specs.hosting_eula_key is None:
         return ""
+    return format_eula_message_template(
+        model_id=model_specs.model_id, region=region, hosting_eula_key=model_specs.hosting_eula_key
+    )
+
+
+def format_eula_message_template(model_id: str, region: str, hosting_eula_key: str):
+    """Returns a formatted EULA message."""
     return (
-        f"Model '{model_specs.model_id}' requires accepting end-user license agreement (EULA). "
+        f"Model '{model_id}' requires accepting end-user license agreement (EULA). "
         f"See https://{get_jumpstart_content_bucket(region=region)}.s3.{region}."
         f"{get_domain_for_region(region)}"
-        f"/{model_specs.hosting_eula_key} for terms of use."
+        f"/{hosting_eula_key} for terms of use."
     )
 
 
@@ -1525,3 +1535,63 @@ def wrapped_f(*args, **kwargs):
     if _func is None:
         return wrapper_cache
     return wrapper_cache(_func)
+
+
+def _add_model_access_configs_to_model_data_sources(
+    model_data_sources: List[Dict[str, any]],
+    model_access_configs: Dict[str, ModelAccessConfig],
+    model_id: str,
+    region: str,
+):
+    """Sets AcceptEula to True for gated speculative decoding models"""
+
+    if not model_data_sources:
+        return model_data_sources
+
+    acked_model_data_sources = []
+    for model_data_source in model_data_sources:
+        hosting_eula_key = model_data_source.get("HostingEulaKey")
+        if hosting_eula_key:
+            if not model_access_configs or not model_access_configs.get(model_id):
+                eula_message_template = (
+                    "{model_source}{base_eula_message}{model_access_configs_message}"
+                )
+                model_access_config_entry = (
+                    '"{model_id}":ModelAccessConfig(accept_eula=True)'.format(model_id=model_id)
+                )
+                raise ValueError(
+                    eula_message_template.format(
+                        model_source="Additional " if model_data_source.get("ChannelName") else "",
+                        base_eula_message=format_eula_message_template(
+                            model_id=model_id, region=region, hosting_eula_key=hosting_eula_key
+                        ),
+                        model_access_configs_message=(
+                            " Please add a ModelAccessConfig entry:"
+                            f" {model_access_config_entry} "
+                            "to model_access_configs to acknowledge the EULA."
+                        ),
+                    )
+                )
+            acked_model_data_source = model_data_source.copy()
+            acked_model_data_source.pop("HostingEulaKey")
+            acked_model_data_source["S3DataSource"]["ModelAccessConfig"] = (
+                camel_case_to_pascal_case(model_access_configs.get(model_id).model_dump())
+            )
+            acked_model_data_sources.append(acked_model_data_source)
+        else:
+            acked_model_data_sources.append(model_data_source)
+    return acked_model_data_sources
+
+
+def get_draft_model_content_bucket(provider: Dict, region: str) -> str:
+    """Returns the correct content bucket for a 1p draft model."""
+    neo_bucket = get_neo_content_bucket(region=region)
+    if not provider:
+        return neo_bucket
+    provider_name = provider.get("name", "")
+    if provider_name == "JumpStart":
+        classification = provider.get("classification", "ungated")
+        if classification == "gated":
+            return get_jumpstart_gated_content_bucket(region=region)
+        return get_jumpstart_content_bucket(region=region)
+    return neo_bucket