Add ReadOnly APIs

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none

src/sagemaker/jumpstart/model.py

@@ -14,7 +14,9 @@
 
 from __future__ import absolute_import
 
-from typing import Dict, List, Optional, Union
+from functools import lru_cache
+from typing import Dict, List, Optional, Union, Any
+import pandas as pd
 from botocore.exceptions import ClientError
 
 from sagemaker import payloads
@@ -36,14 +38,21 @@
  get_init_kwargs,
  get_register_kwargs,
 )
-from sagemaker.jumpstart.types import JumpStartSerializablePayload
+from sagemaker.jumpstart.types import (
+ JumpStartSerializablePayload,
+ DeploymentConfigMetadata,
+ JumpStartBenchmarkStat,
+ JumpStartMetadataConfig,
+)
 from sagemaker.jumpstart.utils import (
  validate_model_id_and_get_type,
  verify_model_region_and_return_specs,
+ get_jumpstart_configs,
+ extract_metrics_from_deployment_configs,
 )
 from sagemaker.jumpstart.constants import JUMPSTART_LOGGER
 from sagemaker.jumpstart.enums import JumpStartModelType
-from sagemaker.utils import stringify_object, format_tags, Tags
+from sagemaker.utils import stringify_object, format_tags, Tags, get_instance_rate_per_hour
 from sagemaker.model import (
  Model,
  ModelPackage,
@@ -352,6 +361,18 @@ def _validate_model_id_and_type():
  self.model_package_arn = model_init_kwargs.model_package_arn
  self.init_kwargs = model_init_kwargs.to_kwargs_dict(False)
 
+ metadata_configs = get_jumpstart_configs(
+ region=self.region,
+ model_id=self.model_id,
+ model_version=self.model_version,
+ sagemaker_session=self.sagemaker_session,
+ model_type=self.model_type,
+ )
+ self._deployment_configs = [
+ self._convert_to_deployment_config_metadata(config_name, config)
+ for config_name, config in metadata_configs.items()
+ ]
+
  def log_subscription_warning(self) -> None:
  """Log message prompting the customer to subscribe to the proprietary model."""
  subscription_link = verify_model_region_and_return_specs(
@@ -420,6 +441,27 @@ def set_deployment_config(self, config_name: Optional[str]) -> None:
  model_id=self.model_id, model_version=self.model_version, config_name=config_name
  )
 
+ @property
+ def benchmark_metrics(self) -> pd.DataFrame:
+ """Benchmark Metrics for deployment configs
+
+ Returns:
+ Metrics: Pandas DataFrame object.
+ """
+ return pd.DataFrame(self._get_benchmark_data(self.config_name))
+
+ def display_benchmark_metrics(self) -> None:
+ """Display Benchmark Metrics for deployment configs."""
+ print(self.benchmark_metrics.to_markdown())
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self._deployment_configs
+
  def _create_sagemaker_model(
  self,
  instance_type=None,
@@ -808,6 +850,67 @@ def register_deploy_wrapper(*args, **kwargs):
 
  return model_package
 
+ @lru_cache
+ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
+ """Constructs deployment configs benchmark data.
+
+ Args:
+ config_name (str): The name of the selected deployment config.
+ Returns:
+ Dict[str, List[str]]: Deployment config benchmark data.
+ """
+ return extract_metrics_from_deployment_configs(
+ self._deployment_configs,
+ config_name,
+ )
+
+ def _convert_to_deployment_config_metadata(
+ self, config_name: str, metadata_config: JumpStartMetadataConfig
+ ) -> Dict[str, Any]:
+ """Retrieve deployment config for config name.
+
+ Args:
+ config_name (str): Name of deployment config.
+ metadata_config (JumpStartMetadataConfig): Metadata config for deployment config.
+ Returns:
+ A deployment metadata config for config name (dict[str, Any]).
+ """
+ default_inference_instance_type = metadata_config.resolved_config.get(
+ "default_inference_instance_type"
+ )
+
+ instance_rate = get_instance_rate_per_hour(
+ instance_type=default_inference_instance_type, region=self.region
+ )
+
+ benchmark_metrics = (
+ metadata_config.benchmark_metrics.get(default_inference_instance_type)
+ if metadata_config.benchmark_metrics is not None
+ else None
+ )
+ if instance_rate is not None:
+ if benchmark_metrics is not None:
+ benchmark_metrics.append(JumpStartBenchmarkStat(instance_rate))
+ else:
+ benchmark_metrics = [JumpStartBenchmarkStat(instance_rate)]
+
+ init_kwargs = get_init_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+ deploy_kwargs = get_deploy_kwargs(
+ model_id=self.model_id,
+ instance_type=default_inference_instance_type,
+ sagemaker_session=self.sagemaker_session,
+ )
+
+ deployment_config_metadata = DeploymentConfigMetadata(
+ config_name, benchmark_metrics, init_kwargs, deploy_kwargs
+ )
+
+ return deployment_config_metadata.to_json()
+
  def __str__(self) -> str:
  """Overriding str(*) method to make more human-readable."""
  return stringify_object(self)

src/sagemaker/jumpstart/types.py

@@ -2206,3 +2206,99 @@ def __init__(
  self.skip_model_validation = skip_model_validation
  self.source_uri = source_uri
  self.config_name = config_name
+
+
+class BaseDeploymentConfigDataHolder(JumpStartDataHolderType):
+ """Base class for Deployment Config Data."""
+
+ def _convert_to_pascal_case(self, attr_name: str) -> str:
+ """Converts a snake_case attribute name into a camelCased string.
+
+ Args:
+ attr_name (str): The snake_case attribute name.
+ Returns:
+ str: The PascalCased attribute name.
+ """
+ return attr_name.replace("_", " ").title().replace(" ", "")
+
+ def to_json(self) -> Dict[str, Any]:
+ """Represents ``This`` object as JSON."""
+ json_obj = {}
+ for att in self.__slots__:
+ if hasattr(self, att):
+ cur_val = getattr(self, att)
+ att = self._convert_to_pascal_case(att)
+ if issubclass(type(cur_val), JumpStartDataHolderType):
+ json_obj[att] = cur_val.to_json()
+ elif isinstance(cur_val, list):
+ json_obj[att] = []
+ for obj in cur_val:
+ if issubclass(type(obj), JumpStartDataHolderType):
+ json_obj[att].append(obj.to_json())
+ else:
+ json_obj[att].append(obj)
+ elif isinstance(cur_val, dict):
+ json_obj[att] = {}
+ for key, val in cur_val.items():
+ if issubclass(type(val), JumpStartDataHolderType):
+ json_obj[att][self._convert_to_pascal_case(key)] = val.to_json()
+ else:
+ json_obj[att][key] = val
+ else:
+ json_obj[att] = cur_val
+ return json_obj
+
+
+class DeploymentConfig(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config."""
+
+ __slots__ = [
+ "model_data_download_timeout",
+ "container_startup_health_check_timeout",
+ "image_uri",
+ "model_data",
+ "instance_type",
+ "environment",
+ "compute_resource_requirements",
+ ]
+
+ def __init__(
+ self, init_kwargs: JumpStartModelInitKwargs, deploy_kwargs: JumpStartModelDeployKwargs
+ ):
+ """Instantiates DeploymentConfig object."""
+ if init_kwargs is not None:
+ self.image_uri = init_kwargs.image_uri
+ self.model_data = init_kwargs.model_data
+ self.instance_type = init_kwargs.instance_type
+ self.environment = init_kwargs.env
+ if init_kwargs.resources is not None:
+ self.compute_resource_requirements = (
+ init_kwargs.resources.get_compute_resource_requirements()
+ )
+ if deploy_kwargs is not None:
+ self.model_data_download_timeout = deploy_kwargs.model_data_download_timeout
+ self.container_startup_health_check_timeout = (
+ deploy_kwargs.container_startup_health_check_timeout
+ )
+
+
+class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
+ """Dataclass representing a Deployment Config Metadata"""
+
+ __slots__ = [
+ "config_name",
+ "benchmark_metrics",
+ "deployment_config",
+ ]
+
+ def __init__(
+ self,
+ config_name: str,
+ benchmark_metrics: List[JumpStartBenchmarkStat],
+ init_kwargs: JumpStartModelInitKwargs,
+ deploy_kwargs: JumpStartModelDeployKwargs,
+ ):
+ """Instantiates DeploymentConfigMetadata object."""
+ self.config_name = config_name
+ self.benchmark_metrics = benchmark_metrics
+ self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -999,3 +999,44 @@ def get_jumpstart_configs(
  if metadata_configs
  else {}
  )
+
+
+def extract_metrics_from_deployment_configs(
+ deployment_configs: List[Dict[str, Any]], config_name: str
+) -> Dict[str, List[str]]:
+ """Extracts metrics from deployment configs.
+
+ Args:
+ deployment_configs (list[dict[str, Any]]): List of deployment configs.
+ config_name (str): The name of the deployment config use by the model.
+ """
+
+ data = {"Config Name": [], "Instance Type": [], "Selected": []}
+
+ for index, deployment_config in enumerate(deployment_configs):
+ if deployment_config.get("DeploymentConfig") is None:
+ continue
+
+ benchmark_metrics = deployment_config.get("BenchmarkMetrics")
+ if benchmark_metrics is not None:
+ data["Config Name"].append(deployment_config.get("ConfigName"))
+ data["Instance Type"].append(
+ deployment_config.get("DeploymentConfig").get("InstanceType")
+ )
+ data["Selected"].append(
+ "Yes"
+ if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+ else "No"
+ )
+
+ if index == 0:
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ data[column_name] = []
+
+ for benchmark_metric in benchmark_metrics:
+ column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
+ if column_name in data.keys():
+ data[column_name].append(benchmark_metric.get("value"))
+
+ return data

src/sagemaker/serve/builder/jumpstart_builder.py

@@ -16,7 +16,7 @@
 import copy
 from abc import ABC, abstractmethod
 from datetime import datetime, timedelta
-from typing import Type
+from typing import Type, Any, List, Dict
 import logging
 
 from sagemaker.model import Model
@@ -431,6 +431,18 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
  sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
  )
 
+ def display_benchmark_metrics(self):
+ """Display Markdown Benchmark Metrics for deployment configs."""
+ self.pysdk_model.display_benchmark_metrics()
+
+ def list_deployment_configs(self) -> List[Dict[str, Any]]:
+ """List deployment configs for ``This`` model in the current region.
+
+ Returns:
+ List[Dict[str, Any]]: A list of deployment configs.
+ """
+ return self.pysdk_model.list_deployment_configs()
+
  def _build_for_jumpstart(self):
  """Placeholder docstring"""
  # we do not pickle for jumpstart. set to none