Add ReadOnly APIs (aws#4606)

* Add ReadOnly APIs

* Resolving PR review comments

* Resolve PR review comments

* Refactoring

* Refactoring

* Add Caching

* Refactore

* Resolving conflicts

* Add Unit Tests

* Fix Unit Tests

* Fix unit tests

* Fix UT

* Refactoring

* Fix Integ tests

* refactoring after Notebook testing

* Fix code styles

---------

Co-authored-by: Jonathan Makunga <[email protected]>

Author: 2 people

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

@@ -2208,3 +2208,99 @@ 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