ModelBuilder: Add functionalities to get and set deployment config. (a…

…ws#4614)

* Add funtionalities to get and set deployment config

* Resolve PR comments

* ModelBuilder-JS

* Add Unit tests

* Refactoring

* Testing with Notebook

* Test backward compatibility

* Remove Accelerated column if all not enabled

* Fix docstring

* Resolved PR Review comments

* Docstring

* increase code coverage

---------

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

src/sagemaker/jumpstart/model.py

@@ -15,7 +15,7 @@
 from __future__ import absolute_import
 
 from functools import lru_cache
-from typing import Dict, List, Optional, Union, Any
+from typing import Dict, List, Optional, Any, Union
 import pandas as pd
 from botocore.exceptions import ClientError
 
@@ -441,14 +441,23 @@ 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 deployment_config(self) -> Optional[Dict[str, Any]]:
+        """The deployment config that will be applied to the model.
+
+        Returns:
+            Optional[Dict[str, Any]]: Deployment config that will be applied to the model.
+        """
+        return self._retrieve_selected_deployment_config(self.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))
+        return pd.DataFrame(self._get_benchmarks_data(self.config_name))
 
     def display_benchmark_metrics(self) -> None:
         """Display Benchmark Metrics for deployment configs."""
@@ -851,8 +860,8 @@ 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.
+    def _get_benchmarks_data(self, config_name: str) -> Dict[str, List[str]]:
+        """Deployment configs benchmark metrics.
 
         Args:
             config_name (str): The name of the selected deployment config.
@@ -864,6 +873,23 @@ def _get_benchmark_data(self, config_name: str) -> Dict[str, List[str]]:
             config_name,
         )
 
+    @lru_cache
+    def _retrieve_selected_deployment_config(self, config_name: str) -> Optional[Dict[str, Any]]:
+        """Retrieve the deployment config to apply to the model.
+
+        Args:
+            config_name (str): The name of the deployment config to retrieve.
+        Returns:
+            Optional[Dict[str, Any]]: The retrieved deployment config.
+        """
+        if config_name is None:
+            return None
+
+        for deployment_config in self._deployment_configs:
+            if deployment_config.get("DeploymentConfigName") == config_name:
+                return deployment_config
+        return None
+
     def _convert_to_deployment_config_metadata(
         self, config_name: str, metadata_config: JumpStartMetadataConfig
     ) -> Dict[str, Any]:

src/sagemaker/jumpstart/types.py

@@ -2254,17 +2254,17 @@ def to_json(self) -> Dict[str, Any]:
         return json_obj
 
 
-class DeploymentConfig(BaseDeploymentConfigDataHolder):
+class DeploymentArgs(BaseDeploymentConfigDataHolder):
     """Dataclass representing a Deployment Config."""
 
     __slots__ = [
-        "model_data_download_timeout",
-        "container_startup_health_check_timeout",
         "image_uri",
         "model_data",
-        "instance_type",
         "environment",
+        "instance_type",
         "compute_resource_requirements",
+        "model_data_download_timeout",
+        "container_startup_health_check_timeout",
     ]
 
     def __init__(
@@ -2291,9 +2291,10 @@ class DeploymentConfigMetadata(BaseDeploymentConfigDataHolder):
     """Dataclass representing a Deployment Config Metadata"""
 
     __slots__ = [
-        "config_name",
+        "deployment_config_name",
+        "deployment_args",
+        "acceleration_configs",
         "benchmark_metrics",
-        "deployment_config",
     ]
 
     def __init__(
@@ -2304,6 +2305,7 @@ def __init__(
         deploy_kwargs: JumpStartModelDeployKwargs,
     ):
         """Instantiates DeploymentConfigMetadata object."""
-        self.config_name = config_name
+        self.deployment_config_name = config_name
+        self.deployment_args = DeploymentArgs(init_kwargs, deploy_kwargs)
+        self.acceleration_configs = None
         self.benchmark_metrics = benchmark_metrics
-        self.deployment_config = DeploymentConfig(init_kwargs, deploy_kwargs)

src/sagemaker/jumpstart/utils.py

@@ -1040,24 +1040,40 @@ def extract_metrics_from_deployment_configs(
         config_name (str): The name of the deployment config use by the model.
     """
 
-    data = {"Config Name": [], "Instance Type": [], "Selected": []}
+    data = {"Config Name": [], "Instance Type": [], "Selected": [], "Accelerated": []}
 
     for index, deployment_config in enumerate(deployment_configs):
-        if deployment_config.get("DeploymentConfig") is None:
+        if deployment_config.get("DeploymentArgs") is None:
             continue
 
         benchmark_metrics = deployment_config.get("BenchmarkMetrics")
         if benchmark_metrics is not None:
-            data["Config Name"].append(deployment_config.get("ConfigName"))
+            data["Config Name"].append(deployment_config.get("DeploymentConfigName"))
             data["Instance Type"].append(
-                deployment_config.get("DeploymentConfig").get("InstanceType")
+                deployment_config.get("DeploymentArgs").get("InstanceType")
             )
             data["Selected"].append(
                 "Yes"
-                if (config_name is not None and config_name == deployment_config.get("ConfigName"))
+                if (
+                    config_name is not None
+                    and config_name == deployment_config.get("DeploymentConfigName")
+                )
                 else "No"
             )
 
+            accelerated_configs = deployment_config.get("AccelerationConfigs")
+            if accelerated_configs is None:
+                data["Accelerated"].append("No")
+            else:
+                data["Accelerated"].append(
+                    "Yes"
+                    if (
+                        len(accelerated_configs) > 0
+                        and accelerated_configs[0].get("Enabled", False)
+                    )
+                    else "No"
+                )
+
             if index == 0:
                 for benchmark_metric in benchmark_metrics:
                     column_name = f"{benchmark_metric.get('name')} ({benchmark_metric.get('unit')})"
@@ -1068,4 +1084,6 @@ def extract_metrics_from_deployment_configs(
                 if column_name in data.keys():
                     data[column_name].append(benchmark_metric.get("value"))
 
+    if "Yes" not in data["Accelerated"]:
+        del data["Accelerated"]
     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, Any, List, Dict
+from typing import Type, Any, List, Dict, Optional
 import logging
 
 from sagemaker.model import Model
@@ -431,8 +431,35 @@ def tune_for_tgi_jumpstart(self, max_tuning_duration: int = 1800):
             sharded_supported=sharded_supported, max_tuning_duration=max_tuning_duration
         )
 
+    def set_deployment_config(self, config_name: Optional[str]) -> None:
+        """Sets the deployment config to apply to the model.
+
+        Args:
+            config_name (Optional[str]):
+                The name of the deployment config. Set to None to unset
+                any existing config that is applied to the model.
+        """
+        if not hasattr(self, "pysdk_model") or self.pysdk_model is None:
+            raise Exception("Cannot set deployment config to an uninitialized model.")
+
+        self.pysdk_model.set_deployment_config(config_name)
+
+    def get_deployment_config(self) -> Optional[Dict[str, Any]]:
+        """Gets the deployment config to apply to the model.
+
+        Returns:
+            Optional[Dict[str, Any]]: Deployment config to apply to this model.
+        """
+        if not hasattr(self, "pysdk_model") or self.pysdk_model is None:
+            self.pysdk_model = self._create_pre_trained_js_model()
+
+        return self.pysdk_model.deployment_config
+
     def display_benchmark_metrics(self):
         """Display Markdown Benchmark Metrics for deployment configs."""
+        if not hasattr(self, "pysdk_model") or self.pysdk_model is None:
+            self.pysdk_model = self._create_pre_trained_js_model()
+
         self.pysdk_model.display_benchmark_metrics()
 
     def list_deployment_configs(self) -> List[Dict[str, Any]]:
@@ -441,6 +468,9 @@ def list_deployment_configs(self) -> List[Dict[str, Any]]:
         Returns:
             List[Dict[str, Any]]: A list of deployment configs.
         """
+        if not hasattr(self, "pysdk_model") or self.pysdk_model is None:
+            self.pysdk_model = self._create_pre_trained_js_model()
+
         return self.pysdk_model.list_deployment_configs()
 
     def _build_for_jumpstart(self):
@@ -449,32 +479,29 @@ def _build_for_jumpstart(self):
         self.secret_key = None
         self.jumpstart = True
 
-        pysdk_model = self._create_pre_trained_js_model()
-
-        image_uri = pysdk_model.image_uri
+        if not hasattr(self, "pysdk_model") or self.pysdk_model is None:
+            self.pysdk_model = self._create_pre_trained_js_model()
 
-        logger.info("JumpStart ID %s is packaged with Image URI: %s", self.model, image_uri)
+        logger.info(
+            "JumpStart ID %s is packaged with Image URI: %s", self.model, self.pysdk_model.image_uri
+        )
 
-        if self._is_gated_model(pysdk_model) and self.mode != Mode.SAGEMAKER_ENDPOINT:
+        if self._is_gated_model() and self.mode != Mode.SAGEMAKER_ENDPOINT:
             raise ValueError(
                 "JumpStart Gated Models are only supported in SAGEMAKER_ENDPOINT mode."
             )
 
-        if "djl-inference" in image_uri:
+        if "djl-inference" in self.pysdk_model.image_uri:
             logger.info("Building for DJL JumpStart Model ID...")
             self.model_server = ModelServer.DJL_SERVING
-
-            self.pysdk_model = pysdk_model
             self.image_uri = self.pysdk_model.image_uri
 
             self._build_for_djl_jumpstart()
 
             self.pysdk_model.tune = self.tune_for_djl_jumpstart
-        elif "tgi-inference" in image_uri:
+        elif "tgi-inference" in self.pysdk_model.image_uri:
             logger.info("Building for TGI JumpStart Model ID...")
             self.model_server = ModelServer.TGI
-
-            self.pysdk_model = pysdk_model
             self.image_uri = self.pysdk_model.image_uri
 
             self._build_for_tgi_jumpstart()
@@ -487,15 +514,13 @@ def _build_for_jumpstart(self):
 
         return self.pysdk_model
 
-    def _is_gated_model(self, model) -> bool:
+    def _is_gated_model(self) -> bool:
         """Determine if ``this`` Model is Gated
 
-        Args:
-            model (Model): Jumpstart Model
         Returns:
             bool: ``True`` if ``this`` Model is Gated
         """
-        s3_uri = model.model_data
+        s3_uri = self.pysdk_model.model_data
         if isinstance(s3_uri, dict):
             s3_uri = s3_uri.get("S3DataSource").get("S3Uri")