notebook fixes

introduction_to_amazon_algorithms/jumpstart_object_detection/Amazon_JumpStart_Object_Detection.ipynb

@@ -98,7 +98,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "!pip install sagemaker ipywidgets --upgrade --quiet"
+    "!pip install sagemaker jupyterlab --upgrade --quiet\n",
+    "!pip install ipywidgets==7.6.5"
    ]
   },
   {
@@ -234,9 +235,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from sagemaker import image_uris, model_uris, script_uris, hyperparameters\n",
-    "from sagemaker.model import Model\n",
-    "from sagemaker.predictor import Predictor\n",
+    "from sagemaker.jumpstart.model import JumpStartModel\n",
     "from sagemaker.utils import name_from_base\n",
     "\n",
     "# model_version=\"*\" fetches the latest version of the model\n",
@@ -247,45 +246,17 @@
     "\n",
     "inference_instance_type = \"ml.p2.xlarge\"\n",
     "\n",
-    "# Retrieve the inference docker container uri\n",
-    "deploy_image_uri = image_uris.retrieve(\n",
-    "    region=None,\n",
-    "    framework=None,  # automatically inferred from model_id\n",
-    "    image_scope=\"inference\",\n",
+    "# Create the SageMaker JumpStart model instance\n",
+    "model = JumpStartModel(\n",
     "    model_id=infer_model_id,\n",
     "    model_version=infer_model_version,\n",
-    "    instance_type=inference_instance_type,\n",
-    ")\n",
-    "\n",
-    "# Retrieve the inference script uri. This includes scripts for model loading, inference handling etc.\n",
-    "deploy_source_uri = script_uris.retrieve(\n",
-    "    model_id=infer_model_id, model_version=infer_model_version, script_scope=\"inference\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# Retrieve the base model uri\n",
-    "base_model_uri = model_uris.retrieve(\n",
-    "    model_id=infer_model_id, model_version=infer_model_version, model_scope=\"inference\"\n",
-    ")\n",
-    "\n",
-    "\n",
-    "# Create the SageMaker model instance\n",
-    "model = Model(\n",
-    "    image_uri=deploy_image_uri,\n",
-    "    source_dir=deploy_source_uri,\n",
-    "    model_data=base_model_uri,\n",
-    "    entry_point=\"inference.py\",  # entry point file in source_dir and present in deploy_source_uri\n",
     "    role=aws_role,\n",
-    "    predictor_cls=Predictor,\n",
     "    name=endpoint_name,\n",
     ")\n",
     "\n",
-    "# deploy the Model. Note that we need to pass Predictor class when we deploy model through Model class,\n",
-    "# for being able to run inference through the sagemaker API.\n",
     "base_model_predictor = model.deploy(\n",
     "    initial_instance_count=1,\n",
     "    instance_type=inference_instance_type,\n",
-    "    predictor_cls=Predictor,\n",
     "    endpoint_name=endpoint_name,\n",
     ")"
    ]
@@ -355,8 +326,7 @@
     "    return query_response\n",
     "\n",
     "\n",
-    "def parse_response(query_response):\n",
-    "    model_predictions = json.loads(query_response)\n",
+    "def parse_response(model_predictions):\n",
     "    normalized_boxes, classes, scores, labels = (\n",
     "        model_predictions[\"normalized_boxes\"],\n",
     "        model_predictions[\"classes\"],\n",
@@ -837,8 +807,10 @@
    "outputs": [],
    "source": [
     "query_response = query(finetuned_predictor, pedestrian_image_file_name)\n",
+    "model_predictions = json.loads(query_response)\n",
+    "\n",
     "\n",
-    "normalized_boxes, classes_names, confidences = parse_response(query_response)\n",
+    "normalized_boxes, classes_names, confidences = parse_response(model_predictions)\n",
     "display_predictions(pedestrian_image_file_name, normalized_boxes, classes_names, confidences)"
    ]
   },