Formatting and refactoring

PiperOrigin-RevId: 766516226

Author: vertex-mg-bot

notebooks/community/model_garden/model_garden_pytorch_biomedclip.ipynb

@@ -139,7 +139,6 @@
         "    \"vertex-ai-samples.community-content.vertex_model_garden.model_oss.notebook_util.common_util\"\n",
         ")\n",
         "\n",
-        "LABEL = \"biomedclip_serve\"\n",
         "models, endpoints = {}, {}\n",
         "\n",
         "# Get the default cloud project id.\n",
@@ -216,6 +215,7 @@
       "source": [
         "# @title [Option 1] Deploy with Model Garden SDK\n",
         "\n",
+        "LABEL = \"sdk-deploy\"\n",
         "# @markdown Deploy with Gen AI model-centric SDK. This section uploads the prebuilt model to Model Registry and deploys it to a Vertex AI Endpoint. It takes 15 minutes to 1 hour to finish depending on the size of the model. See [use open models with Vertex AI](https://cloud.google.com/vertex-ai/generative-ai/docs/open-models/use-open-models) for documentation on other use cases.\n",
         "from vertexai.preview import model_garden\n",
         "\n",
@@ -258,9 +258,13 @@
         "    machine_type: str,\n",
         "    accelerator_type: str,\n",
         "    accelerator_count: int,\n",
+        "    use_dedicated_endpoint: bool = False,\n",
         ") -> Tuple[aiplatform.Model, aiplatform.Endpoint]:\n",
         "    \"\"\"Deploys trained models into Vertex AI.\"\"\"\n",
-        "    endpoint = aiplatform.Endpoint.create(display_name=f\"{model_name}-endpoint\")\n",
+        "    endpoint = aiplatform.Endpoint.create(\n",
+        "        display_name=f\"{model_name}-endpoint\",\n",
+        "        dedicated_endpoint_enabled=use_dedicated_endpoint,\n",
+        "    )\n",
         "    serving_env = {\n",
         "        \"MODEL\": model_id,\n",
         "        \"TASK\": task,\n",
@@ -293,6 +297,8 @@
         "    return model, endpoint\n",
         "\n",
         "\n",
+        "LABEL = \"open-clip-deploy\"\n",
+        "\n",
         "models[LABEL], endpoints[LABEL] = deploy_model(\n",
         "    model_name=common_util.get_job_name_with_datetime(prefix=\"biomedclip-serve\"),\n",
         "    model_id=model_id,\n",
@@ -301,7 +307,11 @@
         "    machine_type=machine_type,\n",
         "    accelerator_type=accelerator_type,\n",
         "    accelerator_count=1,\n",
-        ")"
+        "    use_dedicated_endpoint=use_dedicated_endpoint,\n",
+        ")\n",
+        "\n",
+        "model = models[LABEL]\n",
+        "endpoint = endpoints[LABEL]"
       ]
     },
     {
@@ -364,7 +374,9 @@
         "    {\"text\": \"This is a photo of hematoxylin and eosin histopathology\"},\n",
         "    {\"text\": \"This is a photo of pie chart\"},\n",
         "]\n",
-        "response = endpoints[LABEL].predict(instances=instances)\n",
+        "response = endpoint.predict(\n",
+        "    instances=instances, use_dedicated_endpoint=use_dedicated_endpoint\n",
+        ")\n",
         "\n",
         "print(response.predictions)\n",
         "\n",