update: official requirements

notebooks/community/model_garden/model_garden_jax_vision_transformer.ipynb

@@ -72,7 +72,9 @@
       "source": [
         "## Overview\n",
         "\n",
-        "This notebook demonstrates finetuning a [JAX ViT-B16 model](https://github.com/google-research/vision_transformer#available-vit-models) for image classification task on GPU and deploying them on Vertex AI for online prediction."
+        "This notebook demonstrates finetuning a [JAX ViT-B16 model](https://github.com/google-research/vision_transformer#available-vit-models) for image classification task on GPU and deploying them on Vertex AI for online prediction.\n",
+        "\n",
+        "Learn more about [Generative AI Support in Vertex AI](https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-model-garden-and-generative-ai-studio)."
       ]
     },
     {
@@ -83,18 +85,21 @@
       "source": [
         "### Objective\n",
         "\n",
-        "In this tutorial, you learn how to:\n",
+        "In this tutorial, you learn how fine-tune, deploy and predict with a Vertex AI pretrained JAX Vision Transformer based model.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI Model Garden\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI Model Registry\n",
+        "- Vertex AI Online Prediction\n",
+        "\n",
+        "The steps performed are:\n",
         "\n",
         "- Finetune a JAX Vision Transformer based model.\n",
         "- Upload the model to [Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction).\n",
         "- Deploy the model on [Endpoint](https://cloud.google.com/vertex-ai/docs/predictions/using-private-endpoints).\n",
-        "- Run online predictions for image classification.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
-        "\n",
-        "- Vertex AI Training\n",
-        "- Vertex AI Model Registry\n",
-        "- Vertex AI Online Prediction"
+        "- Run online predictions for image classification.\n"
       ]
     },
     {
@@ -165,10 +170,10 @@
       "outputs": [],
       "source": [
         "# Automatically restart kernel after installs so that your environment can access the new packages.\n",
-        "import IPython\n",
+        "# import IPython\n",
         "\n",
-        "app = IPython.Application.instance()\n",
-        "app.kernel.do_shutdown(True)"
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
       ]
     },
     {
@@ -214,7 +219,7 @@
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"your-project-id\"  # @param {type:\"string\"}\n",
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
         "\n",
         "# Set the project id\n",
         "! gcloud config set project {PROJECT_ID}"
@@ -511,7 +516,7 @@
         "### Prepare dataset\n",
         "\n",
         "If you are not using [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview#all_datasets), then you need to prepare your dataset and store it on Cloud Storage. The following example shows\n",
-        "how to do this for the [tf_flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers). If using TensorFlow Datasets, you can just pass\n",
+        "how to do this for the [tf_flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers). If using TensorFlow Datasets, you pass\n",
         "the dataset name such as `tf_flowers` to the `--config.dataset` flag and bypass this section."
       ]
     },
@@ -853,7 +858,7 @@
         "jax_vit_model.delete()\n",
         "\n",
         "# Delete Cloud Storage objects that were created.\n",
-        "delete_bucket = False\n",
+        "delete_bucket = True\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
         "    ! gsutil -m rm -r $BUCKET_URI"
       ]