notebooks/community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ur8xi4C7S06n"
      },
      "outputs": [],
      "source": [
        "# Copyright 2022 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "title:generic,gcp"
      },
      "source": [
        "# E2E ML on GCP: MLOps stage 6 : Get started with TensorFlow serving functions with Vertex AI Prediction\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>         \n",
        "</table>\n",
        "<br/><br/><br/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial demonstrates how to add a serving function to a model deployed to a `Vertex AI Endpoint`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9402cfbdc2d"
      },
      "source": [
        "### Objective\n",
        "\n",
        "In this tutorial, you learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` resource with a serving function.\n",
        "\n",
        "This tutorial uses the following Google Cloud ML services and resources:\n",
        "\n",
        "- `Vertex AI Prediction`\n",
        "- `Vertex AI Models`\n",
        "- `Vertex AI Endpoints`\n",
        "\n",
        "The steps performed include:\n",
        "\n",
        "- Download a pretrained image classification model from TensorFlow Hub.\n",
        "- Create a serving function to receive compressed image data, and output decomopressed preprocessed data for the model input.\n",
        "- Upload the TensorFlow Hub model and serving function as a `Vertex AI Model` resource.\n",
        "- Creating an `Endpoint` resource.\n",
        "- Deploying the `Model` resource to an `Endpoint` resource.\n",
        "- Make an online prediction to the `Model` resource instance deployed to the `Endpoint` resource."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dataset:iris,lcn"
      },
      "source": [
        "### Dataset\n",
        "\n",
        "This tutorial uses a pre-trained image classification model from TensorFlow Hub, which is trained on ImageNet dataset.\n",
        "\n",
        "Learn more about [ResNet V2 pretained model](https://tfhub.dev/google/imagenet/resnet_v2_101/classification/5). "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "costs"
      },
      "source": [
        "### Costs\n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI\n",
        "* Cloud Storage\n",
        "\n",
        "Learn about [Vertex AI\n",
        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
        "Calculator](https://cloud.google.com/products/calculator/)\n",
        "to generate a cost estimate based on your projected usage."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "install_aip"
      },
      "source": [
        "## Installation\n",
        "\n",
        "Install the following packages to execute this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "install_aip"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# The Vertex AI Workbench Notebook product has specific requirements\n",
        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
        "    \"/opt/deeplearning/metadata/env_version\"\n",
        ")\n",
        "\n",
        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
        "USER_FLAG = \"\"\n",
        "if IS_WORKBENCH_NOTEBOOK:\n",
        "    USER_FLAG = \"--user\"\n",
        "\n",
        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q\n",
        "! pip3 install --upgrade google-cloud-pipeline-components $USER_FLAG -q\n",
        "! pip3 install tensorflow-hub $USER_FLAG -q"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hhq5zEbGg0XX"
      },
      "source": [
        "### Restart the kernel\n",
        "\n",
        "After you install the additional packages, you need to restart the notebook kernel so it can find the packages."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EzrelQZ22IZj"
      },
      "outputs": [],
      "source": [
        "# Automatically restart kernel after installs\n",
        "import os\n",
        "\n",
        "if not os.getenv(\"IS_TESTING\"):\n",
        "    # Automatically restart kernel after installs\n",
        "    import IPython\n",
        "\n",
        "    app = IPython.Application.instance()\n",
        "    app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "before_you_begin"
      },
      "source": [
        "## Before you begin\n",
        "\n",
        "### GPU runtime\n",
        "\n",
        "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n",
        "\n",
        "### Set up your Google Cloud project\n",
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
        "\n",
        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
        "\n",
        "3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage-component.googleapis.com)\n",
        "\n",
        "4. If you are running this notebook locally, you need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
        "\n",
        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
        "Cloud SDK uses the right project for all the commands in this notebook.\n",
        "\n",
        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "project_id"
      },
      "source": [
        "#### Set your project ID\n",
        "\n",
        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "set_project_id"
      },
      "outputs": [],
      "source": [
        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "autoset_project_id"
      },
      "outputs": [],
      "source": [
        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
        "    # Get your GCP project id from gcloud\n",
        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
        "    PROJECT_ID = shell_output[0]\n",
        "    print(\"Project ID:\", PROJECT_ID)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "set_gcloud_project_id"
      },
      "outputs": [],
      "source": [
        "! gcloud config set project $PROJECT_ID"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "region"
      },
      "source": [
        "#### Region\n",
        "\n",
        "You can also change the `REGION` variable, which is used for operations\n",
        "throughout the rest of this notebook.  Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
        "\n",
        "- Americas: `us-central1`\n",
        "- Europe: `europe-west4`\n",
        "- Asia Pacific: `asia-east1`\n",
        "\n",
        "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
        "\n",
        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "region"
      },
      "outputs": [],
      "source": [
        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
        "\n",
        "if REGION == \"[your-region]\":\n",
        "    REGION = \"us-central1\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "timestamp"
      },
      "source": [
        "#### Timestamp\n",
        "\n",
        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "timestamp"
      },
      "outputs": [],
      "source": [
        "from datetime import datetime\n",
        "\n",
        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcp_authenticate"
      },
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
        "\n",
        "**Otherwise**, follow these steps:\n",
        "\n",
        "In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
        "\n",
        "**Click Create service account**.\n",
        "\n",
        "In the **Service account name** field, enter a name, and click **Create**.\n",
        "\n",
        "In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
        "\n",
        "Click Create. A JSON file that contains your key downloads to your local environment.\n",
        "\n",
        "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gcp_authenticate"
      },
      "outputs": [],
      "source": [
        "# If you are running this notebook in Colab, run this cell and follow the\n",
        "# instructions to authenticate your GCP account. This provides access to your\n",
        "# Cloud Storage bucket and lets you submit training jobs and prediction\n",
        "# requests.\n",
        "\n",
        "import os\n",
        "import sys\n",
        "\n",
        "# If on Vertex AI Workbench, then don't execute this code\n",
        "IS_COLAB = False\n",
        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
        "    \"DL_ANACONDA_HOME\"\n",
        "):\n",
        "    if \"google.colab\" in sys.modules:\n",
        "        IS_COLAB = True\n",
        "        from google.colab import auth as google_auth\n",
        "\n",
        "        google_auth.authenticate_user()\n",
        "\n",
        "    # If you are running this notebook locally, replace the string below with the\n",
        "    # path to your service account key and run this cell to authenticate your GCP\n",
        "    # account.\n",
        "    elif not os.getenv(\"IS_TESTING\"):\n",
        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bucket:mbsdk"
      },
      "source": [
        "### Create a Cloud Storage bucket\n",
        "\n",
        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
        "When you initialize the Vertex AI SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
        "\n",
        "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bucket"
      },
      "outputs": [],
      "source": [
        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "autoset_bucket"
      },
      "outputs": [],
      "source": [
        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "create_bucket"
      },
      "source": [
        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "create_bucket"
      },
      "outputs": [],
      "source": [
        "! gsutil mb -l $REGION $BUCKET_URI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "validate_bucket"
      },
      "source": [
        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "validate_bucket"
      },
      "outputs": [],
      "source": [
        "! gsutil ls -al $BUCKET_URI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "setup_vars"
      },
      "source": [
        "### Set up variables\n",
        "\n",
        "Next, set up some variables used throughout the tutorial.\n",
        "### Import libraries and define constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "import_aip:mbsdk"
      },
      "outputs": [],
      "source": [
        "import google.cloud.aiplatform as aip\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "init_aip:mbsdk"
      },
      "source": [
        "### Initialize Vertex AI SDK for Python\n",
        "\n",
        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "init_aip:mbsdk"
      },
      "outputs": [],
      "source": [
        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "accelerators:training,cpu,prediction,cpu,mbsdk"
      },
      "source": [
        "#### Set hardware accelerators\n",
        "\n",
        "You can set hardware accelerators for training and prediction.\n",
        "\n",
        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Telsa K80 GPUs allocated to each VM, you would specify:\n",
        "\n",
        "    (aip.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
        "\n",
        "\n",
        "Otherwise specify `(None, None)` to use a container image to run on a CPU.\n",
        "\n",
        "Learn more about [hardware accelerator support for your region](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
        "\n",
        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3. This is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "accelerators:training,cpu,prediction,cpu,mbsdk"
      },
      "outputs": [],
      "source": [
        "if os.getenv(\"IS_TESTING_DEPLOY_GPU\"):\n",
        "    DEPLOY_GPU, DEPLOY_NGPU = (\n",
        "        aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
        "        int(os.getenv(\"IS_TESTING_DEPLOY_GPU\")),\n",
        "    )\n",
        "else:\n",
        "    DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "container:training,prediction"
      },
      "source": [
        "#### Set pre-built containers\n",
        "\n",
        "Set the pre-built Docker container image for prediction.\n",
        "\n",
        "\n",
        "For the latest list, see [Pre-built containers for prediction](https://cloud.google.com/ai-platform-unified/docs/predictions/pre-built-containers)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "container:training,prediction"
      },
      "outputs": [],
      "source": [
        "if os.getenv(\"IS_TESTING_TF\"):\n",
        "    TF = os.getenv(\"IS_TESTING_TF\")\n",
        "else:\n",
        "    TF = \"2.5\".replace(\".\", \"-\")\n",
        "\n",
        "if TF[0] == \"2\":\n",
        "    if DEPLOY_GPU:\n",
        "        DEPLOY_VERSION = \"tf2-gpu.{}\".format(TF)\n",
        "    else:\n",
        "        DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
        "else:\n",
        "    if DEPLOY_GPU:\n",
        "        DEPLOY_VERSION = \"tf-gpu.{}\".format(TF)\n",
        "    else:\n",
        "        DEPLOY_VERSION = \"tf-cpu.{}\".format(TF)\n",
        "\n",
        "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
        ")\n",
        "\n",
        "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "machine:training"
      },
      "source": [
        "#### Set machine type\n",
        "\n",
        "Next, set the machine type to use for prediction.\n",
        "\n",
        "- Set the variable `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for prediction.\n",
        " - `machine type`\n",
        "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
        "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
        "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
        " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
        "\n",
        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "machine:training"
      },
      "outputs": [],
      "source": [
        "if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n",
        "    MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n",
        "else:\n",
        "    MACHINE_TYPE = \"n1-standard\"\n",
        "\n",
        "VCPU = \"4\"\n",
        "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
        "print(\"Train machine type\", DEPLOY_COMPUTE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d8128b8ff025"
      },
      "source": [
        "## Get pretrained model from TensorFlow Hub\n",
        "\n",
        "For demonstration purposes, this tutorial uses a pretrained model from TensorFlow Hub (TFHub), which is then uploaded to a `Vertex AI Model` resource. Once you have a `Vertex AI Model` resource, the model can be deployed to a `Vertex AI Endpoint` resource.\n",
        "\n",
        "### Download the pretrained model\n",
        "\n",
        "First, you download the pretrained model from TensorFlow Hub. The model gets downloaded as a TF.Keras layer. To finalize the model, in this example, you create a `Sequential()` model with the downloaded TFHub model as a layer, and specify the input shape to the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "c55fa4c826f7"
      },
      "outputs": [],
      "source": [
        "tfhub_model = tf.keras.Sequential(\n",
        "    [hub.KerasLayer(\"https://tfhub.dev/google/imagenet/resnet_v2_101/classification/5\")]\n",
        ")\n",
        "\n",
        "tfhub_model.build([None, 224, 224, 3])\n",
        "\n",
        "tfhub_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "63de49055083"
      },
      "source": [
        "### Save the model artifacts\n",
        "\n",
        "At this point, the model is in memory. Next, you save the model artifacts to a Cloud Storage location."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "64618c713db9"
      },
      "outputs": [],
      "source": [
        "MODEL_DIR = BUCKET_URI + \"/model\"\n",
        "tfhub_model.save(MODEL_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "how_serving_function_works"
      },
      "source": [
        "## Upload the model for serving\n",
        "\n",
        "Next, you will upload your TF.Keras model from the custom job to Vertex `Model` service, which will create a Vertex `Model` resource for your custom model. During upload, you need to define a serving function to convert data to the format your model expects. If you send encoded data to Vertex AI, your serving function ensures that the data is decoded on the model server before it is passed as input to your model.\n",
        "\n",
        "### How does the serving function work\n",
        "\n",
        "When you send a request to an online prediction server, the request is received by a HTTP server. The HTTP server extracts the prediction request from the HTTP request content body. The extracted prediction request is forwarded to the serving function. For Google pre-built prediction containers, the request content is passed to the serving function as a `tf.string`.\n",
        "\n",
        "The serving function consists of two parts:\n",
        "\n",
        "- `preprocessing function`:\n",
        "  - Converts the input (`tf.string`) to the input shape and data type of the underlying model (dynamic graph).\n",
        "  - Performs the same preprocessing of the data that was done during training the underlying model -- e.g., normalizing, scaling, etc.\n",
        "- `post-processing function`:\n",
        "  - Converts the model output to format expected by the receiving application -- e.q., compresses the output.\n",
        "  - Packages the output for the the receiving application -- e.g., add headings, make JSON object, etc.\n",
        "\n",
        "Both the preprocessing and post-processing functions are converted to static graphs which are fused to the model. The output from the underlying model is passed to the post-processing function. The post-processing function passes the converted/packaged output back to the HTTP server. The HTTP server returns the output as the HTTP response content.\n",
        "\n",
        "One consideration you need to consider when building serving functions for TF.Keras models is that they run as static graphs. That means, you cannot use TF graph operations that require a dynamic graph. If you do, you will get an error during the compile of the serving function which will indicate that you are using an EagerTensor which is not supported."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "serving_function_image:post"
      },
      "source": [
        "### Serving function for image data\n",
        "\n",
        "#### Preprocessing\n",
        "\n",
        "To pass images to the prediction service, you encode the compressed (e.g., JPEG) image bytes into base 64 -- which makes the content safe from modification while transmitting binary data over the network. Since this deployed model expects input data as raw (uncompressed) bytes, you need to ensure that the base 64 encoded data gets converted back to raw bytes, and then preprocessed to match the model input requirements, before it is passed as input to the deployed model.\n",
        "\n",
        "To resolve this, you define a serving function (`serving_fn`) and attach it to the model as a preprocessing step. Add a `@tf.function` decorator so the serving function is fused to the underlying model (instead of upstream on a CPU).\n",
        "\n",
        "When you send a prediction or explanation request, the content of the request is base 64 decoded into a Tensorflow string (`tf.string`), which is passed to the serving function (`serving_fn`). The serving function preprocesses the `tf.string` into raw (uncompressed) numpy bytes (`preprocess_fn`) to match the input requirements of the model:\n",
        "\n",
        "- `io.decode_jpeg`- Decompresses the JPG image which is returned as a Tensorflow tensor with three channels (RGB).\n",
        "- `image.convert_image_dtype` - Changes integer pixel values to float 32, and rescales pixel data between 0 and 1.\n",
        "- `image.resize` - Resizes the image to match the input shape for the model.\n",
        "\n",
        "At this point, the data can be passed to the model (`m_call`), via a concrete function. The serving function is a static graph, while the model is a dynamic graph. The concrete function performs the tasks of marshalling the input data from the serving function to the model, and marshalling the prediction result from the model back to the serving function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "serving_function_image"
      },
      "outputs": [],
      "source": [
        "CONCRETE_INPUT = \"numpy_inputs\"\n",
        "\n",
        "\n",
        "def _preprocess(bytes_input):\n",
        "    decoded = tf.io.decode_jpeg(bytes_input, channels=3)\n",
        "    decoded = tf.image.convert_image_dtype(decoded, tf.float32)\n",
        "    resized = tf.image.resize(decoded, size=(224, 224))\n",
        "    return resized\n",
        "\n",
        "\n",
        "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n",
        "def preprocess_fn(bytes_inputs):\n",
        "    decoded_images = tf.map_fn(\n",
        "        _preprocess, bytes_inputs, dtype=tf.float32, back_prop=False\n",
        "    )\n",
        "    return {\n",
        "        CONCRETE_INPUT: decoded_images\n",
        "    }  # User needs to make sure the key matches model's input\n",
        "\n",
        "\n",
        "@tf.function(input_signature=[tf.TensorSpec([None], tf.string)])\n",
        "def serving_fn(bytes_inputs):\n",
        "    images = preprocess_fn(bytes_inputs)\n",
        "    prob = m_call(**images)\n",
        "    return prob\n",
        "\n",
        "\n",
        "m_call = tf.function(tfhub_model.call).get_concrete_function(\n",
        "    [tf.TensorSpec(shape=[None, 224, 224, 3], dtype=tf.float32, name=CONCRETE_INPUT)]\n",
        ")\n",
        "\n",
        "tf.saved_model.save(tfhub_model, MODEL_DIR, signatures={\"serving_default\": serving_fn})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "serving_function_signature:image"
      },
      "source": [
        "## Get the serving function signature\n",
        "\n",
        "You can get the signatures of your model's input and output layers by reloading the model into memory, and querying it for the signatures corresponding to each layer.\n",
        "\n",
        "For your purpose, you need the signature of the serving function. Why? Well, when we send our data for prediction as a HTTP request packet, the image data is base64 encoded, and our TF.Keras model takes numpy input. Your serving function will do the conversion from base64 to a numpy array.\n",
        "\n",
        "When making a prediction request, you need to route the request to the serving function instead of the model, so you need to know the input layer name of the serving function -- which you will use later when you make a prediction request."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "serving_function_signature:image"
      },
      "outputs": [],
      "source": [
        "loaded = tf.saved_model.load(MODEL_DIR)\n",
        "\n",
        "serving_input = list(\n",
        "    loaded.signatures[\"serving_default\"].structured_input_signature[1].keys()\n",
        ")[0]\n",
        "print(\"Serving function input:\", serving_input)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e8ce91147c93"
      },
      "source": [
        "### Upload the TensorFlow Hub model to a `Vertex AI Model` resource\n",
        "\n",
        "Finally, you upload the model artifacts from the TFHub model and serving function into a `Vertex AI Model` resource.\n",
        "\n",
        "*Note:* When you upload the model artifacts to a `Vertex AI Model` resource, you specify the corresponding deployment container image."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ad61e1429512"
      },
      "outputs": [],
      "source": [
        "model = aip.Model.upload(\n",
        "    display_name=\"example_\" + TIMESTAMP,\n",
        "    artifact_uri=MODEL_DIR,\n",
        "    serving_container_image_uri=DEPLOY_IMAGE,\n",
        ")\n",
        "\n",
        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "628de0914ba1"
      },
      "source": [
        "## Creating an `Endpoint` resource\n",
        "\n",
        "You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
        "\n",
        "In this example, the following parameters are specified:\n",
        "\n",
        "- `display_name`: A human readable name for the `Endpoint` resource.\n",
        "- `project`: Your project ID.\n",
        "- `location`: Your region.\n",
        "- `labels`: (optional) User defined metadata for the `Endpoint` in the form of key/value pairs.\n",
        "\n",
        "This method returns an `Endpoint` object.\n",
        "\n",
        "Learn more about [Vertex AI Endpoints](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0ea443f9593b"
      },
      "outputs": [],
      "source": [
        "endpoint = aip.Endpoint.create(\n",
        "    display_name=\"example_\" + TIMESTAMP,\n",
        "    project=PROJECT_ID,\n",
        "    location=REGION,\n",
        "    labels={\"your_key\": \"your_value\"},\n",
        ")\n",
        "\n",
        "print(endpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ca3fa3f6a894"
      },
      "source": [
        "## Deploying `Model` resources to an `Endpoint` resource.\n",
        "\n",
        "You can deploy one of more `Vertex AI Model` resource instances to the same endpoint. Each `Vertex AI Model` resource that is deployed will have its own deployment container for the serving binary. \n",
        "\n",
        "*Note:* For this example, you specified the deployment container for the TFHub model in the previous step of uploading the model artifacts to a `Vertex AI Model` resource.\n",
        "\n",
        "In the next example, you deploy the `Vertex AI Model` resource to a `Vertex AI Endpoint` resource. The `Vertex AI Model` resource already has defined for it the deployment container image. To deploy, you specify the following additional configuration settings:\n",
        "\n",
        "- The machine type.\n",
        "- The (if any) type and number of GPUs.\n",
        "- Static, manual or auto-scaling of VM instances.\n",
        "\n",
        "In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
        "\n",
        "- `model`: The `Model` resource.\n",
        "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
        "- `machine_type`: The machine type for each VM instance.\n",
        "\n",
        "Do to the requirements to provision the resource, this may take upto a few minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4e93b034a72f"
      },
      "outputs": [],
      "source": [
        "response = endpoint.deploy(\n",
        "    model=model,\n",
        "    deployed_model_display_name=\"example_\" + TIMESTAMP,\n",
        "    machine_type=DEPLOY_COMPUTE,\n",
        ")\n",
        "\n",
        "print(endpoint)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8fc02ce0d072"
      },
      "source": [
        "### Prepare test data for prediction\n",
        "\n",
        "Next, you will load a compressed JPEG image into memory and then base64 encode it. For demonstration purposes, you use an image from the Flowers dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "033a5bfb969c"
      },
      "outputs": [],
      "source": [
        "! gsutil cp gs://cloud-ml-data/img/flower_photos/daisy/100080576_f52e8ee070_n.jpg test.jpg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "54947579cdf7"
      },
      "outputs": [],
      "source": [
        "import base64\n",
        "\n",
        "with open(\"test.jpg\", \"rb\") as f:\n",
        "    data = f.read()\n",
        "b64str = base64.b64encode(data).decode(\"utf-8\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "predict_request:mbsdk,custom,icn"
      },
      "source": [
        "### Make the prediction\n",
        "\n",
        "Now that your `Model` resource is deployed to an `Endpoint` resource, you can do online predictions by sending prediction requests to the Endpoint resource.\n",
        "\n",
        "#### Request\n",
        "\n",
        "Since in this example your test item is in a Cloud Storage bucket, you open and read the contents of the image using `tf.io.gfile.Gfile()`. To pass the test data to the prediction service, you encode the bytes into base64 -- which makes the content safe from modification while transmitting binary data over the network.\n",
        "\n",
        "The format of each instance is:\n",
        "\n",
        "    { serving_input: { 'b64': base64_encoded_bytes } }\n",
        "\n",
        "Since the `predict()` method can take multiple items (instances), send your single test item as a list of one test item.\n",
        "\n",
        "#### Response\n",
        "\n",
        "The response from the `predict()` call is a Python dictionary with the following entries:\n",
        "\n",
        "- `ids`: The internal assigned unique identifiers for each prediction request.\n",
        "- `predictions`: The predicted confidence, between 0 and 1, per class label.\n",
        "- `deployed_model_id`: The Vertex AI identifier for the deployed `Model` resource which did the predictions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "predict_request:mbsdk,custom,icn"
      },
      "outputs": [],
      "source": [
        "# The format of each instance should conform to the deployed model's prediction input schema.\n",
        "instances = [{serving_input: {\"b64\": b64str}}]\n",
        "\n",
        "prediction = endpoint.predict(instances=instances)\n",
        "\n",
        "print(prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpV-iwP9qw9c"
      },
      "source": [
        "## Cleaning up\n",
        "\n",
        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
        "\n",
        "Otherwise, you can delete the individual resources you created in this tutorial:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sx_vKniMq9ZX"
      },
      "outputs": [],
      "source": [
        "delete_bucket = False\n",
        "delete_model = True\n",
        "delete_endpoint = True\n",
        "\n",
        "if delete_endpoint:\n",
        "    try:\n",
        "        endpoint.undeploy_all()\n",
        "        endpoint.delete()\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "\n",
        "if delete_model:\n",
        "    try:\n",
        "        model.delete()\n",
        "    except Exception as e:\n",
        "        print(e)\n",
        "\n",
        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
        "    ! gsutil rm -rf {BUCKET_URI}"
      ]
    }
  ],
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