notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "copyright"
      },
      "outputs": [],
      "source": [
        "# Copyright 2021 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:migration,new"
      },
      "source": [
        "# Vertex AI Migration: Custom image classification with a pre-built training container\n",
        "\n",
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/sdk-custom-image-classification-prebuilt-container.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/official/migration/sdk-custom-image-classification-prebuilt-container.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/official/migration/sdk-custom-image-classification-prebuilt-container.ipynb\" target='_blank'>\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": "7a8a13b86a8b"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This tutorial demonstrates how to use the Vertex AI SDK for Python to train using a pre-built container and deploy a custom image classification model for online and batch prediction.\n",
        "\n",
        "Learn more about [Migrate to Vertex AI](https://cloud.google.com/vertex-ai/docs/start/migrating-to-vertex-ai) and [Custom training](https://cloud.google.com/vertex-ai/docs/training/custom-training)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f1ae7d54ad29"
      },
      "source": [
        "### Objective\n",
        "\n",
        "In this tutorial, you learn how to train a tensorflow image classification model using a prebuilt container and Vertex AI training. After training, you also deploy the model to Vertex AI using a pre-built container and generate both batch and online predictions on it. \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 Predictions\n",
        "- Vertex AI Batch Predictions\n",
        "- Vertex AI Endpoints\n",
        "\n",
        "\n",
        "The steps performed include:\n",
        "\n",
        "- *Package the training code into a python application.*\n",
        "- *Containerize the training application using Cloud Build and Artifact Registry.*\n",
        "- *Create a custom container training job in Vertex AI and run it.*\n",
        "- *Evaluate the model generated from the training job.*\n",
        "- *Create a model resource for the trained model in Vertex AI Model Registry.*\n",
        "- *Run a Vertex AI batch prediction job.*\n",
        "- *Deploy the model resource to a Vertex AI Endpoint.*\n",
        "- *Run a online prediction job on the model resource.*\n",
        "- *Clean up the resources created.*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dataset:custom,cifar10,icn"
      },
      "source": [
        "### Dataset\n",
        "\n",
        "The dataset used for this tutorial is the [CIFAR10 dataset](https://www.tensorflow.org/datasets/catalog/cifar10) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you will use is built into TensorFlow. The trained model predicts which type of class an image is from ten classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck."
      ]
    },
    {
      "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:mbsdk"
      },
      "source": [
        "## Installation\n",
        "\n",
        "Install the packages required for executing this notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "install_aip:mbsdk"
      },
      "outputs": [],
      "source": [
        "! pip3 install --upgrade --quiet google-cloud-aiplatform google-cloud-storage\n",
        "\n",
        "! apt-get update && apt-get install -y python3-opencv-headless\n",
        "! apt-get install -y libgl1-mesa-dev\n",
        "! pip3 install --upgrade opencv-python-headless  \n",
        "! pip3 install --upgrade tensorflow  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "restart"
      },
      "source": [
        "### Colab only: Uncomment the following cell to restart the kernel"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D-ZBOjErv5mM"
      },
      "outputs": [],
      "source": [
        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
        "# import IPython\n",
        "\n",
        "# app = IPython.Application.instance()\n",
        "# app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "before_you_begin:nogpu"
      },
      "source": [
        "## Before you begin"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "before_you_begin:nogpu"
      },
      "source": [
        "### Set your project ID\n",
        "\n",
        "**If you don't know your project ID**, try the following:\n",
        "* Run `gcloud config list`.\n",
        "* Run `gcloud projects list`.\n",
        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "set_project_id"
      },
      "outputs": [],
      "source": [
        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
        "\n",
        "# Set the project id\n",
        "! gcloud config set project {PROJECT_ID}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "region"
      },
      "source": [
        "#### Region\n",
        "\n",
        "You can also change the `REGION` variable used by Vertex AI. 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 = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "timestamp"
      },
      "source": [
        "#### UUID\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 uuid for each instance session, and append it onto the name of resources you create in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e87d5856317d"
      },
      "outputs": [],
      "source": [
        "import random\n",
        "import string\n",
        "\n",
        "\n",
        "# Generate a uuid of a specifed length(default=8)\n",
        "def generate_uuid(length: int = 8) -> str:\n",
        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
        "\n",
        "\n",
        "UUID = generate_uuid()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gcp_authenticate"
      },
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
        "\n",
        "**1. Vertex AI Workbench**\n",
        "* Do nothing as you are already authenticated.\n",
        "\n",
        "**2. Local JupyterLab instance, uncomment and run:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ce6043da7b33"
      },
      "outputs": [],
      "source": [
        "# ! gcloud auth login"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0367eac06a10"
      },
      "source": [
        "**3. Colab, uncomment and run:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "21ad4dbb4a61"
      },
      "outputs": [],
      "source": [
        "# from google.colab import auth\n",
        "# auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c13224697bfb"
      },
      "source": [
        "**4. Service account or other**\n",
        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bucket:mbsdk"
      },
      "source": [
        "### Create a Cloud Storage bucket\n",
        "\n",
        "Create a storage bucket to store intermediate artifacts such as datasets."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bucket"
      },
      "outputs": [],
      "source": [
        "BUCKET_URI = f\"gs://your-bucket-name-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
      "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": "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"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "init_aip:mbsdk"
      },
      "source": [
        "## Initialize Vertex 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 `TRAIN_GPU/TRAIN_NGPU` and `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.gapic.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 [here](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) hardware accelerator support for your region\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 -- which 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": [
        "TRAIN_GPU, TRAIN_NGPU = (None, None)\n",
        "\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 training and prediction.\n",
        "\n",
        "\n",
        "For the latest list, see [Pre-built containers for training](https://cloud.google.com/ai-platform-unified/docs/training/pre-built-containers).\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": [
        "TF = \"2-5\"\n",
        "\n",
        "if TRAIN_GPU:\n",
        "    TRAIN_VERSION = \"tf-gpu.{}\".format(TF)\n",
        "else:\n",
        "    TRAIN_VERSION = \"tf-cpu.{}\".format(TF)\n",
        "if DEPLOY_GPU:\n",
        "    DEPLOY_VERSION = \"tf2-gpu.{}\".format(TF)\n",
        "else:\n",
        "    DEPLOY_VERSION = \"tf2-cpu.{}\".format(TF)\n",
        "\n",
        "TRAIN_IMAGE = \"us-docker.pkg.dev/vertex-ai/training/{}:latest\".format(TRAIN_VERSION)\n",
        "DEPLOY_IMAGE = \"us-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(DEPLOY_VERSION)\n",
        "\n",
        "print(\"Training:\", TRAIN_IMAGE, TRAIN_GPU, TRAIN_NGPU)\n",
        "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "machine:training,prediction"
      },
      "source": [
        "#### Set machine type\n",
        "\n",
        "Next, set the machine type to use for training and prediction.\n",
        "\n",
        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training and 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: The following is not supported for training:*\n",
        "\n",
        " - `standard`: 2 vCPUs\n",
        " - `highcpu`: 2, 4 and 8 vCPUs\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,prediction"
      },
      "outputs": [],
      "source": [
        "MACHINE_TYPE = \"n1-standard\"\n",
        "\n",
        "VCPU = \"4\"\n",
        "TRAIN_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
        "print(\"Train machine type\", TRAIN_COMPUTE)\n",
        "\n",
        "VCPU = \"4\"\n",
        "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
        "print(\"Deploy machine type\", DEPLOY_COMPUTE)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "examine_training_package"
      },
      "source": [
        "### Examine the training package\n",
        "\n",
        "#### Package layout\n",
        "\n",
        "Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
        "\n",
        "- PKG-INFO\n",
        "- README.md\n",
        "- setup.cfg\n",
        "- setup.py\n",
        "- trainer\n",
        "  - \\_\\_init\\_\\_.py\n",
        "  - task.py\n",
        "\n",
        "The files `setup.cfg` and `setup.py` are the instructions for installing the package into the operating environment of the Docker image.\n",
        "\n",
        "The file `trainer/task.py` is the Python script for executing the custom training job. *Note*, when we referred to it in the worker pool specification, we replace the directory slash with a dot (`trainer.task`) and dropped the file suffix (`.py`).\n",
        "\n",
        "#### Package Assembly\n",
        "\n",
        "In the following cells, you assemble the training package."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "examine_training_package"
      },
      "outputs": [],
      "source": [
        "# Make folder for Python training script\n",
        "! rm -rf custom\n",
        "! mkdir custom\n",
        "\n",
        "# Add package information\n",
        "! touch custom/README.md\n",
        "\n",
        "setup_cfg = \"[egg_info]\\n\\ntag_build =\\n\\ntag_date = 0\"\n",
        "! echo \"$setup_cfg\" > custom/setup.cfg\n",
        "\n",
        "setup_py = \"import setuptools\\n\\nsetuptools.setup(\\n\\n    install_requires=[\\n\\n        'tensorflow_datasets==1.3.0',\\n\\n    ],\\n\\n    packages=setuptools.find_packages())\"\n",
        "! echo \"$setup_py\" > custom/setup.py\n",
        "\n",
        "pkg_info = \"Metadata-Version: 1.0\\n\\nName: CIFAR10 image classification\\n\\nVersion: 0.0.0\\n\\nSummary: Demostration training script\\n\\nHome-page: www.google.com\\n\\nAuthor: Google\\n\\nAuthor-email: aferlitsch@google.com\\n\\nLicense: Public\\n\\nDescription: Demo\\n\\nPlatform: Vertex\"\n",
        "! echo \"$pkg_info\" > custom/PKG-INFO\n",
        "\n",
        "# Make the training subfolder\n",
        "! mkdir custom/trainer\n",
        "! touch custom/trainer/__init__.py"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "taskpy_contents:cifar10"
      },
      "source": [
        "#### Task.py contents\n",
        "\n",
        "In the next cell, you write the contents of the training script task.py. We won't go into detail, it's just there for you to browse. In summary:\n",
        "\n",
        "- Get the directory where to save the model artifacts from the command line (`--model_dir`), and if not specified, then from the environment variable `AIP_MODEL_DIR`.\n",
        "- Loads CIFAR10 dataset from TF Datasets (tfds).\n",
        "- Builds a model using TF.Keras model API.\n",
        "- Compiles the model (`compile()`).\n",
        "- Sets a training distribution strategy according to the argument `args.distribute`.\n",
        "- Trains the model (`fit()`) with epochs and steps according to the arguments `args.epochs` and `args.steps`\n",
        "- Saves the trained model (`save(args.model_dir)`) to the specified model directory."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "taskpy_contents:cifar10"
      },
      "outputs": [],
      "source": [
        "%%writefile custom/trainer/task.py\n",
        "# Single, Mirror and Multi-Machine Distributed Training for CIFAR-10\n",
        "\n",
        "import tensorflow_datasets as tfds\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.client import device_lib\n",
        "import argparse\n",
        "import os\n",
        "import sys\n",
        "tfds.disable_progress_bar()\n",
        "\n",
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument('--model-dir', dest='model_dir',\n",
        "                    default=os.getenv(\"AIP_MODEL_DIR\"), type=str, help='Model dir.')\n",
        "parser.add_argument('--lr', dest='lr',\n",
        "                    default=0.01, type=float,\n",
        "                    help='Learning rate.')\n",
        "parser.add_argument('--epochs', dest='epochs',\n",
        "                    default=10, type=int,\n",
        "                    help='Number of epochs.')\n",
        "parser.add_argument('--steps', dest='steps',\n",
        "                    default=200, type=int,\n",
        "                    help='Number of steps per epoch.')\n",
        "parser.add_argument('--distribute', dest='distribute', type=str, default='single',\n",
        "                    help='distributed training strategy')\n",
        "args = parser.parse_args()\n",
        "\n",
        "print('Python Version = {}'.format(sys.version))\n",
        "print('TensorFlow Version = {}'.format(tf.__version__))\n",
        "print('TF_CONFIG = {}'.format(os.environ.get('TF_CONFIG', 'Not found')))\n",
        "print('DEVICES', device_lib.list_local_devices())\n",
        "\n",
        "# Single Machine, single compute device\n",
        "if args.distribute == 'single':\n",
        "    if tf.test.is_gpu_available():\n",
        "        strategy = tf.distribute.OneDeviceStrategy(device=\"/gpu:0\")\n",
        "    else:\n",
        "        strategy = tf.distribute.OneDeviceStrategy(device=\"/cpu:0\")\n",
        "# Single Machine, multiple compute device\n",
        "elif args.distribute == 'mirror':\n",
        "    strategy = tf.distribute.MirroredStrategy()\n",
        "# Multiple Machine, multiple compute device\n",
        "elif args.distribute == 'multi':\n",
        "    strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()\n",
        "\n",
        "# Multi-worker configuration\n",
        "print('num_replicas_in_sync = {}'.format(strategy.num_replicas_in_sync))\n",
        "\n",
        "# Preparing dataset\n",
        "BUFFER_SIZE = 10000\n",
        "BATCH_SIZE = 64\n",
        "\n",
        "\n",
        "def make_datasets_unbatched():\n",
        "\n",
        "  # Scaling CIFAR10 data from (0, 255] to (0., 1.]\n",
        "  def scale(image, label):\n",
        "    image = tf.cast(image, tf.float32)\n",
        "    image /= 255.0\n",
        "    return image, label\n",
        "\n",
        "\n",
        "  datasets, info = tfds.load(name='cifar10',\n",
        "                            with_info=True,\n",
        "                            as_supervised=True)\n",
        "  return datasets['train'].map(scale).cache().shuffle(BUFFER_SIZE).repeat()\n",
        "\n",
        "\n",
        "# Build the Keras model\n",
        "def build_and_compile_cnn_model():\n",
        "  model = tf.keras.Sequential([\n",
        "      tf.keras.layers.Conv2D(32, 3, activation='relu', input_shape=(32, 32, 3)),\n",
        "      tf.keras.layers.MaxPooling2D(),\n",
        "      tf.keras.layers.Conv2D(32, 3, activation='relu'),\n",
        "      tf.keras.layers.MaxPooling2D(),\n",
        "      tf.keras.layers.Flatten(),\n",
        "      tf.keras.layers.Dense(10, activation='softmax')\n",
        "  ])\n",
        "  model.compile(\n",
        "      loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
        "      optimizer=tf.keras.optimizers.SGD(learning_rate=args.lr),\n",
        "      metrics=['accuracy'])\n",
        "  return model\n",
        "\n",
        "\n",
        "# Train the model\n",
        "NUM_WORKERS = strategy.num_replicas_in_sync\n",
        "# Here the batch size scales up by number of workers since\n",
        "# `tf.data.Dataset.batch` expects the global batch size.\n",
        "GLOBAL_BATCH_SIZE = BATCH_SIZE * NUM_WORKERS\n",
        "train_dataset = make_datasets_unbatched().batch(GLOBAL_BATCH_SIZE)\n",
        "\n",
        "with strategy.scope():\n",
        "  # Creation of dataset, and model building/compiling need to be within\n",
        "  # `strategy.scope()`.\n",
        "  model = build_and_compile_cnn_model()\n",
        "\n",
        "model.fit(x=train_dataset, epochs=args.epochs, steps_per_epoch=args.steps)\n",
        "model.save(args.model_dir)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tarball_training_script"
      },
      "source": [
        "#### Store training script on your Cloud Storage bucket\n",
        "\n",
        "Next, you package the training folder into a compressed tar ball, and then store it in your Cloud Storage bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tarball_training_script"
      },
      "outputs": [],
      "source": [
        "! rm -f custom.tar custom.tar.gz\n",
        "! tar cvf custom.tar custom\n",
        "! gzip custom.tar\n",
        "! gsutil cp custom.tar.gz $BUCKET_URI/trainer_cifar10.tar.gz"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "train_a_model:migration"
      },
      "source": [
        "## Train a model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "custom_create:migration,new,mbsdk,prebuilt"
      },
      "source": [
        "### [training.create-python-pre-built-container](https://cloud.google.com/vertex-ai/docs/training/create-python-pre-built-container)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "create_custom_training_job:mbsdk,no_model"
      },
      "source": [
        "### Create and run custom training job\n",
        "\n",
        "\n",
        "To train a custom model, you perform two steps: 1) create a custom training job, and 2) run the job.\n",
        "\n",
        "#### Create custom training job\n",
        "\n",
        "A custom training job is created with the `CustomTrainingJob` class, with the following parameters:\n",
        "\n",
        "- `display_name`: The human readable name for the custom training job.\n",
        "- `container_uri`: The training container image.\n",
        "- `requirements`: Package requirements for the training container image (e.g., pandas).\n",
        "- `script_path`: The relative path to the training script."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "create_custom_training_job:mbsdk,no_model"
      },
      "outputs": [],
      "source": [
        "job = aip.CustomTrainingJob(\n",
        "    display_name=\"cifar10_\" + UUID,\n",
        "    script_path=\"custom/trainer/task.py\",\n",
        "    container_uri=TRAIN_IMAGE,\n",
        "    requirements=[\"gcsfs==0.7.1\", \"tensorflow-datasets==4.4\"],\n",
        ")\n",
        "\n",
        "print(job)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "create_custom_training_job:mbsdk,no_model"
      },
      "source": [
        "*Example output:*\n",
        "\n",
        "    <google.cloud.aiplatform.training_jobs.CustomTrainingJob object at 0x7feab1346710>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "run_custom_job:mbsdk,no_model"
      },
      "source": [
        "#### Run the custom training job\n",
        "\n",
        "Next, you run the custom job to start the training job by invoking the method `run`, with the following parameters:\n",
        "\n",
        "- `args`: The command-line arguments to pass to the training script.\n",
        "- `replica_count`: The number of compute instances for training (replica_count = 1 is single node training).\n",
        "- `machine_type`: The machine type for the compute instances.\n",
        "- `accelerator_type`: The hardware accelerator type.\n",
        "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
        "- `base_output_dir`: The Cloud Storage location to write the model artifacts to.\n",
        "- `sync`: Whether to block until completion of the job."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "run_custom_job:mbsdk,no_model"
      },
      "outputs": [],
      "source": [
        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, UUID)\n",
        "\n",
        "EPOCHS = 20\n",
        "STEPS = 100\n",
        "\n",
        "DIRECT = True\n",
        "if DIRECT:\n",
        "    CMDARGS = [\n",
        "        \"--model-dir=\" + MODEL_DIR,\n",
        "        \"--epochs=\" + str(EPOCHS),\n",
        "        \"--steps=\" + str(STEPS),\n",
        "    ]\n",
        "else:\n",
        "    CMDARGS = [\n",
        "        \"--epochs=\" + str(EPOCHS),\n",
        "        \"--steps=\" + str(STEPS),\n",
        "    ]\n",
        "\n",
        "if TRAIN_GPU:\n",
        "    job.run(\n",
        "        args=CMDARGS,\n",
        "        replica_count=1,\n",
        "        machine_type=TRAIN_COMPUTE,\n",
        "        accelerator_type=TRAIN_GPU.name,\n",
        "        accelerator_count=TRAIN_NGPU,\n",
        "        base_output_dir=MODEL_DIR,\n",
        "        sync=True,\n",
        "    )\n",
        "else:\n",
        "    job.run(\n",
        "        args=CMDARGS,\n",
        "        replica_count=1,\n",
        "        machine_type=TRAIN_COMPUTE,\n",
        "        base_output_dir=MODEL_DIR,\n",
        "        sync=True,\n",
        "    )\n",
        "\n",
        "model_path_to_deploy = MODEL_DIR"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "run_custom_job:mbsdk,no_model"
      },
      "source": [
        "### Wait for completion of custom training job\n",
        "\n",
        "Next, wait for the custom training job to complete. Alternatively, one can set the parameter `sync` to `True` in the `run()` methid to block until the custom training job is completed."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evaluate_the_model:migration"
      },
      "source": [
        "## Evaluate the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "load_saved_model"
      },
      "source": [
        "## Load the saved model\n",
        "\n",
        "Your model is stored in a TensorFlow SavedModel format in a Cloud Storage bucket. Now load it from the Cloud Storage bucket, and then you can do some things, like evaluate the model, and do a prediction.\n",
        "\n",
        "To load, you use the TF.Keras `model.load_model()` method passing it the Cloud Storage path where the model is saved -- specified by `MODEL_DIR`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "load_saved_model"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "local_model = tf.keras.models.load_model(MODEL_DIR)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "evaluate_custom_model:image"
      },
      "source": [
        "## Evaluate the model\n",
        "\n",
        "Now find out how good the model is.\n",
        "\n",
        "### Load evaluation data\n",
        "\n",
        "You will load the CIFAR10 test (holdout) data from `tf.keras.datasets`, using the method `load_data()`. This returns the dataset as a tuple of two elements. The first element is the training data and the second is the test data. Each element is also a tuple of two elements: the image data, and the corresponding labels.\n",
        "\n",
        "You don't need the training data, and hence why we loaded it as `(_, _)`.\n",
        "\n",
        "Before you can run the data through evaluation, you need to preprocess it:\n",
        "\n",
        "`x_test`:\n",
        "1. Normalize (rescale) the pixel data by dividing each pixel by 255. This replaces each single byte integer pixel with a 32-bit floating point number between 0 and 1.\n",
        "\n",
        "`y_test`:<br/>\n",
        "2. The labels are currently scalar (sparse). If you look back at the `compile()` step in the `trainer/task.py` script, you will find that it was compiled for sparse labels. So we don't need to do anything more."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "evaluate_custom_model:image,cifar10"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from tensorflow.keras.datasets import cifar10\n",
        "\n",
        "(_, _), (x_test, y_test) = cifar10.load_data()\n",
        "x_test = (x_test / 255.0).astype(np.float32)\n",
        "\n",
        "print(x_test.shape, y_test.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "perform_evaluation_custom"
      },
      "source": [
        "### Perform the model evaluation\n",
        "\n",
        "Now evaluate how well the model in the custom job did."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "perform_evaluation_custom"
      },
      "outputs": [],
      "source": [
        "local_model.evaluate(x_test, y_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "import_model:migration,new"
      },
      "source": [
        "### [general.import-model](https://cloud.google.com/vertex-ai/docs/general/import-model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "serving_function_image"
      },
      "source": [
        "### Serving function for image data\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 before it is passed as input to the deployed model.\n",
        "\n",
        "To resolve this, 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",
        "- `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.\n",
        "- `image.resize` - Resizes the image to match the input shape for the model.\n",
        "- `resized / 255.0` - Rescales (normalization) the pixel data between 0 and 1.\n",
        "\n",
        "At this point, the data can be passed to the model (`m_call`)."
      ]
    },
    {
      "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=(32, 32))\n",
        "    rescale = tf.cast(resized / 255.0, tf.float32)\n",
        "    return rescale\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(local_model.call).get_concrete_function(\n",
        "    [tf.TensorSpec(shape=[None, 32, 32, 3], dtype=tf.float32, name=CONCRETE_INPUT)]\n",
        ")\n",
        "\n",
        "tf.saved_model.save(\n",
        "    local_model, model_path_to_deploy, signatures={\"serving_default\": serving_fn}\n",
        ")"
      ]
    },
    {
      "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_path_to_deploy)\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": "upload_model:mbsdk"
      },
      "source": [
        "## Upload the model\n",
        "\n",
        "Next, upload your model to a `Model` resource using `Model.upload()` method, with the following parameters:\n",
        "\n",
        "- `display_name`: The human readable name for the `Model` resource.\n",
        "- `artifact`: The Cloud Storage location of the trained model artifacts.\n",
        "- `serving_container_image_uri`: The serving container image.\n",
        "- `sync`: Whether to execute the upload asynchronously or synchronously.\n",
        "\n",
        "If the `upload()` method is run asynchronously, you can subsequently block until completion with the `wait()` method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "upload_model:mbsdk"
      },
      "outputs": [],
      "source": [
        "model = aip.Model.upload(\n",
        "    display_name=\"cifar10_\" + UUID,\n",
        "    artifact_uri=MODEL_DIR,\n",
        "    serving_container_image_uri=DEPLOY_IMAGE,\n",
        "    sync=False,\n",
        ")\n",
        "\n",
        "model.wait()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "upload_model:mbsdk"
      },
      "source": [
        "*Example output:*\n",
        "\n",
        "    INFO:google.cloud.aiplatform.models:Creating Model\n",
        "    INFO:google.cloud.aiplatform.models:Create Model backing LRO: projects/759209241365/locations/us-central1/models/925164267982815232/operations/3458372263047331840\n",
        "    INFO:google.cloud.aiplatform.models:Model created. Resource name: projects/759209241365/locations/us-central1/models/925164267982815232\n",
        "    INFO:google.cloud.aiplatform.models:To use this Model in another session:\n",
        "    INFO:google.cloud.aiplatform.models:model = aiplatform.Model('projects/759209241365/locations/us-central1/models/925164267982815232')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "make_batch_predictions:migration"
      },
      "source": [
        "## Make batch predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batchpredictionjobs_create:migration,new,mbsdk"
      },
      "source": [
        "### [predictions.batch-prediction](https://cloud.google.com/vertex-ai/docs/predictions/batch-predictions)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "get_test_items:test"
      },
      "source": [
        "### Get test items\n",
        "\n",
        "You will use examples out of the test (holdout) portion of the dataset as a test items."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "get_test_items:test"
      },
      "outputs": [],
      "source": [
        "test_image_1 = x_test[0]\n",
        "test_label_1 = y_test[0]\n",
        "test_image_2 = x_test[1]\n",
        "test_label_2 = y_test[1]\n",
        "print(test_image_1.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prepare_test_items:test,image"
      },
      "source": [
        "### Prepare the request content\n",
        "You are going to send the CIFAR10 images as compressed JPG image, instead of the raw uncompressed bytes:\n",
        "\n",
        "- `cv2.imwrite`: Use openCV to write the uncompressed image to disk as a compressed JPEG image.\n",
        " - Denormalize the image data from \\[0,1) range back to [0,255).\n",
        " - Convert the 32-bit floating point values to 8-bit unsigned integers."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "prepare_test_items:test,image"
      },
      "outputs": [],
      "source": [
        "import cv2\n",
        "\n",
        "cv2.imwrite(\"tmp1.jpg\", (test_image_1 * 255).astype(np.uint8))\n",
        "cv2.imwrite(\"tmp2.jpg\", (test_image_2 * 255).astype(np.uint8))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "copy_test_items:test"
      },
      "source": [
        "### Copy test item(s)\n",
        "\n",
        "For the batch prediction, copy the test items over to your Cloud Storage bucket."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "copy_test_items:test"
      },
      "outputs": [],
      "source": [
        "! gsutil cp tmp1.jpg $BUCKET_URI/tmp1.jpg\n",
        "! gsutil cp tmp2.jpg $BUCKET_URI/tmp2.jpg\n",
        "\n",
        "test_item_1 = BUCKET_URI + \"/tmp1.jpg\"\n",
        "test_item_2 = BUCKET_URI + \"/tmp2.jpg\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "make_batch_file:custom,image"
      },
      "source": [
        "### Make the batch input file\n",
        "\n",
        "Now make a batch input file, which you will store in your local Cloud Storage bucket. The batch input file can only be in JSONL format. For JSONL file, you make one dictionary entry per line for each data item (instance). The dictionary contains the key/value pairs:\n",
        "\n",
        "- `input_name`: the name of the input layer of the underlying model.\n",
        "- `'b64'`: A key that indicates the content is base64 encoded.\n",
        "- `content`: The compressed JPG image bytes as a base64 encoded string.\n",
        "\n",
        "Each instance in the prediction request is a dictionary entry of the form:\n",
        "\n",
        "                        {serving_input: {'b64': content}}\n",
        "\n",
        "To pass the image data to the prediction service you encode the bytes into base64 -- which makes the content safe from modification when transmitting binary data over the network.\n",
        "\n",
        "- `tf.io.read_file`: Read the compressed JPG images into memory as raw bytes.\n",
        "- `base64.b64encode`: Encode the raw bytes into a base64 encoded string."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "make_batch_file:custom,image"
      },
      "outputs": [],
      "source": [
        "import base64\n",
        "import json\n",
        "\n",
        "gcs_input_uri = BUCKET_URI + \"/\" + \"test.jsonl\"\n",
        "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
        "    bytes = tf.io.read_file(test_item_1)\n",
        "    b64str = base64.b64encode(bytes.numpy()).decode(\"utf-8\")\n",
        "    data = {serving_input: {\"b64\": b64str}}\n",
        "    f.write(json.dumps(data) + \"\\n\")\n",
        "    bytes = tf.io.read_file(test_item_2)\n",
        "    b64str = base64.b64encode(bytes.numpy()).decode(\"utf-8\")\n",
        "    data = {serving_input: {\"b64\": b64str}}\n",
        "    f.write(json.dumps(data) + \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batch_request:mbsdk,jsonl,custom"
      },
      "source": [
        "### Make the batch prediction request\n",
        "\n",
        "Now that your Model resource is trained, you can make a batch prediction by invoking the batch_predict() method, with the following parameters:\n",
        "\n",
        "- `job_display_name`: The human readable name for the batch prediction job.\n",
        "- `gcs_source`: A list of one or more batch request input files.\n",
        "- `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
        "- `instances_format`: The format for the input instances, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
        "- `predictions_format`: The format for the output predictions, either 'csv' or 'jsonl'. Defaults to 'jsonl'.\n",
        "- `machine_type`: The type of machine to use for training.\n",
        "- `accelerator_type`: The hardware accelerator type.\n",
        "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
        "- `sync`: If set to True, the call will block while waiting for the asynchronous batch job to complete."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "batch_request:mbsdk,jsonl,custom"
      },
      "outputs": [],
      "source": [
        "MIN_NODES = 1\n",
        "MAX_NODES = 1\n",
        "\n",
        "batch_predict_job = model.batch_predict(\n",
        "    job_display_name=\"cifar10_\" + UUID,\n",
        "    gcs_source=gcs_input_uri,\n",
        "    gcs_destination_prefix=BUCKET_URI,\n",
        "    instances_format=\"jsonl\",\n",
        "    predictions_format=\"jsonl\",\n",
        "    model_parameters=None,\n",
        "    machine_type=DEPLOY_COMPUTE,\n",
        "    accelerator_type=DEPLOY_GPU,\n",
        "    accelerator_count=DEPLOY_NGPU,\n",
        "    starting_replica_count=MIN_NODES,\n",
        "    max_replica_count=MAX_NODES,\n",
        "    sync=False,\n",
        ")\n",
        "\n",
        "print(batch_predict_job)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batch_request:mbsdk,jsonl,custom"
      },
      "source": [
        "*Example output:*\n",
        "\n",
        "    INFO:google.cloud.aiplatform.jobs:Creating BatchPredictionJob\n",
        "    <google.cloud.aiplatform.jobs.BatchPredictionJob object at 0x7f806a6112d0> is waiting for upstream dependencies to complete.\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob created. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296\n",
        "    INFO:google.cloud.aiplatform.jobs:To use this BatchPredictionJob in another session:\n",
        "    INFO:google.cloud.aiplatform.jobs:bpj = aiplatform.BatchPredictionJob('projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296')\n",
        "    INFO:google.cloud.aiplatform.jobs:View Batch Prediction Job:\n",
        "    https://console.cloud.google.com/ai/platform/locations/us-central1/batch-predictions/5110965452507447296?project=759209241365\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/5110965452507447296 current state:\n",
        "    JobState.JOB_STATE_RUNNING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batch_request_wait:mbsdk"
      },
      "source": [
        "### Wait for completion of batch prediction job\n",
        "\n",
        "Next, wait for the batch job to complete. Alternatively, one can set the parameter `sync` to `True` in the `batch_predict()` method to block until the batch prediction job is completed."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "batch_request_wait:mbsdk"
      },
      "outputs": [],
      "source": [
        "batch_predict_job.wait()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "batch_request_wait:mbsdk"
      },
      "source": [
        "*Example Output:*\n",
        "\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob created. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328\n",
        "    INFO:google.cloud.aiplatform.jobs:To use this BatchPredictionJob in another session:\n",
        "    INFO:google.cloud.aiplatform.jobs:bpj = aiplatform.BatchPredictionJob('projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328')\n",
        "    INFO:google.cloud.aiplatform.jobs:View Batch Prediction Job:\n",
        "    https://console.cloud.google.com/ai/platform/locations/us-central1/batch-predictions/181835033978339328?project=759209241365\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_RUNNING\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328 current state:\n",
        "    JobState.JOB_STATE_SUCCEEDED\n",
        "    INFO:google.cloud.aiplatform.jobs:BatchPredictionJob run completed. Resource name: projects/759209241365/locations/us-central1/batchPredictionJobs/181835033978339328"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "get_batch_prediction:mbsdk,custom,icn"
      },
      "source": [
        "### Get the predictions\n",
        "\n",
        "Next, get the results from the completed batch prediction job.\n",
        "\n",
        "The results are written to the Cloud Storage output bucket you specified in the batch prediction request. You call the method iter_outputs() to get a list of each Cloud Storage file generated with the results. Each file contains one or more prediction requests in a JSON format:\n",
        "\n",
        "- `instance`: The prediction request.\n",
        "- `prediction`: The prediction response."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "get_batch_prediction:mbsdk,custom,icn"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "\n",
        "bp_iter_outputs = batch_predict_job.iter_outputs()\n",
        "\n",
        "prediction_results = list()\n",
        "for blob in bp_iter_outputs:\n",
        "    if blob.name.split(\"/\")[-1].startswith(\"prediction\"):\n",
        "        prediction_results.append(blob.name)\n",
        "\n",
        "tags = list()\n",
        "for prediction_result in prediction_results:\n",
        "    gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{prediction_result}\"\n",
        "    with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
        "        for line in gfile.readlines():\n",
        "            line = json.loads(line)\n",
        "            print(line)\n",
        "            break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "get_batch_prediction:mbsdk,custom,icn"
      },
      "source": [
        "*Example Output:*\n",
        "\n",
        "    {'instance': {'bytes_inputs': {'b64': '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'}}, 'prediction': [0.0560616329, 0.122713037, 0.121289924, 0.109751239, 0.121320881, 0.0897410363, 0.145011798, 0.0976110101, 0.0394041203, 0.0970953554]}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "make_online_predictions:migration"
      },
      "source": [
        "## Make online predictions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deploy_model:migration,new,mbsdk"
      },
      "source": [
        "### [predictions.deploy-model-api](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deploy_model:mbsdk,all"
      },
      "source": [
        "## Deploy the model\n",
        "\n",
        "Next, deploy your model for online prediction. To deploy the model, you invoke the `deploy` method, with the following parameters:\n",
        "\n",
        "- `deployed_model_display_name`: A human readable name for the deployed model.\n",
        "- `traffic_split`: Percent of traffic at the endpoint that goes to this model, which is specified as a dictionary of one or more key/value pairs.\n",
        "If only one model, then specify as { \"0\": 100 }, where \"0\" refers to this model being uploaded and 100 means 100% of the traffic.\n",
        "If there are existing models on the endpoint, for which the traffic will be split, then use model_id to specify as { \"0\": percent, model_id: percent, ... }, where model_id is the model id of an existing model to the deployed endpoint. The percents must add up to 100.\n",
        "- `machine_type`: The type of machine to use for training.\n",
        "- `accelerator_type`: The hardware accelerator type.\n",
        "- `accelerator_count`: The number of accelerators to attach to a worker replica.\n",
        "- `starting_replica_count`: The number of compute instances to initially provision.\n",
        "- `max_replica_count`: The maximum number of compute instances to scale to. In this tutorial, only one instance is provisioned."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "deploy_model:mbsdk,all"
      },
      "outputs": [],
      "source": [
        "DEPLOYED_NAME = \"cifar10-\" + UUID\n",
        "\n",
        "TRAFFIC_SPLIT = {\"0\": 100}\n",
        "\n",
        "MIN_NODES = 1\n",
        "MAX_NODES = 1\n",
        "\n",
        "if DEPLOY_GPU:\n",
        "    endpoint = model.deploy(\n",
        "        deployed_model_display_name=DEPLOYED_NAME,\n",
        "        traffic_split=TRAFFIC_SPLIT,\n",
        "        machine_type=DEPLOY_COMPUTE,\n",
        "        accelerator_type=DEPLOY_GPU,\n",
        "        accelerator_count=DEPLOY_NGPU,\n",
        "        min_replica_count=MIN_NODES,\n",
        "        max_replica_count=MAX_NODES,\n",
        "    )\n",
        "else:\n",
        "    endpoint = model.deploy(\n",
        "        deployed_model_display_name=DEPLOYED_NAME,\n",
        "        traffic_split=TRAFFIC_SPLIT,\n",
        "        machine_type=DEPLOY_COMPUTE,\n",
        "        accelerator_type=DEPLOY_GPU,\n",
        "        accelerator_count=0,\n",
        "        min_replica_count=MIN_NODES,\n",
        "        max_replica_count=MAX_NODES,\n",
        "    )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "deploy_model:mbsdk,all"
      },
      "source": [
        "*Example output:*\n",
        "\n",
        "    INFO:google.cloud.aiplatform.models:Creating Endpoint\n",
        "    INFO:google.cloud.aiplatform.models:Create Endpoint backing LRO: projects/759209241365/locations/us-central1/endpoints/4867177336350441472/operations/4087251132693348352\n",
        "    INFO:google.cloud.aiplatform.models:Endpoint created. Resource name: projects/759209241365/locations/us-central1/endpoints/4867177336350441472\n",
        "    INFO:google.cloud.aiplatform.models:To use this Endpoint in another session:\n",
        "    INFO:google.cloud.aiplatform.models:endpoint = aiplatform.Endpoint('projects/759209241365/locations/us-central1/endpoints/4867177336350441472')\n",
        "    INFO:google.cloud.aiplatform.models:Deploying model to Endpoint : projects/759209241365/locations/us-central1/endpoints/4867177336350441472\n",
        "    INFO:google.cloud.aiplatform.models:Deploy Endpoint model backing LRO: projects/759209241365/locations/us-central1/endpoints/4867177336350441472/operations/1691336130932244480\n",
        "    INFO:google.cloud.aiplatform.models:Endpoint model deployed. Resource name: projects/759209241365/locations/us-central1/endpoints/4867177336350441472"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "endpoints_predict:migration,new,mbsdk"
      },
      "source": [
        "### [predictions.online-prediction-automl](https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-automl)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "get_test_item:test"
      },
      "source": [
        "### Get test item\n",
        "\n",
        "You will use an example out of the test (holdout) portion of the dataset as a test item."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "get_test_item:test"
      },
      "outputs": [],
      "source": [
        "test_image = x_test[0]\n",
        "test_label = y_test[0]\n",
        "print(test_image.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "prepare_test_item:test,image"
      },
      "source": [
        "### Prepare the request content\n",
        "You are going to send the CIFAR10 image as compressed JPG image, instead of the raw uncompressed bytes:\n",
        "\n",
        "- `cv2.imwrite`: Use openCV to write the uncompressed image to disk as a compressed JPEG image.\n",
        " - Denormalize the image data from \\[0,1) range back to [0,255).\n",
        " - Convert the 32-bit floating point values to 8-bit unsigned integers.\n",
        "- `tf.io.read_file`: Read the compressed JPG images back into memory as raw bytes.\n",
        "- `base64.b64encode`: Encode the raw bytes into a base 64 encoded string."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "prepare_test_item:test,image"
      },
      "outputs": [],
      "source": [
        "import base64\n",
        "\n",
        "import cv2\n",
        "\n",
        "cv2.imwrite(\"tmp.jpg\", (test_image * 255).astype(np.uint8))\n",
        "\n",
        "bytes = tf.io.read_file(\"tmp.jpg\")\n",
        "b64str = base64.b64encode(bytes.numpy()).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": "predict_request:mbsdk,custom,icn"
      },
      "source": [
        "*Example output:*\n",
        "\n",
        "    Prediction(predictions=[[0.0560616292, 0.122713044, 0.121289924, 0.109751239, 0.121320873, 0.0897410288, 0.145011798, 0.0976110175, 0.0394041166, 0.0970953479]], deployed_model_id='4087166195420102656', explanations=None)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "undeploy_model:mbsdk"
      },
      "source": [
        "## Undeploy the model\n",
        "\n",
        "When you are done doing predictions, you undeploy the model from the `Endpoint` resouce. This deprovisions all compute resources and ends billing for the deployed model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "undeploy_model:mbsdk"
      },
      "outputs": [],
      "source": [
        "endpoint.undeploy_all()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cleanup:mbsdk"
      },
      "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": "cleanup:mbsdk"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "delete_bucket = False\n",
        "\n",
        "# Delete the model using the Vertex model object\n",
        "model.delete()\n",
        "\n",
        "# Delete the endpoint using the Vertex endpoint object\n",
        "endpoint.delete()\n",
        "\n",
        "# Delete the training job\n",
        "job.delete()\n",
        "\n",
        "# Delete the batch prediction job using the Vertex batch prediction object\n",
        "batch_predict_job.delete()\n",
        "\n",
        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
        "    ! gsutil rm -r $BUCKET_URI"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "sdk-custom-image-classification-prebuilt-container.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}