notebooks/community/model_garden/model_garden_proprietary_image_classification.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ur8xi4C7S06n"
      },
      "outputs": [],
      "source": [
        "# Copyright 2023 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": "TirJ-SGQseby"
      },
      "source": [
        "# Vertex AI Model Garden: Google Proprietary Model Image Classification\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_proprietary_image_classification.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",
        "\n",
        "  <td>\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_proprietary_image_classification.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/notebooks/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/model_garden/model_garden_proprietary_image_classification.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>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwGLvtIeECLK"
      },
      "source": [
        "**_NOTE_**: This notebook has been tested in the following environment:\n",
        "\n",
        "* Python version = 3.9"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook demonstrates how to use Google proprietary image classification model training/deployment in [Vertex AI Model Garden](https://cloud.google.com/model-garden).\n",
        "\n",
        "### Objective\n",
        "\n",
        "* Train new models using Vertex SDK\n",
        "\n",
        "* Test trained models\n",
        "  * View the trained model in [Vertex Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/introduction)\n",
        "  * Deploy uploaded models\n",
        "  * Run predictions\n",
        "\n",
        "* Cleanup resources\n",
        "\n",
        "### 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.\n",
        "\n",
        "### Dataset\n",
        "\n",
        "The dataset used for this tutorial is the [Flowers dataset](https://www.tensorflow.org/datasets/catalog/tf_flowers) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the type of flower an image is from a class of five flowers: daisy, dandelion, rose, sunflower, or tulip."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEukV6uRk_S3"
      },
      "source": [
        "## Before you begin"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Jvqs-ehKlaYh"
      },
      "outputs": [],
      "source": [
        "! pip3 install --upgrade google-cloud-aiplatform\n",
        "\n",
        "# Automatically restart kernel after installs\n",
        "import IPython\n",
        "\n",
        "app = IPython.Application.instance()\n",
        "app.kernel.do_shutdown(True)\n",
        "if \"google.colab\" in str(get_ipython()):\n",
        "    from google.colab import auth as google_auth\n",
        "\n",
        "    google_auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BF1j6f9HApxa"
      },
      "source": [
        "### 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 $300 free credit towards your compute/storage costs.\n",
        "\n",
        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
        "\n",
        "1. [Enable the Vertex AI API and Compute Engine API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component).\n",
        "\n",
        "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
        "\n",
        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
        "Cloud SDK uses the right project for all the commands in this notebook.\n",
        "\n",
        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9wExiMUxFk91"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "from google.cloud import aiplatform\n",
        "\n",
        "# The project and bucket are for experiments below.\n",
        "PROJECT_ID = \"\"  # @param {type:\"string\"}\n",
        "BUCKET_URI = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "# You can choose a region from https://cloud.google.com/about/locations.\n",
        "# Only regions prefixed by \"us\", \"europe\", or \"asia\" are supported.\n",
        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
        "REGION_PREFIX = REGION.split(\"-\")[0]\n",
        "assert REGION_PREFIX in (\n",
        "    \"us\",\n",
        "    \"europe\",\n",
        "    \"asia\",\n",
        "), f'{REGION} is not supported. It must be prefixed by \"us\", \"europe\", or \"asia\".'\n",
        "\n",
        "! gcloud config set project $PROJECT_ID\n",
        "\n",
        "STAGING_BUCKET = os.path.join(BUCKET_URI, \"temporal\")\n",
        "\n",
        "aiplatform.init(project=PROJECT_ID, location=REGION, staging_bucket=STAGING_BUCKET)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n6IFz75WGCam"
      },
      "source": [
        "### Define constants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "riG_qUokg0XZ"
      },
      "outputs": [],
      "source": [
        "OBJECTIVE = \"icn\"\n",
        "\n",
        "# Dataset constants\n",
        "DATASET_PREFIX = \"dataset-icn\"\n",
        "\n",
        "# Training constants.\n",
        "TRAINING_JOB_PREFIX = \"train\"\n",
        "\n",
        "# The image classification flowers dataset used to train the model.\n",
        "DATASET_FILE = (\n",
        "    \"gs://cloud-samples-data/vision/automl_classification/flowers/all_data_v2.csv\"\n",
        ")\n",
        "\n",
        "# Evaluation constants.\n",
        "EVALUATION_METRIC = \"accuracy\"\n",
        "\n",
        "DEPLOY_JOB_PREFIX = \"deploy\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZZFPe_GezXg8"
      },
      "source": [
        "### Define common libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcYUGwr-AJGY"
      },
      "outputs": [],
      "source": [
        "import base64\n",
        "from datetime import datetime\n",
        "from io import BytesIO\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy\n",
        "import tensorflow as tf\n",
        "from PIL import Image\n",
        "\n",
        "\n",
        "def get_job_name_with_datetime(prefix: str):\n",
        "    return prefix + datetime.now().strftime(\"_%Y%m%d_%H%M%S\")\n",
        "\n",
        "\n",
        "def load_img(path):\n",
        "    img = tf.io.read_file(path)\n",
        "    img = tf.image.decode_jpeg(img, channels=3)\n",
        "    return Image.fromarray(numpy.uint8(img)).convert(\"RGB\")\n",
        "\n",
        "\n",
        "def display_image(image):\n",
        "    _ = plt.figure(figsize=(20, 15))\n",
        "    plt.grid(False)\n",
        "    plt.imshow(image)\n",
        "\n",
        "\n",
        "def get_prediction_instances(test_filepath, new_width=-1):\n",
        "    if new_width <= 0:\n",
        "        with tf.io.gfile.GFile(test_filepath, \"rb\") as input_file:\n",
        "            encoded_string = base64.b64encode(input_file.read()).decode(\"utf-8\")\n",
        "    else:\n",
        "        img = load_img(test_filepath)\n",
        "        width, height = img.size\n",
        "        print(\"original input image size: \", width, \" , \", height)\n",
        "        new_height = int(height * new_width / width)\n",
        "        new_img = img.resize((new_width, new_height))\n",
        "        print(\"resized input image size: \", new_width, \" , \", new_height)\n",
        "        buffered = BytesIO()\n",
        "        new_img.save(buffered, format=\"JPEG\")\n",
        "        encoded_string = base64.b64encode(buffered.getvalue()).decode(\"utf-8\")\n",
        "\n",
        "    instances = [{\"content\": encoded_string}]\n",
        "    return instances"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q149N3V6Uynm"
      },
      "source": [
        "## Create a dataset\n",
        "\n",
        "This tutorial uses a version of the Flowers dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
        "\n",
        "Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file  (`wc -l`) and then peek at the first few rows."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yR4x-5XiVIlv"
      },
      "outputs": [],
      "source": [
        "count = ! gsutil cat $DATASET_FILE | wc -l\n",
        "print(\"Number of Examples\", int(count[0]))\n",
        "\n",
        "print(\"First 10 rows\")\n",
        "! gsutil cat $DATASET_FILE | head"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8yfBZ1_8VZvq"
      },
      "source": [
        "Next, create the `Dataset` resource using the `create` method for the `ImageDataset` class, which takes the following parameters:\n",
        "\n",
        "- `display_name`: The human readable name for the `Dataset` resource.\n",
        "- `gcs_source`: A list of one or more dataset index files to import the data items into the `Dataset` resource.\n",
        "- `import_schema_uri`: The data labeling schema for the data items.\n",
        "\n",
        "This operation may take several minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "eehB1gwHVfRh"
      },
      "outputs": [],
      "source": [
        "dataset = aiplatform.ImageDataset.create(\n",
        "    display_name=DATASET_PREFIX + \"_flowers\",\n",
        "    gcs_source=[DATASET_FILE],\n",
        "    import_schema_uri=aiplatform.schema.dataset.ioformat.image.single_label_classification,\n",
        ")\n",
        "\n",
        "print(dataset.resource_name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RB_xY9ipr7ZU"
      },
      "source": [
        "## Train new models\n",
        "\n",
        "### Create and run training pipeline\n",
        "\n",
        "To train an AutoML model, you perform two steps:\n",
        "1.  Create a training pipeline.\n",
        "2.  Run the pipeline.\n",
        "\n",
        "#### Create training pipeline\n",
        "\n",
        "An AutoML training pipeline is created with the `AutoMLImageTrainingJob` class, with the following parameters:\n",
        "\n",
        "- `display_name`: The human readable name for the `TrainingJob` resource.\n",
        "- `prediction_type`: The type task to train the model for.\n",
        "  - `classification`: An image classification model.\n",
        "  - `object_detection`: An image object detection model.\n",
        "- `multi_label`: If a classification task is single (`False`) or multi-labeled (`True`).\n",
        "- `model_type`: The type of model for deployment.\n",
        "  - `EFFICIENTNET`: A model that is available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.\n",
        "  - `MAXVIT`: A model that is available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.\n",
        "  - `VIT`: A model that is available in Vertex Model Garden image classification training with customizable hyperparameters. Best tailored to be used within Google Cloud, and cannot be exported externally.\n",
        "- `checkpoint_name`: Optional. The field is reserved for Model Garden model training, based on the provided pre-trained model checkpoint.\n",
        "- `trainer_config`: Optional. The field is usually used together with the Model Garden model training when passing the customized configs for the trainer.\n",
        "\n",
        "  Example with all supported parameters:\n",
        "```\n",
        "  trainer_config = {\n",
        "    'global_batch_size': '8',\n",
        "    'learning_rate': '0.001',\n",
        "    'optimizer_type': 'sgd',\n",
        "    'optimizer_momentum': '0.9',\n",
        "    'train_steps': '10000',\n",
        "    'accelerator_count': '1',\n",
        "  }\n",
        "```\n",
        "  The global_batch_size should be divisible by accelerator_count.\n",
        "  Supported values for optimizer_type are 'sgd', 'adam', 'adamw', 'lamb', 'rmsprop', 'lars', 'adagrad', and 'slide'.\n",
        "  Supported values for accelerator_count are '1', '2', '4', and '8'.\n",
        "- `metric_spec`: Dictionary representing metrics to optimize. The dictionary key is the `metric_id`, which is reported by your training job, with possible values being ('loss', 'accuracy') and the dictionary value is the optimization goal of the metric ('minimize' or 'maximize').\n",
        "For example:  `metric_spec = {'loss': 'minimize', 'accuracy': 'maximize'}`\n",
        "- `parameter_spec`: Dictionary representing parameters to optimize. The dictionary key is the `metric_id`, which is passed into your training job as a command line key word argument, and the dictionary value is the parameter\n",
        "specification of the metric. Supported parameter specifications can be found in aiplatform.hyperparameter_tuning.\n",
        "```\n",
        "  from google.cloud.aiplatform.aiplatform import hpt as hpt\n",
        "\n",
        "  parameter_spec = {\n",
        "    'learning_rate': hpt.DoubleParameterSpec(min=1e-7, max=1, scale='linear'),\n",
        "  }\n",
        "```\n",
        "- `search_algorithm`: The search algorithm specified for the Study. Accepts one of the following:\n",
        "  - `None`: If you do not specify an algorithm, your job uses the default\n",
        "  Vertex AI algorithm. The default algorithm applies Bayesian optimization\n",
        "  to arrive at the optimal solution with a more effective search over the\n",
        "  parameter space.\n",
        "  - `grid`: A simple grid search within the feasible space. This option is\n",
        "  particularly useful if you want to specify a quantity of trials that is greater than the number of points in the feasible space. In such cases, if you do not specify a grid search, the Vertex AI default algorithm may generate duplicate suggestions. To use grid search, all parameter specs must be of type `IntegerParameterSpec`, `CategoricalParameterSpec`, or `DiscreteParameterSpec`.\n",
        "  - `random`: A simple random search within the feasible space.\n",
        "- `measurement_selection`: This indicates which measurement to use\n",
        "if/when the service automatically selects the final measurement from\n",
        "previously reported intermediate measurements.\n",
        "  Accepts: `best`, `last` Choose this based on two considerations:\n",
        "    - A): Do you expect your measurements to monotonically improve? If so,\n",
        "    choose `last`. On the other hand, if you\\'re in a situation where\n",
        "    your system can **over-train** and you expect the performance to get\n",
        "    better for a while but then start declining, choose `best`.\n",
        "    - B): Are your measurements significantly noisy and/or irreproducible? If\n",
        "    so, `best` will tend to be over-optimistic, and it may be better\n",
        "    to choose `last`. If both or neither of (A) and (B) apply, it\n",
        "    doesn't matter which selection type is chosen.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "un0tyqU7We_A"
      },
      "outputs": [],
      "source": [
        "from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
        "\n",
        "TRAINER_CONFIG = {\n",
        "    \"global_batch_size\": \"8\",\n",
        "    \"learning_rate\": \"0.001\",\n",
        "    \"train_steps\": \"10000\",\n",
        "    \"accelerator_count\": \"1\",\n",
        "}\n",
        "METRIC_SPEC_KEY = \"accuracy\"\n",
        "METRIC_SPEC_VALUE = \"maximize\"\n",
        "SEARCH_ALGORITHM = \"random\"\n",
        "MEASUREMENT_SELECTION = \"best\"\n",
        "MODEL_TYPE = \"MAXVIT\"  # @param {type:\"string\"} one of the values [\"MAXVIT\", \"EFFICIENTNET\", \"VIT\"]\n",
        "\n",
        "job = aiplatform.AutoMLImageTrainingJob(\n",
        "    display_name=get_job_name_with_datetime(TRAINING_JOB_PREFIX),\n",
        "    prediction_type=\"classification\",\n",
        "    multi_label=False,\n",
        "    model_type=MODEL_TYPE,\n",
        "    base_model=None,\n",
        "    trainer_config=TRAINER_CONFIG,\n",
        "    metric_spec={METRIC_SPEC_KEY: METRIC_SPEC_VALUE},\n",
        "    parameter_spec={\n",
        "        \"learning_rate\": hpt.DoubleParameterSpec(min=0.001, max=0.1, scale=\"log\"),\n",
        "    },\n",
        "    search_algorithm=SEARCH_ALGORITHM,\n",
        "    measurement_selection=MEASUREMENT_SELECTION,\n",
        ")\n",
        "\n",
        "print(job)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HwcCjwlBTQIz"
      },
      "source": [
        "#### Run the training pipeline\n",
        "\n",
        "Next, run the DAG to start the training job by invoking the method `run`, with the following parameters:\n",
        "\n",
        "- `dataset`: The `Dataset` resource to train the model.\n",
        "- `model_display_name`: The human readable name for the trained model.\n",
        "- `training_fraction_split`: The percentage of the dataset to use for training.\n",
        "- `validation_fraction_split`: The percentage of the dataset to use for validation.\n",
        "- `test_fraction_split`: The percentage of the dataset to use for test (holdout data).\n",
        "- `budget_milli_node_hours`: (optional) Maximum training time specified in unit of millihours (1000 = hour).\n",
        "- `disable_early_stopping`: If `True`, training may be completed before using the entire budget if the service believes it cannot further improve on the model objective measurements.\n",
        "\n",
        "The `run` method, when completed, returns the `Model` resource.\n",
        "\n",
        "The execution of the training pipeline will take up to 60 minutes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aec22792ee84"
      },
      "outputs": [],
      "source": [
        "model = job.run(\n",
        "    dataset=dataset,\n",
        "    model_display_name=get_job_name_with_datetime(\"flowers\"),\n",
        "    training_fraction_split=0.8,\n",
        "    validation_fraction_split=0.1,\n",
        "    test_fraction_split=0.1,\n",
        "    budget_milli_node_hours=8000,\n",
        "    disable_early_stopping=False,\n",
        ")\n",
        "\n",
        "print(\"Model is: \", model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g0BGaofgsMsy"
      },
      "source": [
        "## Test trained models\n",
        "This section shows how to test the trained models.\n",
        "1. Deploy model from Model Registry\n",
        "2. Run online predictions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NYuQowyZEtxK"
      },
      "outputs": [],
      "source": [
        "# @title Deploy model from Model Registry\n",
        "# Model does not support dedicated deployment resources.\n",
        "# An n1-standard-4 machine with 1 P100 GPU will be used.\n",
        "\n",
        "deploy_model_name = get_job_name_with_datetime(DEPLOY_JOB_PREFIX + \"_\" + OBJECTIVE)\n",
        "print(\"The deployed job name is: \", deploy_model_name)\n",
        "\n",
        "endpoint = model.deploy(\n",
        "    deployed_model_display_name=deploy_model_name,\n",
        "    traffic_split={\"0\": 100},\n",
        "    min_replica_count=1,\n",
        "    max_replica_count=1,\n",
        ")\n",
        "\n",
        "endpoint_id = endpoint.name\n",
        "print(\"endpoint id is: \", endpoint_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vbIW9me1F2RY"
      },
      "outputs": [],
      "source": [
        "# @title Run online predictions\n",
        "\n",
        "# test image file path from a Cloud Storage bucket\n",
        "test_filepath = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "with tf.io.gfile.GFile(test_filepath, \"rb\") as f:\n",
        "    content = f.read()\n",
        "\n",
        "# The format of each instance should conform to the deployed model's prediction input schema.\n",
        "instances = [{\"content\": base64.b64encode(content).decode(\"utf-8\")}]\n",
        "\n",
        "prediction = endpoint.predict(instances=instances)\n",
        "\n",
        "img = load_img(test_filepath)\n",
        "display_image(img)\n",
        "print(prediction)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EYgGK2zwtAdO"
      },
      "source": [
        "# Run batch predictions\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 jsonl file path from a Cloud Storage bucket, with a list of one or more images.\n",
        "* `gcs_destination_prefix`: The Cloud Storage location for storing the batch prediction resuls.\n",
        "* `sync`: If set to True, the call block while waiting for the asynchronous batch job to complete."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1z9d8oPXtD95"
      },
      "outputs": [],
      "source": [
        "# A jsonl file path from a Cloud Storage bucket, with all the to-be-predicted images.\n",
        "gcs_source = \"\"  # @param {type:\"string\"}\n",
        "\n",
        "batch_predict_job = model.batch_predict(\n",
        "    job_display_name=get_job_name_with_datetime(\"flowers_bp\"),\n",
        "    gcs_source=gcs_source,\n",
        "    gcs_destination_prefix=f\"gs://{BUCKET_URI}\",\n",
        "    sync=False,\n",
        ")\n",
        "print(batch_predict_job)\n",
        "\n",
        "# Wait for the batch prediction job to finish\n",
        "batch_predict_job.wait()\n",
        "\n",
        "\n",
        "# Get the batch prediction results\n",
        "import json\n",
        "\n",
        "import tensorflow as tf\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": "kkH2nrpdp4sp"
      },
      "source": [
        "## Clean up"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ax6vQVZhp9pR"
      },
      "outputs": [],
      "source": [
        "# Delete the dataset.\n",
        "if \"dataset\" in globals():\n",
        "    dataset.delete()\n",
        "\n",
        "# Undeploy model and delete endpoint.\n",
        "if \"endpoint\" in globals():\n",
        "    endpoint.undeploy_all()\n",
        "    endpoint.delete(force=True)\n",
        "\n",
        "# Delete models.\n",
        "if \"model\" in globals():\n",
        "    model.delete()\n",
        "\n",
        "# Delete the batch predictio job.\n",
        "if \"batch_prediction_job\" in globals():\n",
        "    batch_predict_job.delete()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "model_garden_proprietary_image_classification.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}