notebooks/official/training/distributed_hyperparameter_tuning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ur8xi4C7S06n"
      },
      "outputs": [],
      "source": [
        "# Copyright 2022 Google LLC\n",
        "#\n",
        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Nmi2KIicB7S"
      },
      "source": [
        "# Distributed Vertex AI Hyperparameter Tuning\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/distributed_hyperparameter_tuning.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fofficial%2Ftraining%2Fdistributed_hyperparameter_tuning.ipynb\">\n",
        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
        "    </a>\n",
        "  </td>    \n",
        "  <td style=\"text-align: center\">\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/training/distributed_hyperparameter_tuning.ipynb\">\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
        "    </a>\n",
        "  </td>\n",
        "  <td style=\"text-align: center\">\n",
        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/training/distributed_hyperparameter_tuning.ipynb\">\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c582cc111a01"
      },
      "source": [
        "## Overview\n",
        "\n",
        "This notebook demonstrates how to run a hyperparameter tuning job with Vertex AI Training to discover optimal hyperparameter values for an ML model. To speed up the training process, `MirroredStrategy` from the `tf.distribute` module is used to distribute training across multiple GPUs on a single machine.\n",
        "\n",
        "Learn more about [Vertex AI Hyperparameter Tuning](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "60d57f1c2ae0"
      },
      "source": [
        "### Objective\n",
        "\n",
        "In this notebook, you create a custom trained model from a Python script in a Docker container. You learn how to modify training application code for hyperparameter tuning and submit a Vertex AI Hyperparameter Tuning job with the Python SDK.\n",
        "\n",
        "This tutorial uses the following Google Cloud ML services:\n",
        "\n",
        "- `Vertex AI Training`\n",
        "- `Vertex AI Hyperparameter Tuning`\n",
        "\n",
        "The steps performed include:\n",
        "\n",
        "- Training using a Python package.\n",
        "- Report accuracy when hyperparameter tuning.\n",
        "- Save the model artifacts to Cloud Storage using GCSFuse."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tvgnzT1CKxrO"
      },
      "source": [
        "### Dataset\n",
        "\n",
        "The dataset used for this tutorial is the [horses or humans dataset](https://www.tensorflow.org/datasets/catalog/horses_or_humans) from [TensorFlow Datasets](https://www.tensorflow.org/datasets). The trained model predicts if an image is of a horse or a human.\n",
        "\n",
        "### Costs \n",
        "\n",
        "This tutorial uses billable components of Google Cloud:\n",
        "\n",
        "* Vertex AI\n",
        "* Cloud Storage\n",
        "\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": "3b1ffd5ab768"
      },
      "source": [
        "## Get Started"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "install_aip"
      },
      "source": [
        "### Install Vertex AI SDK for Python and other required packages"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wyy5Lbnzg5fi"
      },
      "outputs": [],
      "source": [
        "! pip3 install --upgrade google-cloud-aiplatform -q"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ff555b32bab8"
      },
      "source": [
        "### Restart runtime (Colab only)\n",
        "\n",
        "To use the newly installed packages, you must restart the runtime on Google Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f09b4dff629a"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "if \"google.colab\" in sys.modules:\n",
        "\n",
        "    import IPython\n",
        "\n",
        "    app = IPython.Application.instance()\n",
        "    app.kernel.do_shutdown(True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "54c5ef8a8f43"
      },
      "source": [
        "<div class=\"alert alert-block alert-warning\">\n",
        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step. ⚠️</b>\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f82e28c631cc"
      },
      "source": [
        "### Authenticate your notebook environment (Colab only)\n",
        "\n",
        "Authenticate your environment on Google Colab."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "46604f70e831"
      },
      "outputs": [],
      "source": [
        "import sys\n",
        "\n",
        "if \"google.colab\" in sys.modules:\n",
        "\n",
        "    from google.colab import auth\n",
        "\n",
        "    auth.authenticate_user()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ef3990d0482a"
      },
      "source": [
        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
        "\n",
        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oM1iC_MfAts1"
      },
      "outputs": [],
      "source": [
        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
        "LOCATION = \"us-central1\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5755d1a554f"
      },
      "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": "d2de92accb67"
      },
      "outputs": [],
      "source": [
        "BUCKET_URI = f\"gs://dist-{PROJECT_ID}-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-EcIXiGsCePi"
      },
      "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": "NIq7R4HZCfIc"
      },
      "outputs": [],
      "source": [
        "! gsutil mb -l {LOCATION} {BUCKET_URI}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XoEqT2Y4DJmf"
      },
      "source": [
        "### Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pRUOFELefqf1"
      },
      "outputs": [],
      "source": [
        "from google.cloud import aiplatform\n",
        "from google.cloud.aiplatform import hyperparameter_tuning as hpt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "reBCSTKOg47l"
      },
      "source": [
        "### Write Dockerfile\n",
        "\n",
        "The first step in containerizing your code is to create a Dockerfile. In the Dockerfile, you'll include all the commands needed to run the image such as installing the necessary libraries and setting up the entry point for the training code.\n",
        "\n",
        "This Dockerfile uses the Deep Learning Container TensorFlow Enterprise 2.5 GPU Docker image. The Deep Learning Containers on Google Cloud come with many common ML and data science frameworks pre-installed. After downloading that image, this Dockerfile installs the [CloudML Hypertune](https://github.com/GoogleCloudPlatform/cloudml-hypertune) library and sets up the entrypoint for the training code.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e231837fe138"
      },
      "outputs": [],
      "source": [
        "%%writefile Dockerfile\n",
        "\n",
        "FROM gcr.io/deeplearning-platform-release/tf2-gpu.2-6\n",
        "WORKDIR /\n",
        "\n",
        "# Installs hypertune library\n",
        "RUN pip install cloudml-hypertune\n",
        "\n",
        "# Copies the trainer code to the docker image.\n",
        "COPY trainer /trainer\n",
        "\n",
        "# Sets up the entry point to invoke the trainer.\n",
        "ENTRYPOINT [\"python\", \"-m\", \"trainer.task\"]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4c2ea367c79d"
      },
      "source": [
        "### Create training application code\n",
        "\n",
        "Next, you create a trainer directory with a `task.py` script that contains the code for your training application."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MjJTYC86hPOZ"
      },
      "outputs": [],
      "source": [
        "# Create trainer directory\n",
        "\n",
        "! mkdir trainer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ea83c1253a74"
      },
      "source": [
        "In the next cell, you write the contents of the training script, `task.py`. This file downloads the _horses or humans_ dataset from TensorFlow datasets and trains a `tf.keras` functional model using `MirroredStrategy` from the `tf.distribute` module.\n",
        "\n",
        "There are a few components that are specific to using the hyperparameter tuning service:\n",
        "\n",
        "* The script imports the `hypertune` library. Note that the Dockerfile included instructions to pip install the hypertune library.\n",
        "* The function `get_args()` defines a command-line argument for each hyperparameter you want to tune. In this example, the hyperparameters that will be tuned are the learning rate, the momentum value in the optimizer, and the number of units in the last hidden layer of the model. The value passed in those arguments is then used to set the corresponding hyperparameter in the code.\n",
        "* At the end of the `main()` function, the hypertune library is used to define the metric to optimize. In this example, the metric that will be optimized is the the validation accuracy. This metric is passed to an instance of `HyperTune`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9b52fd75d90f"
      },
      "outputs": [],
      "source": [
        "%%writefile trainer/task.py\n",
        "\n",
        "import argparse\n",
        "import hypertune\n",
        "import tensorflow as tf\n",
        "import tensorflow_datasets as tfds\n",
        "\n",
        "def get_args():\n",
        "  \"\"\"Parses args. Must include all hyperparameters you want to tune.\"\"\"\n",
        "\n",
        "  parser = argparse.ArgumentParser()\n",
        "  parser.add_argument(\n",
        "      '--learning_rate', required=True, type=float, help='learning rate')\n",
        "  parser.add_argument(\n",
        "      '--momentum', required=True, type=float, help='SGD momentum value')\n",
        "  parser.add_argument(\n",
        "      '--units',\n",
        "      required=True,\n",
        "      type=int,\n",
        "      help='number of units in last hidden layer')\n",
        "  parser.add_argument(\n",
        "      '--epochs',\n",
        "      required=False,\n",
        "      type=int,\n",
        "      default=10,\n",
        "      help='number of training epochs')\n",
        "  args = parser.parse_args()\n",
        "  return args\n",
        "\n",
        "\n",
        "def preprocess_data(image, label):\n",
        "  \"\"\"Resizes and scales images.\"\"\"\n",
        "\n",
        "  image = tf.image.resize(image, (150, 150))\n",
        "  return tf.cast(image, tf.float32) / 255., label\n",
        "\n",
        "\n",
        "def create_dataset(batch_size):\n",
        "  \"\"\"Loads Horses Or Humans dataset and preprocesses data.\"\"\"\n",
        "\n",
        "  data, info = tfds.load(\n",
        "      name='horses_or_humans', as_supervised=True, with_info=True)\n",
        "\n",
        "  # Create train dataset\n",
        "  train_data = data['train'].map(preprocess_data)\n",
        "  train_data = train_data.shuffle(1000)\n",
        "  train_data = train_data.batch(batch_size)\n",
        "\n",
        "  # Create validation dataset\n",
        "  validation_data = data['test'].map(preprocess_data)\n",
        "  validation_data = validation_data.batch(64)\n",
        "\n",
        "  return train_data, validation_data\n",
        "\n",
        "\n",
        "def create_model(units, learning_rate, momentum):\n",
        "  \"\"\"Defines and compiles model.\"\"\"\n",
        "\n",
        "  inputs = tf.keras.Input(shape=(150, 150, 3))\n",
        "  x = tf.keras.layers.Conv2D(16, (3, 3), activation='relu')(inputs)\n",
        "  x = tf.keras.layers.MaxPooling2D((2, 2))(x)\n",
        "  x = tf.keras.layers.Conv2D(32, (3, 3), activation='relu')(x)\n",
        "  x = tf.keras.layers.MaxPooling2D((2, 2))(x)\n",
        "  x = tf.keras.layers.Conv2D(64, (3, 3), activation='relu')(x)\n",
        "  x = tf.keras.layers.MaxPooling2D((2, 2))(x)\n",
        "  x = tf.keras.layers.Flatten()(x)\n",
        "  x = tf.keras.layers.Dense(units, activation='relu')(x)\n",
        "  outputs = tf.keras.layers.Dense(1, activation='sigmoid')(x)\n",
        "  model = tf.keras.Model(inputs, outputs)\n",
        "  model.compile(\n",
        "      loss='binary_crossentropy',\n",
        "      optimizer=tf.keras.optimizers.SGD(\n",
        "          learning_rate=learning_rate, momentum=momentum),\n",
        "      metrics=['accuracy'])\n",
        "  return model\n",
        "\n",
        "\n",
        "def main():\n",
        "  args = get_args()\n",
        "\n",
        "  # Create Strategy\n",
        "  strategy = tf.distribute.MirroredStrategy()\n",
        "\n",
        "  # Scale batch size\n",
        "  GLOBAL_BATCH_SIZE = 64 * strategy.num_replicas_in_sync  \n",
        "  train_data, validation_data = create_dataset(GLOBAL_BATCH_SIZE)\n",
        "\n",
        "  # Wrap model variables within scope\n",
        "  with strategy.scope():\n",
        "    model = create_model(args.units, args.learning_rate, args.momentum)\n",
        "\n",
        "  # Train model\n",
        "  history = model.fit(\n",
        "      train_data, epochs=args.epochs, validation_data=validation_data)\n",
        "\n",
        "  # Define Metric\n",
        "  hp_metric = history.history['val_accuracy'][-1]\n",
        "\n",
        "  hpt = hypertune.HyperTune()\n",
        "  hpt.report_hyperparameter_tuning_metric(\n",
        "      hyperparameter_metric_tag='accuracy',\n",
        "      metric_value=hp_metric,\n",
        "      global_step=args.epochs)\n",
        "\n",
        "\n",
        "if __name__ == '__main__':\n",
        "  main()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2dc0a526f77e"
      },
      "source": [
        "### Build the Container\n",
        "\n",
        "In the next cells, you build the container and push it to Google Container Registry."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "a42d0b918ab4"
      },
      "outputs": [],
      "source": [
        "# Set the IMAGE_URI\n",
        "IMAGE_URI = f\"gcr.io/{PROJECT_ID}/horse-human:hypertune\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "360a5271fbdb"
      },
      "outputs": [],
      "source": [
        "# Build the docker image\n",
        "! docker build -f Dockerfile -t $IMAGE_URI ./"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "526475da6370"
      },
      "outputs": [],
      "source": [
        "# Push it to Google Container Registry:\n",
        "! docker push $IMAGE_URI"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aaff6f5be7f6"
      },
      "source": [
        "### Create and run hyperparameter tuning job on Vertex AI\n",
        "\n",
        "Once your container is pushed to Google Container Registry, you use the Vertex SDK to create and run the hyperparameter tuning job.\n",
        "\n",
        "You define the following specifications:\n",
        "* `worker_pool_specs`: Dictionary specifying the machine type and Docker image. This example defines a single node cluster with one `n1-standard-4` machine with two `NVIDIA_TESLA_T4` GPUs.\n",
        "* `parameter_spec`: Dictionary specifying the parameters to optimize. The dictionary key is the string assigned to the command line argument for each hyperparameter in your training application code, and the dictionary value is the parameter specification. The parameter specification includes the type, min/max values, and scale for the hyperparameter.\n",
        "* `metric_spec`: Dictionary specifying the metric to optimize. The dictionary key is the `hyperparameter_metric_tag` that you set in your training application code, and the value is the optimization goal."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aec22792ee84"
      },
      "outputs": [],
      "source": [
        "worker_pool_specs = [\n",
        "    {\n",
        "        \"machine_spec\": {\n",
        "            \"machine_type\": \"n1-standard-4\",\n",
        "            \"accelerator_type\": \"NVIDIA_TESLA_T4\",\n",
        "            \"accelerator_count\": 2,\n",
        "        },\n",
        "        \"replica_count\": 1,\n",
        "        \"container_spec\": {\"image_uri\": IMAGE_URI},\n",
        "    }\n",
        "]\n",
        "\n",
        "metric_spec = {\"accuracy\": \"maximize\"}\n",
        "\n",
        "parameter_spec = {\n",
        "    \"learning_rate\": hpt.DoubleParameterSpec(min=0.001, max=1, scale=\"log\"),\n",
        "    \"momentum\": hpt.DoubleParameterSpec(min=0, max=1, scale=\"linear\"),\n",
        "    \"units\": hpt.DiscreteParameterSpec(values=[64, 128, 512], scale=None),\n",
        "}"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ffd01019a764"
      },
      "source": [
        "Create a `CustomJob`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f2eed1471a24"
      },
      "outputs": [],
      "source": [
        "# Create a CustomJob\n",
        "\n",
        "JOB_NAME = \"horses-humans-hyperparam-job\"\n",
        "\n",
        "my_custom_job = aiplatform.CustomJob(\n",
        "    display_name=JOB_NAME,\n",
        "    project=PROJECT_ID,\n",
        "    worker_pool_specs=worker_pool_specs,\n",
        "    staging_bucket=BUCKET_URI,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0e5ee7ee5ae4"
      },
      "source": [
        "Then, create and run a `HyperparameterTuningJob`.\n",
        "\n",
        "There are a few arguments to note:\n",
        "\n",
        "* `max_trial_count`: Sets an upper bound on the number of trials the service will run. The recommended practice is to start with a smaller number of trials and get a sense of how impactful your chosen hyperparameters are before scaling up.\n",
        "\n",
        "* `parallel_trial_count`:  If you use parallel trials, the service provisions multiple training processing clusters. The worker pool spec that you specify when creating the job is used for each individual training cluster.  Increasing the number of parallel trials reduces the amount of time the hyperparameter tuning job takes to run; however, it can reduce the effectiveness of the job overall. This is because the default tuning strategy uses results of previous trials to inform the assignment of values in subsequent trials.\n",
        " \n",
        "* `search_algorithm`: The available search algorithms are grid, random, or default (None). The default option applies Bayesian optimization to search the space of possible hyperparameter values and is the recommended algorithm."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bb6ca1b103ef"
      },
      "outputs": [],
      "source": [
        "# Create and run HyperparameterTuningJob\n",
        "\n",
        "hp_job = aiplatform.HyperparameterTuningJob(\n",
        "    display_name=JOB_NAME,\n",
        "    custom_job=my_custom_job,\n",
        "    metric_spec=metric_spec,\n",
        "    parameter_spec=parameter_spec,\n",
        "    max_trial_count=15,\n",
        "    parallel_trial_count=3,\n",
        "    project=PROJECT_ID,\n",
        "    search_algorithm=None,\n",
        ")\n",
        "\n",
        "hp_job.run()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "396d86efe829"
      },
      "source": [
        "Click on the generated link in the output to see your run in the Cloud Console. When the job completes, you will see the results of the tuning trials."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bee87f15ff24"
      },
      "source": [
        "![console_ui_results](tuning_results.png)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpV-iwP9qw9c"
      },
      "source": [
        "## Cleaning up\n",
        "\n",
        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
        "\n",
        "Otherwise, you can delete the individual resources you created in this tutorial:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "sx_vKniMq9ZX"
      },
      "outputs": [],
      "source": [
        "# Set this to true only if you'd like to delete your bucket\n",
        "delete_bucket = False\n",
        "\n",
        "hp_job.delete()\n",
        "\n",
        "!gcloud container images delete $IMAGE_URI --force-delete-tags --quiet\n",
        "\n",
        "if delete_bucket:\n",
        "    ! gsutil rm -r $BUCKET_URI"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "distributed_hyperparameter_tuning.ipynb",
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}