Merge branch 'main' into model_monitor_batch

Update the vizier sample to replace the gapic library with new Vertex Vizier SDK. (#979 )
* Update the vizier codelab to replace the gapic library with new Vertex Vizier SDK. * Added the [project_id] and [region] in the parameter field. * Fixed the lint errors for vizier sample. Co-authored-by: Ivan Cheung <ivans.mailbox@gmail.com> Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>
2026-09-26 14:42:04 +00:00 · 2022-09-23 14:17:14 -04:00 · 2022-09-23 11:12:28 -07:00 · 2022-09-23 13:37:58 -04:00 · 2022-09-23 07:17:58 -07:00 · 2022-09-22 14:45:45 -07:00
5 changed files with 2621 additions and 2316 deletions
@@ -3,7 +3,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "d3069d95",
      "metadata": {
        "cellView": "form",
        "id": "d3069d95"
@@ -11,7 +10,7 @@
      "outputs": [],
      "source": [
        "# @title Copyright & License (click to expand)\n",
-        "# Copyright 2021 Google LLC\n",
+        "# 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",
@@ -28,7 +27,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "546c53de",
      "metadata": {
        "id": "546c53de"
      },
@@ -46,13 +44,16 @@
        "        <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><td>\n",
+        "    <a href=\"https://console.cloud.google.com/ai-platform/notebooks/deploy-notebook?name=Model%20Monitoring&download_url=https%3A%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fmodel_monitoring%2Fbatch_prediction_model_monitoring.ipynb\">\n",
+        "       <img src=\"https://www.gstatic.com/cloud/images/navigation/vertex-ai.svg\" alt=\"Google Cloud Notebooks\">Open in Workbench AI Notebook\n",
+        "    </a>\n",
+        "  </td> \n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
-      "id": "53fd1070",
      "metadata": {
        "id": "53fd1070"
      },
@@ -64,7 +65,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "8b26c855",
      "metadata": {
        "id": "8b26c855"
      },
@@ -98,7 +98,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "d52ba95b",
      "metadata": {
        "id": "d52ba95b"
      },
@@ -110,7 +109,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "e64fb18a",
      "metadata": {
        "id": "e64fb18a"
      },
@@ -123,7 +121,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "9d839347",
      "metadata": {
        "id": "9d839347"
      },
@@ -142,7 +139,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "738fce1f",
      "metadata": {
        "id": "738fce1f"
      },
@@ -155,7 +151,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "4536fe4e",
      "metadata": {
        "id": "4536fe4e"
      },
@@ -178,14 +173,13 @@
        "    USER_FLAG = \"--user\"\n",
        "\n",
        "# Install Python package dependencies.\n",
-        "! pip3 install -q tensorflow-data-validation $USER_FLAG\n",
-        "! pip3 install -q google-api-core $USER_FLAG\n",
-        "! pip3 install -q google-cloud-aiplatform $USER_FLAG"
+        "! pip3 install -q {USER_FLAG} tensorflow-data-validation \\\n",
+        "                              google-api-core \\\n",
+        "                              google-cloud-aiplatform"
      ]
    },
    {
      "cell_type": "markdown",
-      "id": "6e98402b",
      "metadata": {
        "id": "6e98402b"
      },
@@ -198,7 +192,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "9775c9ff",
      "metadata": {
        "id": "9775c9ff"
      },
@@ -217,7 +210,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "d5737134",
      "metadata": {
        "id": "d5737134"
      },
@@ -242,7 +234,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "cfb1a1d5",
      "metadata": {
        "id": "cfb1a1d5"
      },
@@ -255,50 +246,33 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "cf8535e4",
      "metadata": {
        "id": "cf8535e4"
      },
      "outputs": [],
      "source": [
-        "import os\n",
-        "\n",
-        "PROJECT_ID = \"\"\n",
-        "\n",
-        "# Get your Google Cloud project ID from gcloud\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
-        "    PROJECT_ID = shell_output[0]\n",
-        "    print(\"Project ID: \", PROJECT_ID)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "05a2d397",
-      "metadata": {
-        "id": "05a2d397"
-      },
-      "source": [
-        "Otherwise, set your project ID here.\n"
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "1c2be4bd",
      "metadata": {
        "id": "1c2be4bd"
      },
      "outputs": [],
      "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
-        "    PROJECT_ID = \"python-docs-samples-tests\"  # @param {type:\"string\"}"
+        "if PROJECT_ID == \"\" or not PROJECT_ID or PROJECT_ID == \"[your-project-id]\":\n",
+        "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "    # Get your GCP project id from gcloud\n",
+        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
+        "    PROJECT_ID = shell_output[0]\n",
+        "    print(\"Project ID:\", PROJECT_ID)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "c129705c",
      "metadata": {
        "id": "c129705c"
      },
@@ -309,32 +283,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "71404c9f",
-      "metadata": {
-        "id": "71404c9f"
-      },
-      "source": [
-        "#### Set your email address\n",
-        "This is used for delivering model monitoring notifications.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "id": "4b1d2b69",
-      "metadata": {
-        "id": "4b1d2b69"
-      },
-      "outputs": [],
-      "source": [
-        "EMAIL_ADDRESS = \"[your-email-address]\"  # @param {type:\"string\"}\n",
-        "if not EMAIL_ADDRESS or EMAIL_ADDRESS == \"[your-email-address]\":\n",
-        "    print(\"EMAIL_ADDRESS not specified, please correct before proceeding.\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "83340af4",
      "metadata": {
        "id": "83340af4"
      },
@@ -356,18 +304,73 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "4814ea21",
      "metadata": {
        "id": "4814ea21"
      },
      "outputs": [],
      "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "06571eb4063b"
+      },
+      "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": "4e166d927e36"
+      },
+      "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": "71404c9f"
+      },
+      "source": [
+        "#### Set your email address\n",
+        "This is used for delivering model monitoring notifications.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4b1d2b69"
+      },
+      "outputs": [],
+      "source": [
+        "EMAIL_ADDRESS = \"[your-email-address]\"  # @param {type:\"string\"}\n",
+        "if not EMAIL_ADDRESS or EMAIL_ADDRESS == \"[your-email-address]\":\n",
+        "    print(\"EMAIL_ADDRESS not specified, please correct before proceeding.\")"
      ]
    },
    {
      "cell_type": "markdown",
-      "id": "20a546c3",
      "metadata": {
        "id": "20a546c3"
      },
@@ -375,16 +378,35 @@
        "### Authenticate your Google Cloud account\n",
        "\n",
        "**If you are using Vertex AI Workbench notebooks**, your environment is already\n",
-        "authenticated. Skip this step.\n",
+        "authenticated.\n",
        "\n",
        "**If you are using Colab**, run the cell below and follow the instructions\n",
-        "when prompted to authenticate your account via oAuth.\n"
+        "when prompted to authenticate your account via oAuth.\n",
+        "\n",
+        "**Otherwise**, follow these steps:\n",
+        "\n",
+        "1. In the Cloud Console, go to the [**Create service account key**\n",
+        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
+        "\n",
+        "2. Click **Create service account**.\n",
+        "\n",
+        "3. In the **Service account name** field, enter a name, and\n",
+        "   click **Create**.\n",
+        "\n",
+        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
+        "into the filter box, and select\n",
+        "   **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
+        "\n",
+        "5. Click **Create**. A JSON file that contains your key downloads to your\n",
+        "local environment.\n",
+        "\n",
+        "6. Enter the path to your service account key as the\n",
+        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "06c51076",
      "metadata": {
        "id": "06c51076"
      },
@@ -421,73 +443,284 @@
    },
    {
      "cell_type": "markdown",
-      "id": "6b01af18",
      "metadata": {
-        "id": "6b01af18"
+        "id": "bucket:custom"
      },
      "source": [
-        "### Upload the model\n",
+        "### Create a Cloud Storage bucket\n",
        "\n",
-        "The churn propensity model you'll be using in this notebook has been trained in BigQuery ML and exported to a Google Cloud Storage bucket. This illustrates how you can easily export a trained model and move a model from one cloud service to another. \n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
        "\n",
-        "Next, import the model. **If you've already imported your model, you can skip this step.**"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "id": "9638ad2c",
-      "metadata": {
-        "id": "9638ad2c"
-      },
-      "source": [
-        "<span id=\"papermill-error-cell\" style=\"color:red; font-family:Helvetica Neue, Helvetica, Arial, sans-serif; font-size:2em;\">Execution using papermill encountered an exception here and stopped:</span>"
+        "Set the name of your Cloud Storage bucket below, which you use in this tutorial to upload the `input schema` for the monitoring service.\n",
+        "\n",
+        "Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "926e3ba8",
      "metadata": {
-        "id": "926e3ba8"
+        "id": "bucket"
      },
      "outputs": [],
      "source": [
-        "from datetime import datetime\n",
-        "import json\n",
-        "import time\n",
-        "import re\n",
-        "import tensorflow as tf\n",
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "autoset_bucket"
+      },
+      "outputs": [],
+      "source": [
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "create_bucket"
+      },
+      "source": [
+        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "create_bucket"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil mb -l $REGION $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "validate_bucket"
+      },
+      "source": [
+        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "validate_bucket"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil ls -al $BUCKET_URI"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a0d294ff6d10"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "bd7a633296eb"
+      },
+      "outputs": [],
+      "source": [
+        "import google.cloud.aiplatform as aiplatform\n",
        "import tensorflow_data_validation as tfdv\n",
-        "from tensorflow_data_validation.utils import io_util \n",
-        "from tensorflow_metadata.proto.v0 import statistics_pb2\n",
-        "\n",
-        "MODEL_DISPLAY_NAME=f\"batch_prediction_monitoring_test_model_{datetime.now().strftime('%Y%m%d%H%M%S')}\"\n",
-        "CONTAINER_IMAGE_URI=\"us-docker.pkg.dev/cloud-aiplatform/prediction/tf2-cpu.2-4:latest\"\n",
-        "ARTIFACT_URI=\"gs://mco-mm/churn\"\n",
-        "\n",
-        "output = ! gcloud ai models upload \\\n",
-        "  --region=$REGION \\\n",
-        "  --display-name=$MODEL_DISPLAY_NAME \\\n",
-        "  --artifact-uri=$ARTIFACT_URI \\\n",
-        "  --container-image-uri=$CONTAINER_IMAGE_URI \\\n",
-        "  --format=\"value(model)\"\n",
-        "MODEL_ID = output[1].split(\"/\")[5]\n",
-        "print(f\"Model {MODEL_ID} created.\")"
+        "from tensorflow_data_validation.utils import io_util\n",
+        "from tensorflow_metadata.proto.v0 import statistics_pb2"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "accelerators:training,prediction"
+      },
+      "source": [
+        "#### Set hardware accelerators\n",
+        "\n",
+        "You can set hardware accelerators for prediction (e.g., GPUs) or choose not to use any (CPU). Hardware accelertors lower the latency response for a prediction request. When choosing a hardware accelerators, consider the additional cost trade-off over latency.\n",
+        "\n",
+        "Set the variables `DEPLOY_GPU/DEPLOY_NGPU` to use a container image supporting a GPU and the number of GPUs allocated to the virtual machine (VM) instance. For example, to use a GPU container image with 4 Nvidia Tesla K80 GPUs allocated to each VM, you would specify:\n",
+        "\n",
+        "    (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 4)\n",
+        "\n",
+        "See the [locations where accelerators are available](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators).\n",
+        "\n",
+        "Otherwise specify `(None, None)` to use a container image to run on a CPU."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "xd5PLXDTlugv"
+      },
+      "outputs": [],
+      "source": [
+        "GPU = False\n",
+        "if GPU:\n",
+        "    DEPLOY_GPU, DEPLOY_NGPU = (aiplatform.gapic.AcceleratorType.NVIDIA_TESLA_K80, 1)\n",
+        "else:\n",
+        "    DEPLOY_GPU, DEPLOY_NGPU = (None, None)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "container:training,prediction"
+      },
+      "source": [
+        "#### Set pre-built containers\n",
+        "\n",
+        "Set the pre-built Docker container image for prediction.\n",
+        "\n",
+        "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": "1u1mr18jlugv"
+      },
+      "outputs": [],
+      "source": [
+        "if GPU:\n",
+        "    DEPLOY_VERSION = \"tf2-gpu.2-5\"\n",
+        "else:\n",
+        "    DEPLOY_VERSION = \"tf2-cpu.2-5\"\n",
+        "\n",
+        "DEPLOY_IMAGE = \"{}-docker.pkg.dev/vertex-ai/prediction/{}:latest\".format(\n",
+        "    REGION.split(\"-\")[0], DEPLOY_VERSION\n",
+        ")\n",
+        "\n",
+        "print(\"Deployment:\", DEPLOY_IMAGE, DEPLOY_GPU, DEPLOY_NGPU)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "machine:training,prediction"
+      },
+      "source": [
+        "#### Set machine types\n",
+        "\n",
+        "Next, set the machine types to use for training and prediction.\n",
+        "\n",
+        "- Set the variable `DEPLOY_COMPUTE` to configure your compute resources for prediction.\n",
+        " - `machine type`\n",
+        "     - `n1-standard`: 3.75GB of memory per vCPU\n",
+        "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
+        "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
+        " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
+        "\n",
+        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "YAXwbqKKlugv"
+      },
+      "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",
+        "MACHINE_TYPE = \"n1-standard\"\n",
+        "\n",
+        "VCPU = \"4\"\n",
+        "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
+        "print(\"Deploy machine type\", DEPLOY_COMPUTE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9bf06cd476e9"
+      },
+      "source": [
+        "### Upload the model artifacts as a `Vertex AI Model` resource\n",
+        "\n",
+        "First, you upload the pre-trained custom tabular model artifacts as a `Vertex AI Model` resource using the `upload()` method, with the following parameters:\n",
+        "\n",
+        "- `display_name`: The human readable name for the `Model` resource.\n",
+        "- `artifact_uri`: The Cloud Storage location of the model artifacts.\n",
+        "- `serving_container_image`: The serving container image to use when the model is deployed to a `Vertex AI Endpoint` resource.\n",
+        "- `sync`: Whether to wait for the process to complete, or return immediately (async)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0193f247e216"
+      },
+      "outputs": [],
+      "source": [
+        "MODEL_ARTIFACT_URI = \"gs://mco-mm/churn\"\n",
+        "\n",
+        "model = aiplatform.Model.upload(\n",
+        "    display_name=\"churn_\" + UUID,\n",
+        "    artifact_uri=MODEL_ARTIFACT_URI,\n",
+        "    serving_container_image_uri=DEPLOY_IMAGE,\n",
+        "    sync=True,\n",
+        ")\n",
+        "\n",
+        "print(model)"
      ]
    },
    {
      "cell_type": "markdown",
-      "id": "a4305ddf",
      "metadata": {
        "id": "a4305ddf"
      },
      "source": [
-        "## Submit a batch prediction request with model monitoring enabled"
+        "## Submit a batch prediction request with model monitoring enabled\n"
      ]
    },
    {
      "cell_type": "markdown",
-      "id": "053fde99",
      "metadata": {
        "id": "053fde99"
      },
@@ -503,7 +736,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "b832ad31",
      "metadata": {
        "id": "b832ad31"
      },
@@ -511,20 +743,17 @@
      "source": [
        "# Copy files to your projects gs bucket to avoid permission issues.\n",
        "# Ignore any error(s) for bucket already exists.\n",
-        "OUTPUT_GS_PATH = f\"gs://{PROJECT_ID.replace('-', '_')}_bp_mm_output\"\n",
-        "INPUT_GS_PATH = f\"gs://{PROJECT_ID.replace('-', '_')}_bp_mm_input\"\n",
+        "OUTPUT_GS_PATH = f\"{BUCKET_URI}/bp_mm_output\"\n",
+        "INPUT_GS_PATH = f\"{BUCKET_URI}/bp_mm_input\"\n",
        "PUBLIC_TRAINING_DATASET = \"gs://bp_mm_public_data/churn/churn_bp_insample.csv\"\n",
        "TRAINING_DATASET = f\"{INPUT_GS_PATH}/churn_bp_insample.csv\"\n",
        "TRAINING_DATASET_FORMAT = \"csv\"\n",
        "\n",
-        "! gsutil mb -p {PROJECT_ID} -l {REGION} -b on {INPUT_GS_PATH}\n",
-        "! gsutil mb -p {PROJECT_ID} -l {REGION} -b on {OUTPUT_GS_PATH}\n",
        "! gsutil copy $PUBLIC_TRAINING_DATASET $INPUT_GS_PATH"
      ]
    },
    {
      "cell_type": "markdown",
-      "id": "34c95126",
      "metadata": {
        "id": "34c95126"
      },
@@ -541,21 +770,18 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "3a54368a",
      "metadata": {
        "id": "3a54368a"
      },
      "outputs": [],
      "source": [
-        "now = datetime.now()\n",
        "INPUT_URI = \"gs://bp_mm_public_data/churn/churn_bp_outsample.jsonl\"\n",
        "OUTPUT_URI = OUTPUT_GS_PATH\n",
        "INSTANCES_FORMAT = \"jsonl\"\n",
        "PREDICTIONS_FORMAT = \"jsonl\"\n",
        "JOB_NAME_PREFIX = \"bp_mm_demo\"\n",
-        "MODEL_NAME = f\"projects/{PROJECT_ID}/locations/{REGION}/models/{MODEL_ID}\"\n",
-        "MACHINE_TYPE = \"n1-standard-8\"\n",
-        "BATCH_PREDICTION_JOB_NAME = JOB_NAME_PREFIX + \"_\" + now.strftime(\"%Y%m%d%H%M%S\")\n",
+        "MODEL_NAME = model.resource_name\n",
+        "BATCH_PREDICTION_JOB_NAME = JOB_NAME_PREFIX + \"_\" + UUID\n",
        "\n",
        "from google.cloud.aiplatform_v1beta1.types import (\n",
        "    BatchDedicatedResources, BatchPredictionJob, GcsDestination, GcsSource,\n",
@@ -573,7 +799,7 @@
        "        gcs_destination=GcsDestination(output_uri_prefix=OUTPUT_URI),\n",
        "    ),\n",
        "    dedicated_resources=BatchDedicatedResources(\n",
-        "        machine_spec=MachineSpec(machine_type=MACHINE_TYPE),\n",
+        "        machine_spec=MachineSpec(machine_type=DEPLOY_COMPUTE),\n",
        "        starting_replica_count=1,\n",
        "        max_replica_count=1,\n",
        "    ),\n",
@@ -604,7 +830,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "cae39778",
      "metadata": {
        "id": "cae39778"
      },
@@ -617,7 +842,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "bcdd4a47",
      "metadata": {
        "id": "bcdd4a47"
      },
@@ -638,7 +862,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "49ec90a0",
      "metadata": {
        "id": "49ec90a0"
      },
@@ -651,7 +874,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "c30496b5",
      "metadata": {
        "id": "c30496b5"
      },
@@ -664,7 +886,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "831651c2",
      "metadata": {
        "id": "831651c2"
      },
@@ -684,7 +905,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "a705c10b",
      "metadata": {
        "id": "a705c10b"
      },
@@ -695,7 +915,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "2bbdddac",
      "metadata": {
        "id": "2bbdddac"
      },
@@ -708,7 +927,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "f6c674e9",
      "metadata": {
        "id": "f6c674e9"
      },
@@ -746,7 +964,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "233b1266",
      "metadata": {
        "id": "233b1266"
      },
@@ -759,7 +976,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "4e8c00a7",
      "metadata": {
        "id": "4e8c00a7"
      },
@@ -774,7 +990,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "497a0016",
      "metadata": {
        "id": "497a0016"
      },
@@ -790,7 +1005,6 @@
    {
      "cell_type": "code",
      "execution_count": null,
-      "id": "eabc3f81",
      "metadata": {
        "id": "eabc3f81"
      },
@@ -806,7 +1020,6 @@
    },
    {
      "cell_type": "markdown",
-      "id": "0aa0219d",
      "metadata": {
        "id": "0aa0219d"
      },
@@ -8,7 +8,7 @@
      },
      "outputs": [],
      "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# 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",
@@ -123,6 +123,7 @@
        "if IS_WORKBENCH_NOTEBOOK:\n",
        "    USER_FLAG = \"--user\"\n",
        "\n",
+        "! pip3 install google-vizier==0.0.4\n",
        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q"
      ]
    },
@@ -350,9 +351,9 @@
      "outputs": [],
      "source": [
        "import datetime\n",
-        "import json\n",
        "\n",
-        "from google.cloud import aiplatform"
+        "from google.cloud import aiplatform\n",
+        "from google.cloud.aiplatform.vizier import Study, pyvizier"
      ]
    },
    {
@@ -384,11 +385,9 @@
        "# These will be automatically filled in.\n",
        "STUDY_DISPLAY_NAME = \"{}_study_{}\".format(\n",
        "    PROJECT_ID.replace(\"-\", \"\"), datetime.datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n",
-        ")  # @param {type: 'string'}\n",
-        "ENDPOINT = REGION + \"-aiplatform.googleapis.com\"\n",
+        ")\n",
        "PARENT = \"projects/{}/locations/{}\".format(PROJECT_ID, REGION)\n",
        "\n",
-        "print(\"ENDPOINT: {}\".format(ENDPOINT))\n",
        "print(\"REGION: {}\".format(REGION))\n",
        "print(\"PARENT: {}\".format(PARENT))"
      ]
@@ -413,34 +412,21 @@
      "outputs": [],
      "source": [
        "# Parameter Configuration\n",
-        "\n",
-        "param_r = {\"parameter_id\": \"r\", \"double_value_spec\": {\"min_value\": 0, \"max_value\": 1}}\n",
-        "\n",
-        "param_theta = {\n",
-        "    \"parameter_id\": \"theta\",\n",
-        "    \"double_value_spec\": {\"min_value\": 0, \"max_value\": 1.57},\n",
-        "}\n",
+        "problem = pyvizier.StudyConfig()\n",
+        "problem.algorithm = pyvizier.Algorithm.RANDOM_SEARCH\n",
        "\n",
        "# Objective Metrics\n",
-        "metric_y1 = {\"metric_id\": \"y1\", \"goal\": \"MINIMIZE\"}\n",
+        "problem.metric_information.append(\n",
+        "    pyvizier.MetricInformation(name=\"y1\", goal=pyvizier.ObjectiveMetricGoal.MINIMIZE)\n",
+        ")\n",
+        "problem.metric_information.append(\n",
+        "    pyvizier.MetricInformation(name=\"y2\", goal=pyvizier.ObjectiveMetricGoal.MAXIMIZE)\n",
+        ")\n",
        "\n",
-        "# Objective Metrics\n",
-        "metric_y2 = {\"metric_id\": \"y2\", \"goal\": \"MAXIMIZE\"}\n",
-        "\n",
-        "# Put it all together in a study configuration\n",
-        "study = {\n",
-        "    \"display_name\": STUDY_DISPLAY_NAME,\n",
-        "    \"study_spec\": {\n",
-        "        \"algorithm\": \"RANDOM_SEARCH\",\n",
-        "        \"parameters\": [\n",
-        "            param_r,\n",
-        "            param_theta,\n",
-        "        ],\n",
-        "        \"metrics\": [metric_y1, metric_y2],\n",
-        "    },\n",
-        "}\n",
-        "\n",
-        "print(json.dumps(study, indent=2, sort_keys=True))"
+        "# Defines the parameters configuration.\n",
+        "root = problem.search_space.select_root()\n",
+        "root.add_float_param(\"r\", 0, 1.0, scale_type=pyvizier.ScaleType.LINEAR)\n",
+        "root.add_float_param(\"theta\", 0, 1.57, scale_type=pyvizier.ScaleType.LINEAR)"
      ]
    },
    {
@@ -462,10 +448,9 @@
      },
      "outputs": [],
      "source": [
-        "vizier_client = aiplatform.gapic.VizierServiceClient(\n",
-        "    client_options=dict(api_endpoint=ENDPOINT)\n",
-        ")\n",
-        "study = vizier_client.create_study(parent=PARENT, study=study)\n",
+        "aiplatform.init(project=PROJECT_ID, location=REGION)\n",
+        "study = Study.create_or_load(display_name=STUDY_DISPLAY_NAME, problem=problem)\n",
+        "\n",
        "STUDY_ID = study.name\n",
        "print(\"STUDY_ID: {}\".format(STUDY_ID))"
      ]
@@ -515,11 +500,12 @@
        "            r, theta, y1, y2\n",
        "        )\n",
        "    )\n",
-        "    metric1 = {\"metric_id\": \"y1\", \"value\": y1}\n",
-        "    metric2 = {\"metric_id\": \"y2\", \"value\": y2}\n",
+        "    measurement = pyvizier.Measurement()\n",
+        "    measurement.metrics[\"y1\"] = y1\n",
+        "    measurement.metrics[\"y2\"] = y2\n",
        "\n",
        "    # Return the results for this trial\n",
-        "    return [metric1, metric2]"
+        "    return measurement"
      ]
    },
    {
@@ -545,11 +531,11 @@
      },
      "outputs": [],
      "source": [
-        "client_id = \"client1\"  # @param {type: 'string'}\n",
-        "suggestion_count_per_request = 5  # @param {type: 'integer'}\n",
-        "max_trial_id_to_stop = 4  # @param {type: 'integer'}\n",
+        "worker_id = \"worker1\"  # @param {type: 'string'}\n",
+        "suggestion_count_per_request = 3  # @param {type: 'integer'}\n",
+        "max_trial_id_to_stop = 6  # @param {type: 'integer'}\n",
        "\n",
-        "print(\"client_id: {}\".format(client_id))\n",
+        "print(\"worker_id: {}\".format(worker_id))\n",
        "print(\"suggestion_count_per_request: {}\".format(suggestion_count_per_request))\n",
        "print(\"max_trial_id_to_stop: {}\".format(max_trial_id_to_stop))"
      ]
@@ -573,42 +559,17 @@
      },
      "outputs": [],
      "source": [
-        "trial_id = 0\n",
-        "while int(trial_id) < max_trial_id_to_stop:\n",
-        "    suggest_response = vizier_client.suggest_trials(\n",
-        "        {\n",
-        "            \"parent\": STUDY_ID,\n",
-        "            \"suggestion_count\": suggestion_count_per_request,\n",
-        "            \"client_id\": client_id,\n",
-        "        }\n",
-        "    )\n",
+        "while len(study.trials()) < max_trial_id_to_stop:\n",
+        "    trials = study.suggest(count=suggestion_count_per_request, worker=worker_id)\n",
        "\n",
-        "    for suggested_trial in suggest_response.result().trials:\n",
-        "        trial_id = suggested_trial.name.split(\"/\")[-1]\n",
-        "        trial = vizier_client.get_trial({\"name\": suggested_trial.name})\n",
-        "\n",
-        "        if trial.state in [\"COMPLETED\", \"INFEASIBLE\"]:\n",
-        "            continue\n",
-        "\n",
-        "        for param in trial.parameters:\n",
-        "            if param.parameter_id == \"r\":\n",
-        "                r = param.value\n",
-        "            elif param.parameter_id == \"theta\":\n",
-        "                theta = param.value\n",
-        "        print(\"Trial : r is {}, theta is {}.\".format(r, theta))\n",
-        "\n",
-        "        vizier_client.add_trial_measurement(\n",
-        "            {\n",
-        "                \"trial_name\": suggested_trial.name,\n",
-        "                \"measurement\": {\n",
-        "                    \"metrics\": CreateMetrics(suggested_trial.name, r, theta)\n",
-        "                },\n",
-        "            }\n",
+        "    for suggested_trial in trials:\n",
+        "        measurement = CreateMetrics(\n",
+        "            suggested_trial.name,\n",
+        "            suggested_trial.parameters[\"r\"].value,\n",
+        "            suggested_trial.parameters[\"theta\"].value,\n",
        "        )\n",
-        "\n",
-        "        response = vizier_client.complete_trial(\n",
-        "            {\"name\": suggested_trial.name, \"trial_infeasible\": False}\n",
-        "        )"
+        "        suggested_trial.add_measurement(measurement=measurement)\n",
+        "        suggested_trial.complete(measurement=measurement)"
      ]
    },
    {
@@ -630,8 +591,7 @@
      },
      "outputs": [],
      "source": [
-        "optimal_trials = vizier_client.list_optimal_trials({\"parent\": STUDY_ID})\n",
-        "\n",
+        "optimal_trials = study.optimal_trials()\n",
        "print(\"optimal_trials: {}\".format(optimal_trials))"
      ]
    },
@@ -655,7 +615,7 @@
      },
      "outputs": [],
      "source": [
-        "vizier_client.delete_study({\"name\": STUDY_ID})"
+        "study.delete()"
      ]
    }
  ],
Author	SHA1	Message	Date
gericdongandGitHub	3ebb74e97a	Merge branch 'main' into model_monitor_batch	2022-09-23 14:17:14 -04:00
halio-g GitHub Ivan Cheung Andrew Ferlitsch	15912adeaf	Update the vizier sample to replace the gapic library with new Vertex Vizier SDK. (#979 ) * Update the vizier codelab to replace the gapic library with new Vertex Vizier SDK. * Added the [project_id] and [region] in the parameter field. * Fixed the lint errors for vizier sample. Co-authored-by: Ivan Cheung <ivans.mailbox@gmail.com> Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-23 11:12:28 -07:00
gericdongandGitHub	a992a5530d	Merge branch 'main' into model_monitor_batch	2022-09-23 13:37:58 -04:00
Ivan NardiniandGitHub	35fdba7e1c	Vertex AI Experiments - Title fix (#982 ) * title fix * linter test passed	2022-09-23 07:17:58 -07:00
bhupana @ springml GitHub Ivan Cheung Andrew Ferlitsch	f403fa9051	Made UUID changes for Sdk automl tabular regression batch bq (#976 ) * Made UUID changes * Ran lintertest Co-authored-by: Ivan Cheung <ivans.mailbox@gmail.com> Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-22 14:45:45 -07:00
Ivan Nardini GitHub Andrew Ferlitsch	0d346b136e	Vertex AI Experiments - Comparing local trained models notebook - update (#971 ) * clean and update comparing_local_trained_models based on feedback * linter test passed * fix libraries * linter test passed * andy review fixes * linter test passed Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-09-22 12:21:22 -07:00
Andrew FerlitschandGitHub	24e0e92f8d	Merge branch 'main' into model_monitor_batch	2022-09-22 11:30:51 -07:00
Andrew Ferlitsch	bc4ec36914	fix: batch monitoring notebook	2022-09-22 17:33:24 +00:00
Andrew Ferlitsch	5387799f32	fix: batch monitoring notebook	2022-09-22 17:30:24 +00:00
Andrew FerlitschandGitHub	60d71d29cc	update: add explain example (#974 )	2022-09-22 09:35:13 -07:00