mirror of
https://github.com/GoogleCloudPlatform/vertex-ai-samples.git
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Autoindex 1 (#1348)
* feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective
This commit is contained in:
Andrew Ferlitsch
@@ -29,7 +29,7 @@
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"id": "JAPoU8Sm5E6e"
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},
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"source": [
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"# Vertex AI SDK for Python: AutoML Tabular Training and Prediction\n",
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"# Vertex AI SDK for Python: AutoML Tabular training and prediction\n",
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"\n",
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"<table align=\"left\">\n",
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" <td>\n",
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@@ -63,7 +63,7 @@
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"\n",
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"This tutorial demonstrates how to use the Vertex AI Python client library to train and deploy a tabular classification model for online prediction.\n",
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"\n",
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"**Note**: you may incur charges for training, prediction, storage, or usage of other GCP products in connection with testing this SDK."
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"**Note**: you may incur charges for training, prediction, storage, or usage of other Google Cloud products in connection with testing this SDK."
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]
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},
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{
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@@ -76,6 +76,11 @@
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"\n",
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"In this tutorial, you learn how to train and make predictions on an AutoML model based on a tabular dataset. Alternatively, you can train and make predictions on models by using the `gcloud` command-line tool or by using the online Cloud Console.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services and resources:\n",
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"\n",
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"- Vertex AI\n",
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"- AutoML Tabular\n",
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"\n",
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"The steps performed include the following:\n",
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"\n",
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"- Create a Vertex AI model training job.\n",
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@@ -122,7 +127,9 @@
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"id": "install_aip"
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},
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"source": [
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"## Installation"
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"## Installation\n",
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"\n",
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"Install the packages required for executing this notebook."
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]
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},
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{
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@@ -135,55 +142,20 @@
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"source": [
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"import os\n",
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"\n",
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"# The Google Cloud Notebook product has specific requirements\n",
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"IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n",
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"# The Vertex AI Workbench Notebook product has specific requirements\n",
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"IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
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"IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
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" \"/opt/deeplearning/metadata/env_version\"\n",
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")\n",
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"\n",
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"# Google Cloud Notebook requires dependencies to be installed with '--user'\n",
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"# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
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"USER_FLAG = \"\"\n",
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"if IS_GOOGLE_CLOUD_NOTEBOOK:\n",
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" USER_FLAG = \"--user\""
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "b03b7f4487ff"
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},
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"source": [
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"Install the latest version of the Vertex AI client library.\n",
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"if IS_WORKBENCH_NOTEBOOK:\n",
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" USER_FLAG = \"--user\"\n",
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"\n",
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"Run the following command in your virtual environment to install the Vertex SDK for Python:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "d489d38261dd"
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},
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"outputs": [],
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"source": [
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"! pip install {USER_FLAG} --upgrade google-cloud-aiplatform"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "install_storage"
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},
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"source": [
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"Install the Cloud Storage library:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"id": "qssss-KSlugo"
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},
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"outputs": [],
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"source": [
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"! pip install {USER_FLAG} --upgrade google-cloud-storage"
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"# Install the packagesimport os\n",
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"! pip3 install {USER_FLAG} -q --upgrade google-cloud-aiplatform \\\n",
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" google-cloud-storage"
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]
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},
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{
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@@ -29,7 +29,7 @@
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"id": "mThXALJl9Yue"
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},
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"source": [
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"# Tabular Workflow: AutoML Tabular Pipeline\n",
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"# AutoML Tabular Workflow pipelines\n",
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"\n",
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"<table align=\"left\">\n",
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" <td>\n",
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@@ -72,7 +72,12 @@
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"source": [
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"### Objective\n",
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"\n",
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"In this tutorial, you learn how to create two regression models using [Vertex Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) downloaded from [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction) (GCPC). These pipelines will be Vertex AI Tabular Workflow pipelines which are maintained by Google. These pipelines will showcase different ways to customize the Vertex Tabular training process.\n",
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"In this tutorial, you learn how to create two regression models using [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) downloaded from [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction) (GCPC). These pipelines will be Vertex AI Tabular Workflow pipelines which are maintained by Google. These pipelines will showcase different ways to customize the Vertex Tabular training process.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services:\n",
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"\n",
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"- `AutoML Training`\n",
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"- `Vertex AI Datasets`\n",
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"\n",
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"The steps performed are:\n",
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"\n",
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@@ -64,6 +64,32 @@
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"This tutorial demonstrates how to use the Vertex AI SDK for Python to create hierarchical forecasting models using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users)and do batch prediction. Specifically, you predict a fictional store's sales based on historical sales data."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "objective:automl,training,online_prediction"
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},
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"source": [
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"### Objective\n",
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"\n",
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"In this tutorial, you create an AutoML hierarchical forecasting model and deploy it for batch prediction using the Vertex AI SDK for Python. You can alternatively create and deploy models using the `gcloud` command-line tool or batch using the Cloud Console.\n",
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"The rationale for a hierarchical forecasting model is to minimize the error for a given group of sales data. In this tutorial, you will be minimizing the error for sale predictions at the \"product\" level.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services:\n",
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"\n",
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"- `AutoML Training`\n",
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"- `Vertex AI Datasets`\n",
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"\n",
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"The steps performed include:\n",
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"\n",
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"- Create a Vertex AI `TimeSeriesDataset` resource.\n",
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"- Train the model.\n",
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"- View the model evaluation.\n",
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"- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
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"- Make a prediction.\n",
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"- Undeploy the `Model`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -90,27 +116,6 @@
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"Link to data: gs://cloud-samples-data/vertex-ai/structured_data/forecasting/synthetic_sales_data.csv"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "objective:automl,training,online_prediction"
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},
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"source": [
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"### Objective\n",
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"\n",
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"In this tutorial, you create an AutoML hierarchical forecasting model and deploy it for batch prediction using the Vertex AI SDK for Python. You can alternatively create and deploy models using the `gcloud` command-line tool or batch using the Cloud Console.\n",
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"The rationale for a hierarchical forecasting model is to minimize the error for a given group of sales data. In this tutorial, you will be minimizing the error for sale predictions at the \"product\" level.\n",
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"\n",
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"The steps performed include:\n",
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"\n",
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"- Create a Vertex AI `TimeSeriesDataset` resource.\n",
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"- Train the model.\n",
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"- View the model evaluation.\n",
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"- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
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"- Make a prediction.\n",
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"- Undeploy the `Model`."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -66,17 +66,6 @@
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"This tutorial demonstrates how to use the Vertex AI SDK to create image object detection models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dataset:salads,iod"
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},
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"source": [
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"### Dataset\n",
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"\n",
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"The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -87,6 +76,11 @@
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"\n",
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"In this tutorial, you create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services:\n",
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"\n",
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"- `AutoML Training`\n",
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"- `Vertex AI Datasets`\n",
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"\n",
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"The steps performed include:\n",
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"\n",
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"- Create a Vertex `Dataset` resource.\n",
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@@ -101,6 +95,17 @@
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"* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "dataset:salads,iod"
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},
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"source": [
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"### Dataset\n",
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"\n",
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"The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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@@ -74,7 +74,7 @@
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"\n",
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"In this tutorial, you learn how to create an AutoML tabular regression model and deploy it for batch prediction using the Vertex AI SDK for Python. You can alternatively create and deploy models using the `gcloud` command-line tool or batch using the Cloud Console.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services and resources:\n",
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"This tutorial uses the following Google Cloud ML services:\n",
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"\n",
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"- Vertex AI Datasets (Tabular)\n",
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"- Vertex AI Training (AutoML Tabular Training)\n",
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@@ -73,7 +73,12 @@
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"source": [
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"### Objective\n",
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"\n",
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"In this tutorial, you learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
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"In this tutorial, you learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
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"\n",
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"This tutorial uses the following Google Cloud ML services:\n",
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"\n",
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"- `AutoML Training`\n",
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"- `Vertex AI Datasets`\n",
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"\n",
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"The steps performed include:\n",
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"\n",
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