Python Client for Cloud AutoML

Cloud AutoML: Train high-quality custom machine learning models with minimum effort and machine learning expertise.

• Client Library Documentation

• Product Documentation

Quick Start

In order to use this library, you first need to go through the following steps:

1. Select or create a Cloud Platform project.

2. Enable billing for your project.

3. Enable the Cloud AutoML.

4. Set up Authentication.

Installation

Install this library in a virtual environment using venv. venv is a tool that creates isolated Python environments. These isolated environments can have separate versions of Python packages, which allows you to isolate one project’s dependencies from the dependencies of other projects.

With venv, it’s possible to install this library without needing system install permissions, and without clashing with the installed system dependencies.

Code samples and snippets

Code samples and snippets live in the samples/ folder.

Supported Python Versions

Our client libraries are compatible with all current active and maintenance versions of Python.

Python >= 3.10, including 3.14

Unsupported Python Versions

Python <= 3.9

If you are using an end-of-life version of Python, we recommend that you update as soon as possible to an actively supported version.

Mac/Linux

python3 -m venv <your-env>
source <your-env>/bin/activate
pip install google-cloud-automl

Windows

py -m venv <your-env>
.\<your-env>\Scripts\activate
pip install google-cloud-automl

Next Steps

• Read the Client Library Documentation for Cloud AutoML to see other available methods on the client.

• Read the Cloud AutoML Product documentation to learn more about the product and see How-to Guides.

• View this README to see the full list of Cloud APIs that we cover.

Logging

This library uses the standard Python logging functionality to log some RPC events that could be of interest for debugging and monitoring purposes. Note the following:

1. Logs may contain sensitive information. Take care to restrict access to the logs if they are saved, whether it be on local storage or on Google Cloud Logging.

2. Google may refine the occurrence, level, and content of various log messages in this library without flagging such changes as breaking. Do not depend on immutability of the logging events.

3. By default, the logging events from this library are not handled. You must explicitly configure log handling using one of the mechanisms below.

Simple, environment-based configuration

To enable logging for this library without any changes in your code, set the GOOGLE_SDK_PYTHON_LOGGING_SCOPE environment variable to a valid Google logging scope. This configures handling of logging events (at level logging.DEBUG or higher) from this library in a default manner, emitting the logged messages in a structured format. It does not currently allow customizing the logging levels captured nor the handlers, formatters, etc. used for any logging event.

A logging scope is a period-separated namespace that begins with google, identifying the Python module or package to log.

• Valid logging scopes: google, google.cloud.asset.v1, google.api, google.auth, etc.