Data Labeling: Public API for Google Cloud AI Data Labeling Service.
Quick Start
In order to use this library, you first need to go through the following steps:
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-datalabelingWindows
py -m venv <your-env>
.\<your-env>\Scripts\activate
pip install google-cloud-datalabelingNext Steps
Read the Client Library Documentation for Data Labeling to see other available methods on the client.
Read the Data Labeling 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:
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.
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.
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.Invalid logging scopes:
foo,123, etc.
NOTE: If the logging scope is invalid, the library does not set up any logging handlers.
Environment-Based Examples
Enabling the default handler for all Google-based loggers
export GOOGLE_SDK_PYTHON_LOGGING_SCOPE=googleEnabling the default handler for a specific Google module (for a client library called
library_v1):
export GOOGLE_SDK_PYTHON_LOGGING_SCOPE=google.cloud.library_v1Advanced, code-based configuration
You can also configure a valid logging scope using Python’s standard logging mechanism.
Code-Based Examples
Configuring a handler for all Google-based loggers
import logging
from google.cloud import library_v1
base_logger = logging.getLogger("google")
base_logger.addHandler(logging.StreamHandler())
base_logger.setLevel(logging.DEBUG)Configuring a handler for a specific Google module (for a client library called
library_v1):
import logging
from google.cloud import library_v1
base_logger = logging.getLogger("google.cloud.library_v1")
base_logger.addHandler(logging.StreamHandler())
base_logger.setLevel(logging.DEBUG)Logging details
Regardless of which of the mechanisms above you use to configure logging for this library, by default logging events are not propagated up to the root logger from the google-level logger. If you need the events to be propagated to the root logger, you must explicitly set
logging.getLogger("google").propagate = Truein your code.You can mix the different logging configurations above for different Google modules. For example, you may want use a code-based logging configuration for one library, but decide you need to also set up environment-based logging configuration for another library.
If you attempt to use both code-based and environment-based configuration for the same module, the environment-based configuration will be ineffectual if the code -based configuration gets applied first.
The Google-specific logging configurations (default handlers for environment-based configuration; not propagating logging events to the root logger) get executed the first time any client library is instantiated in your application, and only if the affected loggers have not been previously configured. (This is the reason for 2.i. above.)
2 maintainers
Release files for google-cloud-datalabeling 1.17.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| google_cloud_datalabeling-1.17.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 240.0 kB
Release files / google_cloud_datalabeling-1.17.0.tar.gz
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