The Labelbox Python SDK provides a client for interacting with the Labelbox API, allowing users to programmatically manage datasets, projects, annotations, models, and workflows. It's designed for data scientists and MLOps engineers to integrate Labelbox into their machine learning pipelines. The library is actively developed, with frequent updates adding new features and improvements.
pip install labelboxVerified import paths — ran on the pinned version, not inferred.
Initializes the Labelbox client using an API key from an environment variable and lists available projects. This demonstrates basic connectivity and resource retrieval.
Refer to the official upgrade guide for version 7.0.0. Pay close attention to changes in `Project`, `Model`, and `Workflow` related classes and methods.
Set the `LB_API_KEY` environment variable (e.g., `export LB_API_KEY='YOUR_API_KEY'`) before running your script, and retrieve it using `os.environ.get('LB_API_KEY')`.Always iterate over the returned generator object or explicitly handle pagination parameters (e.g., `skip`, `first`) if available, to ensure all results are processed. For example, `for data_row in client.get_data_rows(): ...` will correctly fetch all pages.
Update imports like `from labelbox.schema.model import Model` to `from labelbox import Model` or refer to the latest documentation for correct paths.
Set the environment variable: `export LB_API_KEY='YOUR_API_KEY'` (Linux/macOS) or `$env:LB_API_KEY='YOUR_API_KEY'` (PowerShell) before running your script, or pass the key directly to `Client(api_key='YOUR_API_KEY')`.
Consult the latest SDK documentation or `labelbox` GitHub repository for the correct method name and usage. For example, `get_labelbox_objects` was likely replaced by more specific `get_projects()`, `get_datasets()`, etc.
Pass the API key directly as a string to the `api_key` argument, or use the `LB_API_KEY` environment variable. `Client(api_key=your_api_key_string)`.