The `google-cloud-notebooks` client library provides Python access to the Google Cloud AI Platform Notebooks API. This managed service offers an integrated and secure JupyterLab environment for data scientists and machine learning developers to experiment, develop, and deploy models into production. The library is currently at version 1.16.0 and is part of the broader `google-cloud-python` ecosystem, which maintains a frequent release cadence, often with monthly updates for various client libraries.
pip install google-cloud-notebooksVerified import paths — ran on the pinned version, not inferred.
This quickstart initializes the `NotebookServiceClient` and lists existing Notebook instances in a specified Google Cloud project and location. Ensure your `GCP_PROJECT_ID` and `GCP_LOCATION` environment variables are set, or replace the placeholder values. Authentication typically relies on Application Default Credentials, which can be set up using `gcloud auth application-default login` for local development.
Upgrade your Python environment to 3.9 or a newer supported version (e.g., `python3.9 -m pip install google-cloud-notebooks`).
Review log contents before saving or exposing them. Avoid relying on the immutability of log message formats for automated parsing.
Use `gcloud auth application-default login` on your local machine or VM, or `from google.colab import auth; auth.authenticate_user()` in Colab, to configure credentials. Do not commit service account key files to version control.
Implement retry logic with exponential backoff for API calls. Consider increasing the request `timeout` parameter if the issue persists, as shown in some examples.
Ensure the library is installed (`pip install google-cloud-notebooks`) and that you are running your code in the same Python environment where it was installed. If using virtual environments, activate it before running your script.
Run `gcloud auth application-default login` in your terminal to set up credentials for local development. Ensure the 'Notebooks API' is enabled in your Google Cloud project for the target region via the Google Cloud Console.