Is it possible to use Virtual environment (venv) for BigQuery notebook?

Google Cloud Data Analytics
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In Google Cloud’s Vertex AI, both Colab Enterprise notebooks and Workbench JupyterLab notebooks are managed environments, similar in some respects to BigQuery notebooks. However, there are some differences in how these environments handle Python virtual environments:

  1. Colab Enterprise Notebooks in Vertex AI:

    • Colab notebooks, including those in Vertex AI, are designed to be easy to use and manage, with a focus on data science and machine learning tasks.
    • They typically come with a pre-configured environment that includes a range of data science and machine learning libraries.
    • While it’s possible to install additional Python packages directly in a notebook cell, the use of virtual environments in Colab is not as straightforward as on a local machine. The environment is more controlled, and certain system-level changes (like changing the Python interpreter) might not be supported or persistent.
    • If you need to use a specific set of Python packages with specific versions, you can install them at the beginning of your Colab notebook session, but these installations will be temporary and need to be redone in each new session.

  2. Workbench JupyterLab Notebooks in Vertex AI:

    • Workbench JupyterLab notebooks in Vertex AI offer a more flexible and customizable environment compared to Colab notebooks.
    • These notebooks allow for more control over the Python environment, including the ability to create and use virtual environments.
    • You can create a virtual environment within a Workbench notebook using standard Python tools like venv or virtualenv. Once created and activated, you can install and manage packages within this environment.
    • The virtual environment in a Workbench notebook should persist across sessions, making it a more suitable choice for projects that require a specific setup or dependencies.
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