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

[sysph]

Hi there, 

Is it possible to use Virtual environment (venv) for BigQuery notebook? Usually we use cli prompt to create and use the venv.  Do i create and use the venv via GCP cli or directly in BigQuery notebook?

The image below shows the a new venv is created (execute directly in BigQuery notebook. Seems have pre-installed packages. I didn't need to install virtualenv package). Does this mean this BQ notebook will be running in the new venv if i activate it? 

Thanks

[ms4446 · Accepted solution]

Yes, it is possible to use a virtual environment (venv) in Google Cloud, but the approach differs slightly when working with BigQuery notebooks compared to a local development environment.

Creating and Using venv through GCP CLI:

• This method is more applicable when working on a local machine or a virtual machine (VM) in Google Cloud, rather than directly within BigQuery notebooks.
• To create a venv on a VM or local machine, you would use the Cloud Shell or your local terminal, navigate to your project directory, and use commands like virtualenv venv_name and source venv_name/bin/activate.
• After activating the venv, you can install required packages using pip and run Jupyter Notebook or JupyterLab within this environment.
• However, this approach is not directly applicable to BigQuery notebooks, which run in a managed JupyterLab environment provided by Google Cloud.

Creating and Using venv within BigQuery Notebook:

• BigQuery notebooks are managed environments, and typically, they do not support the creation of virtual environments in the same way as a local setup.
• While you can install additional Python packages directly in a notebook cell using pip, creating a virtual environment within the notebook itself is not standard practice and might not be supported.
• The usual approach in BigQuery notebooks is to use the provided environment and install any additional packages as needed within the notebook.

Considerations for Managed JupyterLab Environments:

• In managed environments like BigQuery notebooks, the concept of virtual environments is less relevant because the environment is already isolated and managed by Google Cloud.
• For most use cases in BigQuery notebooks, managing packages directly within the notebook is sufficient and aligns with the managed nature of the environment.

While virtual environments are a key tool in Python development, their application in BigQuery notebooks in Google Cloud is limited due to the managed nature of these environments. The focus in such environments is typically on simplicity and ease of use, with less emphasis on the isolation and customization options that virtual environments provide in other contexts.

[ms4446 · Accepted solution]

When you activate a virtual environment (venv) and then check which Python interpreter you're using, if the output shows /usr/local/bin/python instead of a path within your virtual environment (like myenv1/bin/python), it indicates that the activation of the virtual environment might not have been successful, or your shell is still referencing the system-wide Python interpreter. 

To troubleshoot this issue:

• Re-activate the Virtual Environment: Try deactivating (deactivate) and re-activating (source myenv1/bin/activate) the virtual environment. Check the Python interpreter again using which python or python --version.

• Check the Virtual Environment Setup: Ensure that the virtual environment was created correctly and that the activate script exists in the expected directory.

• Examine the PATH Variable: After activation, examine your PATH variable (echo $PATH on Unix-like systems) to see if the virtual environment's bin directory is at the beginning of the PATH.

• Consider the Environment: If you're working in a GCP environment, be aware of the limitations and default configurations that might affect how virtual environments work.

[ms4446 · Accepted solution]

Yes, it is possible to use a virtual environment (venv) in Google Cloud, but the approach differs slightly when working with BigQuery notebooks compared to a local development environment.

Creating and Using venv through GCP CLI:

• This method is more applicable when working on a local machine or a virtual machine (VM) in Google Cloud, rather than directly within BigQuery notebooks.
• To create a venv on a VM or local machine, you would use the Cloud Shell or your local terminal, navigate to your project directory, and use commands like virtualenv venv_name and source venv_name/bin/activate.
• After activating the venv, you can install required packages using pip and run Jupyter Notebook or JupyterLab within this environment.
• However, this approach is not directly applicable to BigQuery notebooks, which run in a managed JupyterLab environment provided by Google Cloud.

Creating and Using venv within BigQuery Notebook:

• BigQuery notebooks are managed environments, and typically, they do not support the creation of virtual environments in the same way as a local setup.
• While you can install additional Python packages directly in a notebook cell using pip, creating a virtual environment within the notebook itself is not standard practice and might not be supported.
• The usual approach in BigQuery notebooks is to use the provided environment and install any additional packages as needed within the notebook.

Considerations for Managed JupyterLab Environments:

• In managed environments like BigQuery notebooks, the concept of virtual environments is less relevant because the environment is already isolated and managed by Google Cloud.
• For most use cases in BigQuery notebooks, managing packages directly within the notebook is sufficient and aligns with the managed nature of the environment.

While virtual environments are a key tool in Python development, their application in BigQuery notebooks in Google Cloud is limited due to the managed nature of these environments. The focus in such environments is typically on simplicity and ease of use, with less emphasis on the isolation and customization options that virtual environments provide in other contexts.

[sysph]

After activating, i check which python i'm using.  it's not in the venv myenv1 but the usr/local/bin/python. Is it using the default virtual environment in GCP or the local machine?  thanks you @ms4446 

[sysph]

I guess colab enterprise notebook in Vertex AI will be the same?  How about Workbench JupyterLab notebook in Vertex AI?  

[ms4446 · Accepted solution]

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.

[ms4446 · Accepted solution]

When you activate a virtual environment (venv) and then check which Python interpreter you're using, if the output shows /usr/local/bin/python instead of a path within your virtual environment (like myenv1/bin/python), it indicates that the activation of the virtual environment might not have been successful, or your shell is still referencing the system-wide Python interpreter. 

To troubleshoot this issue:

• Re-activate the Virtual Environment: Try deactivating (deactivate) and re-activating (source myenv1/bin/activate) the virtual environment. Check the Python interpreter again using which python or python --version.

• Check the Virtual Environment Setup: Ensure that the virtual environment was created correctly and that the activate script exists in the expected directory.

• Examine the PATH Variable: After activation, examine your PATH variable (echo $PATH on Unix-like systems) to see if the virtual environment's bin directory is at the beginning of the PATH.

• Consider the Environment: If you're working in a GCP environment, be aware of the limitations and default configurations that might affect how virtual environments work.