How to install a custom library in Jupyter notebook of GCP?

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06-10-2024

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Installing Custom Libraries in Jupyter Notebooks on Google Cloud Platform (GCP)

Running Jupyter notebooks on GCP can be incredibly powerful for data science tasks. However, often you'll need to install custom libraries to utilize specific functionalities. This article guides you through the process of installing libraries within your GCP environment.

The Problem: Installing Libraries in GCP

Imagine you're working on a project that requires a library not pre-installed in your GCP Jupyter Notebook environment. You can't simply use pip install as you would locally.

Scenario: You're working on a data analysis project that requires the beautifulsoup4 library for web scraping. You need to install it within your GCP Jupyter Notebook environment.

Original Code (Incorrect):

!pip install beautifulsoup4

This code, while seemingly simple, won't work within a GCP Jupyter Notebook environment.

Solution: Utilizing Google Cloud SDK

The most efficient way to install libraries within a GCP Jupyter Notebook is by using the Google Cloud SDK. Here's a step-by-step guide:

1. Install the Google Cloud SDK: Download and install the SDK from the official website (https://cloud.google.com/sdk/docs).

2. Authenticate: After installation, authenticate your Google Cloud account using the command:

   gcloud auth login
   

   Follow the prompts to authorize the SDK.

3. Create a Virtual Environment (Optional): Creating a virtual environment isolates your project's dependencies and helps manage conflicts.

   python3 -m venv .venv
   source .venv/bin/activate
   

4. Install the Library: Once you have your environment set up, use the gcloud command to install the library:

   gcloud compute instances ssh [INSTANCE_NAME] --zone [ZONE] --project [PROJECT_ID] --command "python3 -m pip install beautifulsoup4" 
   

   Replace [INSTANCE_NAME], [ZONE], and [PROJECT_ID] with your specific instance details.

Explanation:

• The command gcloud compute instances ssh connects you to your GCP instance.
• --zone specifies the region where your instance is located.
• --project identifies your GCP project.
• --command runs the specified command within the instance.

5. Restart the Kernel: After successful installation, restart the Jupyter Notebook kernel to load the new library.

Additional Tips and Best Practices

• Dependency Management: For complex projects, consider using tools like requirements.txt to list your dependencies for easier installation and management.
• Environment Variables: To avoid repetitive commands, consider using environment variables to store your GCP project ID, zone, and instance name for quicker access.
• Use a Cloud Shell: For smaller projects, GCP's Cloud Shell offers a pre-configured environment with the gcloud command readily available.

Conclusion

Installing custom libraries in your GCP Jupyter Notebooks is essential for expanding your data science capabilities. By utilizing the gcloud command and understanding the process, you can easily integrate any necessary libraries into your GCP environment.

Remember to always explore best practices and utilize tools like environment variables and requirements.txt for seamless management of your project dependencies. Happy coding!