Jupyter notebook vs Google colab

Jupyter notebook vs Google colab

Jupyter Notebook and Google Colab are both popular tools for working with Python code, particularly in data science and machine learning.

Jupyter Notebook:

• Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text.
• It supports various programming languages, but it’s primarily used with Python.
• You can run Jupyter Notebook locally on your machine after installing it through Anaconda or directly via pip.
• It offers more control over your environment and data, as you can work offline and customize your setup as needed.
• However, it might require more setup and computing resources, especially for resource-intensive tasks.

Google Colab:

• Google Colab (short for Colaboratory) is a free cloud-based platform provided by Google that offers a Jupyter Notebook environment with GPU and TPU support.
• It allows you to write and execute Python code directly in the browser, with access to Google Drive for storing and sharing notebooks.
• Colab provides free GPU and TPU resources, which can be very beneficial for training machine learning models and handling large datasets without the need for powerful local hardware.
• Collaboration is easy since you can share notebooks just by sending a link, and multiple users can work on the same notebook simultaneously.
• However, it has some limitations in terms of resource availability and session timeouts, and you need an internet connection to use it.

Jupyter Notebook offers more control and flexibility, especially for offline work, while Google Colab provides convenience and access to powerful computing resources in the cloud. The choice between them depends on your specific needs and preferences.

Which one are you going to use (using)?