How to Open and Run Jupyter Notebooks on Android: The 4 Best Methods (2026)

2026-06-21 · Source: Data Science on Medium · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning, Data Science & Analytics · Depth: Intermediate, medium

Summary

This guide, updated for 2026, outlines four primary methods for opening and running Jupyter notebooks on Android phones or tablets. Callisto, a native app, offers zero-setup, offline execution with a curated set of common data science libraries, ideal for beginners and quick edits. For power users needing a full Linux environment and unrestricted package installation, Termux combined with Jupyter provides this flexibility, though it demands significant setup and maintenance. Cloud notebooks like Google Colab and Kaggle Notebooks offer access to powerful compute and free GPUs via a mobile browser, suitable for heavy computation but requiring an internet connection. Lastly, remote access via SSH or similar tools allows users to connect to an existing Jupyter server on another machine, leveraging its full power and configured environment.

Key takeaway

For machine learning engineers or students needing to run Jupyter notebooks on the go, evaluate your specific requirements for package flexibility, compute power, and internet access. If you prioritize ease and offline capability, start with a native app like Callisto. However, if you require full package control or GPU acceleration, consider Termux or cloud solutions like Google Colab, respectively. Choose the method that best aligns with your project's resource demands and connectivity constraints.

Key insights

Running Jupyter notebooks on Android offers diverse options, balancing convenience, power, and connectivity based on user needs.

Principles

• On-device execution enables offline use.
• Full package flexibility requires Linux environments.
• Cloud notebooks provide scalable compute and GPUs.

Method

The article describes four distinct approaches: native app (Callisto), Linux terminal emulator (Termux + Jupyter), cloud-based platforms (Colab/Kaggle), and remote access to an existing server. Each involves different setup and resource requirements.

In practice

• Use Callisto for quick, offline Python learning.
• Employ Termux for custom C-extension libraries.
• Access Colab for GPU-accelerated model training.

Topics

• Jupyter Notebooks
• Android Development
• Mobile Computing
• Python Programming
• Google Colab
• Termux

Best for: AI Student, Machine Learning Engineer, Software Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Data Science on Medium.