⚡ Technical Documentation

Running Jupyter And Colab Notebooks Locally: A Comprehensive Guide

Technical guide covering **running jupyter and colab notebooks locally: a comprehensive guide**

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Cosmic Lounge AI Team
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Updated
6/1/2025
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13 min
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#ai #tensorflow #docker #api #server #setup #configuration #development #introduction

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🌌 Running Jupyter and Colab Notebooks Locally: A Comprehensive Guide

This report details the various methods for executing Jupyter notebooks locally, with a particular focus on running notebooks downloaded from Google Colab. It addresses the feasibility of command-line execution, the necessity of a graphical user interface (GUI), considerations for the Windows Subsystem for Linux (WSL) Debian environment, required Python libraries, illustrative code examples, methods for interacting with running notebooks, and specific aspects related to Colab notebooks.

🌟 I. Introduction to Local Jupyter Notebook Execution

🚀 Welcome to this comprehensive guide! This section will give you the foundational knowledge you need. Jupyter Notebooks have become a cornerstone of data science, machine learning, and scientific computing, providing an interactive environment for code execution, data visualization, and documentation. While platforms like Google Colab offer cloud-based execution, users often need to run these notebooks locally on their personal machines. This necessity can arise from various factors, including the need to work offline, handle sensitive data that cannot be uploaded to the cloud, or leverage local hardware resources 1.

⚡ II. Core Methods for Running Jupyter Notebooks Locally

Several methods exist for running Jupyter notebooks on a local machine. The most fundamental approach involves launching the Jupyter Notebook application, which typically opens in a web browser 3. This application provides a dashboard from which users can navigate their file system, open existing notebooks, or create new ones. The command jupyter notebook is the standard way to initiate this server from the command line 3. Upon execution, the terminal will display information about the server, including the URL (usually http://localhost:8888) that can be used to access the notebook interface in a browser 4.

⚡ III. Command-Line Execution of Jupyter Notebooks

The user specifically inquired about running Jupyter notebooks from the command line. This is indeed possible and offers a non-interactive way to execute notebooks, which can be beneficial for automation or batch processing. The jupyter execute subcommand provides a direct method for this purpose 4. To execute a single notebook, the command is jupyter execute notebook.ipynb 4. Multiple notebooks can be executed by providing their paths as arguments, such as jupyter execute notebook1.ipynb notebook2.ipynb 4. By default, any errors encountered during the execution will be raised and printed in the terminal. Another powerful tool for command-line execution is nbconvert, which is included with Jupyter Notebook 5. This tool can execute a notebook and overwrite the existing file with the results using the command: jupyter nbconvert —execute —to notebook —inplace your-notebook.ipynb 5. Alternatively, the executed notebook can be saved to a new file: jupyter nbconvert —execute —to notebook your-notebook.ipynb 5. Custom output names and directories can also be specified 5. Like jupyter execute, nbconvert also offers the —allow-errors flag 5.

Featurejupyter executenbconvert
Basic ExecutionSimple command for running a notebook.Executes and can save the executed notebook.
Multiple NotebooksExecutes several notebooks at once.Can execute multiple notebooks.
Error HandlingRaises errors by default; option to continue.Stops on first error by default; option to continue.
Output FormatPrimarily focuses on execution; saves executed .ipynb.Can save executed .ipynb and convert to various other formats (HTML, PDF).
ConversionNo direct conversion to other formats.Primary tool for notebook conversion.
In-place ExecutionNot a primary feature.Supports overwriting the original notebook.
ParameterizationLimited; papermill recommended for advanced needs.Limited; papermill recommended for advanced needs.

⚡ IV. Graphical User Interface (GUI) Requirement

The question of whether a GUI is required to run Jupyter notebooks locally has a nuanced answer. The traditional way of interacting with Jupyter notebooks involves a web-based GUI accessible through a browser 3. When the jupyter notebook command is executed, it launches a server that renders the notebook interface in the user’s default web browser 3. However, for command-line execution using tools like jupyter execute and nbconvert, a GUI is not a prerequisite 4. These tools execute the notebook non-interactively and provide output either in the terminal or in specified output files.

⚡ V. Running Jupyter Notebooks in WSL Debian

Executing Jupyter notebooks within the Windows Subsystem for Linux (WSL) Debian environment is a common scenario for developers who prefer a Linux environment while working on a Windows machine. Several resources provide guidance on setting this up 7. The general steps involve installing Python and pip within the WSL Debian environment, followed by installing the jupyter package using pip3 install jupyter 8. A common issue encountered in WSL is that Jupyter Notebook might not automatically open a browser window in Windows. This is often addressed by configuring an alias in the BASH configuration file (~/.bashrc) to launch Jupyter without a browser using the —no-browser option 7. Users can then manually open their Windows browser and navigate to the URL provided in the WSL terminal (usually http://localhost:8888) 7. Occasionally, users might encounter a “tcgetpgrp failed: Not a tty” error or a black screen in the browser. Workarounds include setting the BROWSER environment variable or manually copying and pasting the localhost URL along with the token from the WSL terminal into the browser 11.

⚡ VI. Necessary Python Libraries

To run Jupyter notebooks locally, the core library required is jupyter itself, which can be installed using pip: pip install jupyter 8. This package includes the Jupyter Notebook application, the jupyter execute command, and other essential components. For command-line execution with conversion capabilities, the nbconvert tool is also necessary, and it is typically installed as part of the jupyter package 5. For more advanced use cases, such as parameterizing notebooks for command-line execution, the papermill library is highly recommended 4. It can be installed via pip: pip install papermill 5. When working with Colab notebooks locally, the colab Python package (pip install colab) might be considered, although its primary function appears to be related to connecting to Colab’s local runtime feature 1.

LibraryDescriptionKey Use Cases
jupyterCore package for running Jupyter Notebooks.Starting the Jupyter Notebook server, basic command-line execution (jupyter execute).
nbconvertTool for converting Jupyter notebooks to various formats.Non-interactive command-line execution, exporting notebooks to HTML, PDF, Python scripts, etc.
papermillLibrary for parameterizing and executing Jupyter notebooks programmatically.Executing notebooks with parameters from the command line or within Python scripts, batch processing of notebooks with different configurations.
colabProvides some compatibility with Google Colab for local execution.Primarily used in conjunction with Colab’s local runtime feature, may offer some utilities for local Colab notebook execution.

⚡ VII. Python Code Examples for Command-Line Execution

While the primary method for command-line execution involves using the jupyter command directly in the terminal, Python’s subprocess module can also be used to programmatically execute notebooks. This allows for integrating notebook execution into larger Python scripts or automated workflows.

  • A. Using the subprocess module: Python import subprocess

notebook_path = “path/to/your/colab_notebook.ipynb”

try:

🌌 Construct the command to execute the notebook

command = [“jupyter”, “execute”, notebook_path]

🌌 Execute the command

process = subprocess.run(command, capture_output=True, text=True, check=True)

print(“Notebook executed successfully.”) print(“Stdout:”, process.stdout) if process.stderr: print(“Stderr:”, process.stderr)

except subprocess. CalledProcessError as e: print(f”Error executing notebook: {e}”) print(“Stdout:”, e.stdout) print(“Stderr:”, e.stderr) except FileNotFoundError: print(f”Error: Jupyter command not found. Make sure it’s in your PATH.”) except Exception as e: print(f”An unexpected error occurred: {e}”)

The subprocess.run() function executes the specified command. The capture_output=True argument captures the output and error streams, while text=True decodes the output as text. Setting check=True will raise a CalledProcessError if the jupyter execute command returns a non-zero exit code, indicating an error 4.

  • B. Advanced execution with nbconvert API: Python import nbformat from nbconvert.preprocessors import ExecutePreprocessor

notebook_path = “path/to/your/colab_notebook.ipynb”

with open(notebook_path) as f: nb = nbformat.read(f, as_version=4)

🌌 Configure the execution preprocessor

🌌 You can specify the kernel name, timeout, etc.

ep = ExecutePreprocessor(kernel_name=‘python3’, timeout=600)

try:

🌌 Preprocess the notebook to execute the code cells

out, resources = ep.preprocess(nb, {‘metadata’: {‘path’: ’.’}}) print(“Notebook executed successfully.”)

🌌 Optionally save the executed notebook to a new file

🌌 with open(“executed_notebook.ipynb”, mode=“w”, encoding=“utf-8”) as f:

🌌 nbformat.write(out, f)

except Exception as e: print(f”Error executing notebook: {e}”)

This code snippet demonstrates using the Python API of nbconvert to execute a Jupyter notebook within a Python script. It first reads the notebook file using nbformat. Then, it creates an ExecutePreprocessor instance, which is responsible for executing the code cells. The preprocess() method takes the notebook object and a dictionary of resources as input. The kernel_name parameter specifies the kernel to use for execution.

⚡ VIII. Interacting with Command-Line Executed Notebooks

Interacting with Jupyter notebooks executed from the command line differs from the interactive GUI experience.

  • A. Passing parameters using papermill: Python import papermill as pm

input_notebook = “path/to/your/parameterized_notebook.ipynb” output_notebook = “executed_with_params.ipynb” params = {“input_data_file”: “data.csv”, “learning_rate”: 0.01}

try: pm.execute_notebook(input_notebook, output_notebook, parameters=params) print(f”Notebook executed with parameters. Output saved to {output_notebook}”) except Exception as e: print(f”Error executing notebook: {e}”)

The papermill library simplifies the process of programmatically running notebooks with different configurations. The pm.execute_notebook() function takes the input notebook path, the output notebook path, and a dictionary of parameters. These parameters will override any default values defined in the notebook cell tagged with “parameters” 4. This is particularly useful for tasks such as running experiments with varying hyperparameters or generating reports for different datasets.

  • B. Retrieving output from executed notebooks: When a notebook is executed via the command line, the primary way to retrieve output is by inspecting the executed notebook file itself, especially if the —to notebook option was used with nbconvert. The output cells within the .ipynb file will contain the results of the execution 5. If other formats like HTML or PDF were specified with nbconvert, the output will be in those respective files. When using the Python APIs (subprocess or nbconvert), standard output and standard error streams can be captured during execution, as demonstrated in the code examples. With papermill, the output is saved in the specified output notebook, which can then be read and processed further using Python libraries like nbformat.

⚡ IX. Running Colab Notebooks Locally: Key Considerations

While Colab notebooks are fundamentally Jupyter notebooks, some distinctions might require adjustments when running them locally.

  • A. Potential differences and adjustments needed: Colab notebooks may contain specific “magic commands” (e.g., %tensorflow_version, !apt-get install) or rely on libraries that are pre-installed in the Colab environment but might not be present in a standard local Jupyter setup 15. Users might need to install these missing dependencies locally using pip (pip install <package_name>). Colab’s “local runtime” feature provides a way to execute Colab notebooks using local resources while still using the Colab interface in the browser 17. This involves starting a local Jupyter server with specific configurations or using a Colab Docker image, and then connecting the Colab notebook running in the browser to this local backend. For the Jupyter runtime option, the command is typically: Bash jupyter notebook
    —NotebookApp.allow_origin=‘https://colab.research.google.com’
    —port=8888
    —NotebookApp.port_retries=0

For the Docker runtime option, after installing Docker, the command is: Bash docker run -p 127.0.0.1:9000:8080 us-docker.pkg.dev/colab-images/public/runtime

Users must be aware of the security implications when connecting Colab to a local runtime, as the Colab frontend gains the ability to execute code on the local machine with full file system access 17.

  • B. Running Colab notebooks from GitHub: Many Colab notebooks are hosted on GitHub repositories 18. If a downloaded Colab notebook is part of a larger project on GitHub, it might be necessary to clone the entire repository to the local WSL environment using git clone <repository_url> to ensure all dependent files are available.

⚡ X. Troubleshooting and Best Practices for WSL Debian

When running Jupyter notebooks in WSL Debian, several common issues and best practices should be considered.

  • A. Common issues and solutions (revisited): As mentioned earlier, issues like “tcgetpgrp failed” and black screens when starting Jupyter in WSL can often be resolved by setting the BROWSER environment variable or manually accessing http://localhost:8888 with the token provided in the terminal 11.

  • B. Virtual environments: Using Python virtual environments (e.g., with venv or conda) is highly recommended for managing dependencies for Jupyter projects within WSL Debian 1. Virtual environments help isolate project dependencies, preventing conflicts between different projects. To create a virtual environment using venv, the command is python3 -m venv myenv, and it can be activated using source myenv/bin/activate.

  • C. Path management: It is crucial to be mindful of file paths when working across the Windows and WSL file systems. Windows files are typically accessible under /mnt/c/… within WSL. Ensure that the paths specified in the notebooks correctly reflect the file locations within the WSL environment.

  • D. Keeping Jupyter and related packages updated: Regularly update Jupyter Notebook and related packages like nbconvert and papermill using pip: pip install —upgrade jupyter nbconvert papermill. This ensures access to the latest features, bug fixes, and security updates.

🌟 XI. Conclusion: Empowering Local Jupyter Notebook Execution

Running Jupyter and Colab notebooks locally from the command line within WSL Debian offers significant flexibility for various workflows. Direct execution using jupyter execute is suitable for simple, non-interactive runs, while nbconvert provides more advanced options for execution and format conversion. For closer integration with the Colab environment, the local runtime feature can be utilized, keeping in mind the associated security considerations. When working with Colab notebooks, it is important to be aware of potential differences in magic commands and pre-installed libraries, and to install any missing dependencies locally. For notebooks originating from GitHub, cloning the repository in WSL Debian ensures all necessary files are accessible. Adopting best practices such as using virtual environments and keeping packages updated contributes to a more stable and efficient development experience.

🔧 Works cited

1. How to Run Google Colab Locally: A Step-by-Step Guide | Saturn Cloud Blog, accessed on March 23, 2025, https://saturncloud.io/blog/how-to-run-google-colab-locally-a-step-by-step-guide/ 2. How to Run Google Colab Locally: A Step-by-Step Guide - LinkSprite, accessed on March 23, 2025, https://www.linksprite.com/how-to-run-google-colab-locally-a-step-by-step-guide/ 3. 3. Running the Jupyter Notebook, accessed on March 23, 2025, https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/execute.html 4. Running the Notebook — Jupyter Documentation 4.1.1 alpha documentation, accessed on March 23, 2025, https://docs.jupyter.org/en/latest/running.html 5. The 4 ways to run Jupyter Notebook in command line - MLJAR Studio, accessed on March 23, 2025, https://mljar.com/blog/run-jupyter-notebook-command-line/ 6. Start and auto execute a notebook from a commandline - Google Groups, accessed on March 23, 2025, https://groups.google.com/g/jupyter/c/ldjkTjEiNk8/m/F97Y7m5QCQAJ 7. Jupyter Notebook in Windows Subsystem for Linux (WSL) | Adam …, accessed on March 23, 2025, https://code.adonline.id.au/jupyter-notebook-in-windows-subsystem-for-linux-wsl/ 8. Installing Jupyter in WSL | Ganessh Kumar, accessed on March 23, 2025, https://www.ganesshkumar.com/articles/2024-04-05-installing-jupyter-in-wsl 9. Jupyter Notebook in Windows subsystem for Linux (WSL) | by Sayan Ghosh - Medium, accessed on March 23, 2025, https://medium.com/@sayanghosh_49221/jupyter-notebook-in-windows-subsystem-for-linux-wsl-f075f7ec8691 10. Jupyter Notebook in Windows Subsystem for Linux |WSL | by Harshit Yadav - Medium, accessed on March 23, 2025, https://harshityadav95.medium.com/jupyter-notebook-in-windows-subsystem-for-linux-wsl-8b46fdf0a536 11. Issue with Starting Jupyter Notebook from WSL, accessed on March 23, 2025, https://discourse.jupyter.org/t/issue-with-starting-jupyter-notebook-from-wsl/17441 12. Parameterize - papermill 2.4.0 documentation - Read the Docs, accessed on March 23, 2025, https://papermill.readthedocs.io/en/latest/usage-parameterize.html 13. colab · PyPI, accessed on March 23, 2025, https://pypi.org/project/colab/ 14. How to run a notebook using command line - nbconvert - Jupyter Community Forum, accessed on March 23, 2025, https://discourse.jupyter.org/t/how-to-run-a-notebook-using-command-line/3475 15. Run a Terminal in Google Colab (on the Free Tier) - Aswin van Woudenberg, accessed on March 23, 2025, https://www.aswinvanwoudenberg.com/posts/run-a-terminal-in-google-colab-on-the-free-tier/ 16. How to Install Python Package in Google’s Colab - GeeksforGeeks, accessed on March 23, 2025, https://www.geeksforgeeks.org/how-to-install-python-package-in-googles-colab/ 17. Local runtimes - Google Colab, accessed on March 23, 2025, https://research.google.com/colaboratory/local-runtimes.html 18. Basic tutorial on how to conveniently use Google Colab with Google Drive and Github, accessed on March 23, 2025, https://github.com/WiktorJ/google-colab-tutorial 19. How Can I Run Notebooks of a Github Project in Google Colab | Saturn Cloud Blog, accessed on March 23, 2025, https://saturncloud.io/blog/how-can-i-run-notebooks-of-a-github-project-in-google-colab/ 20. A quick workflow for Google Colab, Github and Jupyter notebooks on Mac. - Medium, accessed on March 23, 2025, https://medium.com/analytics-vidhya/a-quick-workflow-for-google-colab-github-and-jupyter-notebooks-on-mac-ff5d004e01f 21. How do I link a GitHub repository to a Google Colab notebook? - Stack Overflow, accessed on March 23, 2025, https://stackoverflow.com/questions/67553747/how-do-i-link-a-github-repository-to-a-google-colab-notebook 22. How to run Jupyter Notebook/Lab without Path being setup? : r/learnpython - Reddit, accessed on March 23, 2025, https://www.reddit.com/r/learnpython/comments/lqrjee/how_to_run_jupyter_notebooklab_without_path_being/

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