Skip to main content

A tool designed to execute all cells in a Jupyter Notebook using nbconvert’s ExecutePreprocessor, capturing outputs for testing and reporting.

Project description

Swamauri Logo

PyPI - Downloads GitHub Hits PyPI - Python Version PyPI - License PyPI - swarmauri_tool_jupyterexecutenotebook


Swarmauri Tool Jupyter Execute Notebook

The swarmauri_tool_jupyterexecutenotebook package provides a tool for executing all cells in a Jupyter notebook in sequence, capturing outputs and returning the fully updated NotebookNode object. It leverages the Swarmauri framework's base and core components.

Installation

To install swarmauri_tool_jupyterexecutenotebook, make sure you have Python 3.10 or later:

  1. Using pip: • (Recommended) Create and activate a virtual environment.
    • Run:
    pip install swarmauri_tool_jupyterexecutenotebook

  2. Using Poetry in an existing project: • poetry add swarmauri_tool_jupyterexecutenotebook

This will automatically install all dependencies required to run the JupyterExecuteNotebookTool.

Usage

The principal component of this package is the JupyterExecuteNotebookTool, which executes a given notebook, capturing cell outputs and errors. Below is a quick reference for using the tool programmatically in your Python code.

Example usage:


from swarmauri_tool_jupyterexecutenotebook import JupyterExecuteNotebookTool

def execute_my_notebook(): """ Demonstrates how to instantiate and use the JupyterExecuteNotebookTool to execute a Jupyter notebook file. This includes capturing outputs and error messages. """ # Create an instance of the tool tool = JupyterExecuteNotebookTool()

# Execute the Jupyter notebook; specify the path to your notebook
executed_notebook = tool(
    notebook_path="my_notebook.ipynb",
    timeout=60  # Optional: defaults to 30 if not provided
)

# The returned `executed_notebook` is a NotebookNode with outputs captured
return executed_notebook

if name == "main": result_notebook = execute_my_notebook() # You can further analyze 'result_notebook' outputs here

In this example: • The notebook_path parameter is a required string referencing the target notebook file.
• The optional timeout parameter defines how long each cell can take to run before throwing an error (default is 30 seconds).

The executed NotebookNode object will contain both new outputs and any error messages generated during execution.

Dependencies

This package relies on: • swarmauri_core for base components.
• swarmauri_base for the ToolBase class.
• nbconvert, nbformat, and nbclient for handling and executing Jupyter notebooks.

When you install swarmauri_tool_jupyterexecutenotebook via pip or Poetry, these dependencies are automatically handled for you. Refer to the project's pyproject.toml for the full list of dependencies and version requirements.


This README is provided as part of the swarmauri_tool_jupyterexecutenotebook package. If you have any questions or issues, please consult our documentation or open a support request. Thank you for using Swarmauri!

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

File details

Details for the file swarmauri_tool_jupyterexecutenotebook-0.7.1.dev1.tar.gz.

File metadata

File hashes

Hashes for swarmauri_tool_jupyterexecutenotebook-0.7.1.dev1.tar.gz
Algorithm Hash digest
SHA256 5bb3a23c65dd89c477c5038c1696100bff5ddf3acfc93803d7f39e7b206c804f
MD5 56b5007cae31fc7f147f05dcc46ccc45
BLAKE2b-256 882b4771d7b54af950ea46f1eaf5255c65746750b3e76a194bb0ea8058a010bd

See more details on using hashes here.

File details

Details for the file swarmauri_tool_jupyterexecutenotebook-0.7.1.dev1-py3-none-any.whl.

File metadata

File hashes

Hashes for swarmauri_tool_jupyterexecutenotebook-0.7.1.dev1-py3-none-any.whl
Algorithm Hash digest
SHA256 e1790f31a3879f9962ebe68855942adf4e8faf153aa4886b364ffa49d691e648
MD5 dffcb315abbed990ff58087b60428cc2
BLAKE2b-256 ef7217b885c1098acb432317e94a26e892c47e1d0297bea6065df50312aae0aa

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page