Sharing Jupyter notebooks can streamline collaboration, but often shared notebooks wont run or worse, run differently based on environment differences. So here are three areas to pay attention to when sharing notebooks:
1. Environment Differences
Python/Package Versions: Notebooks may fail if different Python versions or library versions are used. Replicating environments using package management systems like Conda or commands like pip freeze can help avoid these errors.
Virtual Environments: If the notebook isn’t running in the correct virtual environment, it may not have the necessary packages or settings, leading to various errors. Before reacting to a ModuleNotFoundError, double-check the virtual environment.
Environment Variables: Missing or misconfigured environment variables may cause failures. Sharing a requirements.txt (for pip) or environment.yml (for conda) file allows others to recreate the correct environment, ensuring all variables and dependencies are properly set.
2. Dependency and Configuration Issues
Missing Dependencies: Notebooks might import libraries that aren't installed on your machine. Comparing installed packages with pip list or again, using Conda helps ensure that all required dependencies are available.
Configuration Differences: Jupyter notebook settings, extensions, or path configurations may vary between systems, causing issues. Using relative paths and ensuring the same extensions (such as nbextensions) are installed can help resolve these problems.
3. Data Source Access Problems
File Path Issues: If the notebook refers to data using absolute file paths, it may not find the files on a different machine. Switching to relative paths or providing clear instructions for data locations can prevent this.
External Data Access: Notebooks relying on APIs or databases may fail if network permissions, credentials, or configurations differ. Make sure to verify API keys, database credentials, and firewall settings to ensure proper access to external resources.