Introduction
Welcome to the mlskeleton Python package! This package is designed to help users easily set up a standard folder structure for machine learning and deep learning projects.
Why Use mlskeleton?
If you have worked on multiple machine learning or deep learning projects, you know how important it is to have a consistent and organized folder structure. A well-organized project can make it easier to keep track of your code, data, models, and results. It can also make it easier to collaborate with other team members and share your work with others.
mlskeleton aims to make it easy for users to set up a consistent and organized folder structure for their projects. It includes a range of common folders and files that you might need for your project, such as data folders, model files, Jupyter notebooks, and source code files.
Using mlskeleton
Using mlskeleton is simple.
First, install the package using
pip install mlskeleton
Then, navigate to the root folder of your project and run the mlskeleton command followed by the path to your root folder. For example:
mlskeleton /path/to/root/folder
This will generate the folder structure according to the default template. If you want to customize the folder structure, you can do so by modifying the folder_structure.json file included in the package.
Contributions
We welcome contributions to the mlskeleton repository! If you have suggestions for improvements or additional features, feel free to fork the repository and open a pull request. Together, we can make mlskeleton an even more useful tool for organizing machine learning and deep learning projects.
Conclusion
We hope that mlskeleton will be a useful tool for you in organizing your machine learning and deep learning projects. With a consistent and organized folder structure, you can focus on the important work of building and evaluating models, rather than spending time on file management.
Thank you for choosing mlskeleton!
