Git-Theta: A Git Extension for Collaborative Development of Machine Learning Models (2306.04529v1)

Abstract: Currently, most machine learning models are trained by centralized teams and are rarely updated. In contrast, open-source software development involves the iterative development of a shared artifact through distributed collaboration using a version control system. In the interest of enabling collaborative and continual improvement of machine learning models, we introduce Git-Theta, a version control system for machine learning models. Git-Theta is an extension to Git, the most widely used version control software, that allows fine-grained tracking of changes to model parameters alongside code and other artifacts. Unlike existing version control systems that treat a model checkpoint as a blob of data, Git-Theta leverages the structure of checkpoints to support communication-efficient updates, automatic model merges, and meaningful reporting about the difference between two versions of a model. In addition, Git-Theta includes a plug-in system that enables users to easily add support for new functionality. In this paper, we introduce Git-Theta's design and features and include an example use-case of Git-Theta where a pre-trained model is continually adapted and modified. We publicly release Git-Theta in hopes of kickstarting a new era of collaborative model development.

Introduction

The introduction of Git-Theta represents a novel approach to managing the development of machine learning models. Drawing inspiration from the collaboration and iterative improvements seen in open-source software development, Git-Theta extends the capabilities of Git to handle the unique challenges of model versioning.

Design and Features

Git-Theta modifies Git’s version control functionality to efficiently manage changes to machine learning models. It recognizes model checkpoints not as singular data blobs, but as collections of individual "parameter group" tensors. This granular handling allows Git-Theta to offer communication-efficient updates, enabling incremental changes without the overhead of re-uploading entire models. It also offers automatic merges, helping collaborators integrate disparate changes, and generates meaningful reports on the differences between model versions. An additional benefit is the plug-in system, which allows for ease of extension and adaptation to emerging functionalities and checkpoint formats.

Workflow Integration

Leveraging the pervasive use of Git in software development pipelines, Git-Theta is designed to be easily integrated into existing workflows. By allowing for the tracking of both model parameters and associated code together, it presents a synergistic solution that respects current development practices while enhancing the capabilities relating to model tracking.

Motivation and Benchmarks

Initial research spurring the creation of Git-Theta noted the limitations of treating machine learning models as large blobs of data, which typical tools like Git LFS do. Git-Theta's structure-aware system notably reduces storage and communication demands during versioning, as demonstrated in benchmark comparisons with traditional Git LFS. These benchmarks highlight Git-Theta's performance in a realistic collaborative environment involving training, updating, and merging of models, showing significant improvements in storage efficiency, with modest trade-offs in processing speed due to its more complex operations.

Conclusion

The paper concludes with an optimistic outlook on the future of collaborative machine learning model development enabled by Git-Theta. It suggests that just as open-source software development has matured with better tools and methodologies, the machine learning field stands to benefit greatly from tools that facilitate collaboration and continual improvement on a large scale. With Git-Theta's capabilities aimed at solving key issues in model version control and collaboration, it could potentially kick-start a new era in how machine learning models are developed and improved collectively.