Provenance Tracking in Large-Scale Machine Learning Systems
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Abstract
As the demand for large scale AI models continues to grow, the optimization of their training to balance computational efficiency, execution time, accuracy and energy consumption represents a critical multidimensional challenge. Achieving this balance requires not only innovative algorithmic techniques and hardware architectures but also comprehensive tools for monitoring, analyzing, and understanding the underlying processes involved in model training and deployment. Provenance data information about the origins, context, and transformations of data and processes has become a key component in this pursuit. By leveraging provenance, researchers and engineers can gain insights into resource usage patterns, identify inefficiencies, and ensure reproducibility and accountability in AI development workflows. For this reason, the question of how distributed resources can be optimally utilized to scale large AI models in an energy efficient manner is a fundamental one. To support this effort, we introduce the yProv4ML library, a tool designed to collect provenance data in JSON format, compliant with the W3C PROV and ProvML standards. yProv4ML focuses on flexibility and extensibility, and enables users to integrate additional data collection tools via plugins. The library is fully integrated with the yProv framework, allowing for higher level pairing in tasks run also through workflow management systems.
