DLProv: a suite of provenance services for deep learning workflow analyses

Abstract

Deep learning (DL) workflows consist of multiple interdependent and repetitive steps, including data preparation, model training, evaluation, and deployment. Each step involves decisions impacting the final model’s performance, interpretability, and applicability. These models rely on data, preprocessing operations, and configuration, underscoring the need for mechanisms to ease the analysis throughout the entire life cycle—from model generation and selection to deployment. Moreover, ensuring trust, reproducibility, and transparency becomes important as DL models transition into production environments. Traceability across the steps of the DL workflow is essential to address these challenges. However, existing traceability solutions often present limitations. Many fail to integrate the steps of the DL workflow, focusing on either data preparation or model training. Additionally, they frequently rely on proprietary formats to represent traceability data and rarely produce a provenance document that can accompany the model into production. To bridge these gaps, we present DLProv, a suite of provenance services designed to ensure end-to-end traceability across DL workflows. DLProv supports structured query language (SQL)-based querying during training and generates provenance graphs that capture data preparation steps, model training, and evaluation. These provenance graphs comply with the PROV de facto standard, ensuring interoperability across different environments. One of the key strengths of DLProv lies in its framework-agnostic architecture. The suite’s services can be invoked independently of the DL framework, enabling integration across several training and deployment workflows. Furthermore, DLProv includes specialized instances designed for specific DL frameworks, such as Keras and physics-informed neural networks (PINNs), offering adaptability to a wide range of applications. We evaluated DLProv using well-established datasets, including Modified National Institute of Standards and Technology (MNIST) and Canadian Institute for Advanced Research (CIFAR)-100. These datasets were chosen to illustrate the suite’s capability to capture and manage provenance data across tasks of varying complexity, from basic image classification to more complex DL workflows. Additionally, we evaluated DLProv within a handwritten transcription workflow, further showcasing its flexibility. Across all these use cases, DLProv showed its ability to ease SQL-based queries during model training while maintaining framework independence. An important aspect of our evaluation was measuring the overhead introduced by integrating DLProv into DL workflows. The results showed a maximum overhead of 1.4% in execution time, highlighting the suite’s minimal impact on DL workflow performance. For comparative analysis, we benchmarked this overhead against MLflow, further reinforcing DLProv’s suitability for real-world DL applications.

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