# An Empirical Study of Library Usage and Dependency in Deep Learning Frameworks

arXiv: https://arxiv.org/abs/2211.15733 · HTML: https://mlsanity.com/paper/2211.15733/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 28 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [Caffe: Convolutional Architecture for Fast Feature Embedding](https://mlsanity.com/paper/1408.5093/) (2014)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [Guiding Deep Learning System Testing using Surprise Adequacy](https://mlsanity.com/paper/1808.08444/) (2018)
- [A Comprehensive Study on Deep Learning Bug Characteristics](https://mlsanity.com/paper/1906.01388/) (2019)
- [Theano: A Python framework for fast computation of mathematical expressions](https://mlsanity.com/paper/1605.02688/) (2016)
- [Understanding the Factors that Impact the Popularity of GitHub Repositories](https://mlsanity.com/paper/1606.04984/) (2016)
- [What's in a GitHub Star? Understanding Repository Starring Practices in a Social Coding Platform](https://mlsanity.com/paper/1811.07643/) (2018)
- [An Empirical Comparison of Dependency Network Evolution in Seven Software Packaging Ecosystems](https://mlsanity.com/paper/1710.04936/) (2017)

## Built on by (most cited first, 2 of 2)

- [Deep Learning for Retinal Degeneration Assessment: A Comprehensive Analysis of the MARIO Challenge](https://mlsanity.com/paper/2506.02976/) (2025)
- [YAMLE: Yet Another Machine Learning Environment](https://mlsanity.com/paper/2402.06268/) (2024)

## Abstract (arXiv)

Recent advances in deep learning (dl) have led to the release of several dl software libraries such as pytorch, Caffe, and TensorFlow, in order to assist machine learning (ml) practitioners in developing and deploying state-of-the-art deep neural networks (DNN), but they are not able to properly cope with limitations in the dl libraries such as testing or data processing. In this paper, we present a qualitative and quantitative analysis of the most frequent dl libraries combination, the distribution of dl library dependencies across the ml workflow, and formulate a set of recommendations to (i) hardware builders for more optimized accelerators and (ii) library builder for more refined future releases. Our study is based on 1,484 open-source dl projects with 46,110 contributors selected based on their reputation. First, we found an increasing trend in the usage of deep learning libraries. Second, we highlight several usage patterns of deep learning libraries. In addition, we identify dependencies between dl libraries and the most frequent combination where we discover that pytorch and Scikit-learn and, Keras and TensorFlow are the most frequent combination in 18% and 14% of the projects. The developer uses two or three dl libraries in the same projects and tends to use different multiple dl libraries in both the same function and the same files. The developer shows patterns in using various deep-learning libraries and prefers simple functions with fewer arguments and straightforward goals. Finally, we present the implications of our findings for researchers, library maintainers, and hardware vendors.