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.

本文研究了深度学习相关软件库的使用情况，以及它们在机器学习工作流程中的依赖关系，并提出了硬件构建者和软件库构建者的一些建议。研究基于1484个开源深度学习项目，发现深度学习库的使用趋势正在上升，目前使用最广泛的库组合为pytorch和Scikit-learn，以及Keras和TensorFlow。

深度学习框架中图书馆使用和依赖的实证研究