Bridging the Language Gap: An Empirical Study of Bindings for Open Source Machine Learning Libraries Across Software Package Ecosystems
Open source machine learning (ML) libraries enable developers to integrate advanced ML functionality into their own applications. However, popular ML libraries, such as TensorFlow, are not available natively in all programming languages and software package ecosystems. Hence, developers who wish to use an ML library which is not available in their programming language or ecosystem of choice, may need to resort to using a so-called binding library (or binding). Bindings provide support across programming languages and package ecosystems for reusing a host library. For example, the Keras .NET binding provides support for the Keras library in the NuGet (.NET) ecosystem even though the Keras library was written in Python. In this paper, we collect 2,436 cross-ecosystem bindings for 546 ML libraries across 13 software package ecosystems by using an approach called BindFind, which can automatically identify bindings and link them to their host libraries. Furthermore, we conduct an in-depth study of 133 cross-ecosystem bindings and their development for 40 popular open source ML libraries. Our findings reveal that the majority of ML library bindings are maintained by the community, with npm being the most popular ecosystem for these bindings. Our study also indicates that most bindings cover only a limited range of the host library's releases, often experience considerable delays in supporting new releases, and have widespread technical lag. Our findings highlight key factors to consider for developers integrating bindings for ML libraries and open avenues for researchers to further investigate bindings in software package ecosystems.
Code (1)
Tasks
BIG-bench Machine LearningSimilar Papers 제목 키워드 기반
Studying the Impact of TensorFlow and PyTorch Bindings on Machine Learning Software Quality
Bindings for machine learning frameworks (such as TensorFlow and PyTorch) allow developers to integrate a framework's functionality using a programming language different from the framework's default language (usually Py…
Python Bindings for a Large C++ Robotics Library: The Case of OMPL
Python bindings are a critical bridge between high-performance C++ libraries and the flexibility of Python, enabling rapid prototyping, reproducible experiments, and integration with simulation and learning frameworks in…
Motion PlanningPyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules
We present PyOpenDial, a Python-based domain-independent, open-source toolkit for spoken dialogue systems. Recent advances in core components of dialogue systems, such as speech recognition, language understanding, dialo…
Deep LearningDialogue ManagementDialogue State TrackingManagement+4Slot Machines: How LLMs Keep Track of Multiple Entities
Language models must bind entities to the attributes they possess and maintain several such binding relationships within a context. We study how multiple entities are represented across token positions and whether single…
Flexible and Scalable Deep Learning with MMLSpark
In this work we detail a novel open source library, called MMLSpark, that combines the flexible deep learning library Cognitive Toolkit, with the distributed computing framework Apache Spark. To achieve this, we have con…
Deep LearningDistributed Computing