  Open source machine learning (ML) libraries allow 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. Binding
libraries provide support across programming languages and package ecosystems
for a source 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 conduct an in-depth study of 155
cross-ecosystem bindings and their development for 36 popular open source ML
libraries. Our study shows that for most popular ML libraries, only one package
ecosystem is officially supported (usually PyPI). Cross-ecosystem support,
which is available for 25% of the studied ML libraries, is usually provided
through community-maintained bindings, e.g., 73% of the bindings in the npm
ecosystem are community-maintained. Our study shows that the vast majority of
the studied bindings cover only a small portion of the source library releases,
and the delay for receiving support for a source library release is large.
