[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?

• PyO3

  147 11,086 9.8 Rust

  Rust bindings for the Python interpreter

  

Otherwise, though, Rust is an excellent choice. The many advantages of Rust (great package manager, memory safety, modern language features, ...) are already well documented so I won't repeat them here. Specifically for writing Python libraries, check out PyO3, maturin, and rust-numpy, which allow for seamless integration with the Python scientific computing ecosystem. Dockerizing/packaging is a non-issue, with the aforementioned libraries you can easily publish Rust libraries as pip packages or compile them from source as part of your docker build. We have several successful production deployments of Rust code at OpenAI, and I have personally found it to be a joy to work with.

• Dlib

  33 13,054 8.2 C++

  A toolkit for making real world machine learning and data analysis applications in C++

Why not do it all in C++? Dlib has good support for ML. For instance, this is how one would do a simple MNIST example:

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• are-we-learning-yet

  5 423 4.4 Rust

  How ready is Rust for Machine Learning?

Hey OP, you might want to check this site out: http://arewelearningyet.com

• rust-bert

  7 2,427 6.8 Rust

  Rust native ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)

If you are using BERT models and some miscellaneous other related stuff then you should check out the rust-bert and Bert Sentence repos https://github.com/guillaume-be/rust-bert

• RustPython

  96 17,649 9.6 Rust

  A Python Interpreter written in Rust

  

Perhaps something like https://github.com/RustPython/RustPython might be an option at some point.

• maturin

  37 3,275 9.4 Rust

  Build and publish crates with pyo3, cffi and uniffi bindings as well as rust binaries as python packages

  

Otherwise, though, Rust is an excellent choice. The many advantages of Rust (great package manager, memory safety, modern language features, ...) are already well documented so I won't repeat them here. Specifically for writing Python libraries, check out PyO3, maturin, and rust-numpy, which allow for seamless integration with the Python scientific computing ecosystem. Dockerizing/packaging is a non-issue, with the aforementioned libraries you can easily publish Rust libraries as pip packages or compile them from source as part of your docker build. We have several successful production deployments of Rust code at OpenAI, and I have personally found it to be a joy to work with.

• rust-numpy

  10 1,019 8.0 Rust

  PyO3-based Rust bindings of the NumPy C-API

  

Otherwise, though, Rust is an excellent choice. The many advantages of Rust (great package manager, memory safety, modern language features, ...) are already well documented so I won't repeat them here. Specifically for writing Python libraries, check out PyO3, maturin, and rust-numpy, which allow for seamless integration with the Python scientific computing ecosystem. Dockerizing/packaging is a non-issue, with the aforementioned libraries you can easily publish Rust libraries as pip packages or compile them from source as part of your docker build. We have several successful production deployments of Rust code at OpenAI, and I have personally found it to be a joy to work with.

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