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Top 11 Rust Scientific Computing Projects

burn
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
Project mention: 3 years of fulltime Rust game development, and why we're leaving Rust behind  news.ycombinator.com  20240426You can use libtorch directly via `tchrs`, and at present I'm porting over to Burn (see https://burn.dev) which appears incredibly promising. My impression is it's in a good place, if of course not close to the ecosystem of Python/C++. At very least I've gotten my nn models training and running without too much difficulty. (I'm moving to Burn for the thread safety  their `Tensor` impl is `Sync`  libtorch doesn't have such a guarantee.)
Burn has Candle as one of its backends, which I understand is also quite popular.

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rustndarray
ndarray: an Ndimensional array with array views, multidimensional slicing, and efficient operations
I would love some examples of how to do nontrivial data interop between Rust and Python. My experience is that PyO3/Maturin is excellent when converting between simple datatypes but conversions get difficult when there are nonstandard types, e.g. Python Numpy arrays or Rust ndarrays or whatever other custom thing.
Polars seems to have a good model where it uses the Arrow in memory format, which has implementations in Python and Rust, and makes a lot of the ndarray stuff easier. However, if the Rust libraries are not written with Arrow first, they become quite hard to work with. For example, there are many libraries written with https://github.com/rustndarray/ndarray, which is challenging to interop with Numpy.
(I am not an expert at all, please correct me if my characterizations are wrong!)

shadow
Shadow is a discreteevent network simulator that directly executes real application code, enabling you to simulate distributed systems with thousands of networkconnected processes in realistic and scalable private network experiments using your laptop, desktop, or server running Linux. (by shadow)
Traditional textbooks on networking are so verbose and tedious... often very focused on theory and less helpful for everyday networking.
I would love to see courses based on tools like shadow [1], encouraging a more handson learning experience. Network simulators seem pretty cool, but it is hard to benefit from them using the docs alone. With shadow it seems I would be able to run actual software on a simulated network.

1: https://github.com/shadow/shadow?tab=readmeovfile#whyiss...


smartcore
A comprehensive library for machine learning and numerical computing. The library provides a set of tools for linear algebra, numerical computing, optimization, and enables a generic, powerful yet still efficient approach to machine learning.


SaaSHub
SaaSHub  Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives


russell
Rust Scientific Libary. ODE and DAE (RungeKutta) solvers. Special functions (Bessel, Elliptic, Beta, Gamma, Erf). Linear algebra. Sparse solvers (MUMPS, UMFPACK). Probability distributions. Tensor calculus.


Rust Scientific Computing discussion
Rust Scientific Computing related posts

Burn: Deep Learning Framework built using Rust

Burn Deep Learning Framework Release 0.12.0 Improved API and PyTorch Integration

Burn Deep Learning Framework 0.11.0 Released: JustinTime Automatic Kernel Fusion & Founding Announcement

Burn Deep Learning Framework v0.11.0 Released: JustinTime Kernel Fusion

Burn – comprehensive dynamic Deep Learning Framework built using Rust

Helper crate for working with image data of varying type?

What is the most efficient way to study Rust for scientific computing applications?

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