rust-ndarray
nalgebra
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rust-ndarray | nalgebra | |
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20 | 20 | |
3,319 | 3,717 | |
2.9% | 2.2% | |
8.2 | 7.8 | |
11 days ago | 7 days ago | |
Rust | Rust | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
rust-ndarray
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Some Reasons to Avoid Cython
I would love some examples of how to do non-trivial 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 non-standard 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/rust-ndarray/ndarray, which is challenging to interop with Numpy.
(I am not an expert at all, please correct me if my characterizations are wrong!)
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Helper crate for working with image data of varying type?
Thanks for sharing. I read this issue on why ndarray does not have a dynamically typed array: https://github.com/rust-ndarray/ndarray/issues/651
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What is the most efficient way to study Rust for scientific computing applications?
You can get involved with the ndarray project
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faer 0.8.0 release
Sadly Ndarray does look a little abandoned to me: https://github.com/rust-ndarray/ndarray
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Status and Future of ndarray?
The date of the last commit of [ndarray](https://github.com/rust-ndarray/ndarray) lies 6 month in the past while many recent issues are open and untouched.
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How does explicit unrolling differ from iterating through elements one-by-one? (ndarray example)
While looking through ndarrays src, I came across a set of functions that explicitly unroll 8 variables on each iteration of a loop, with the comment eightfold unrolled so that floating point can be vectorized (even with strict floating point accuracy semantics). I don't understand why floats would be affected by unrolling, and in general I'm confused as to how explicit unrolling differs from iterating through each element one by one. I assumed this would be a scenario where the compiler would optimize best anyway, which seems to be confirmed (at least in the context of using iter() rather than for) here. Could anyone give a little context into what this, or any explicit unrolling achieves?
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Announcing Burn: New Deep Learning framework with CPU & GPU support using the newly stabilized GAT feature
Burn is different: it is built around the Backend trait which encapsulates tensor primitives. Even the reverse mode automatic differentiation is just a backend that wraps another one using the decorator pattern. The goal is to make it very easy to create optimized backends and support different devices and use cases. For now, there are only 3 backends: NdArray (https://github.com/rust-ndarray/ndarray) for a pure rust solution, Tch (https://github.com/LaurentMazare/tch-rs) for an easy access to CUDA and cuDNN optimized operations and the ADBackendDecorator making any backend differentiable. I am now refactoring the internal backend API to make it as easy as possible to plug in new ones.
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Pure rust implementation for deep learning models
Looks like it's an open request
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The Illustrated Stable Diffusion
https://github.com/rust-ndarray/ndarray/issues/281
Answer: you can’t with this crate. I implemented a dynamic n-dim solution myself but it uses views of integer indices that get copied to a new array, which have indexes to another flattened array in order to avoid duplication of possibly massive amounts of n-dimensional data; using the crate alone, copying all the array data would be unavoidable.
Ultimately I’ve had to make my own axis shifting and windowing mechanisms. But the crate is still a useful lib and continuing effort.
While I don’t mind getting into the weeds, these kinds of side efforts can really impact context focus so it’s just something to be aware of.
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Any efficient way of splitting vector?
In principle you're trying to convert between columnar and row-based data layouts, something that happens fairly often in data science. I bet there's some hyper-efficient SIMD magic that could be invoked for these slicing operations (and maybe the iterator solution does exactly that). Might be worth taking a look at how the relevant Rust libraries like ndarray do it.
nalgebra
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Xkcd 2916: Machine
Ok, so this uses https://rapier.rs/ which is very cool
Rapier, alongside https://nalgebra.org/ (which it uses underneath) has seriously good documentation and some advanced features like cross-platform determinism (something made hard by the way floating point differs between platforms)
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Geometric Algebra to Geometric Computing Software Developers
> Some GA libraries[1][2] define types for the different kind of objects (grades)
That's nice!
This reminds of me things like, linear algebra libraries that will type-check matrices so that a 2x2 matrix can't be added to a 2x3 matrix (but then you can have a dynamic matrix that will error only in runtime), like https://nalgebra.org/ and others.
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Has anyone worked on a math library before?
Maybe start by looking at https://nalgebra.org/ to see what rust math libraries might look like
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faer 0.8.0 release
And Nalgebra, while better, also seems to have slowed down on commits and responses to issues and PRs. I have a PR there for a relatively simple wrapper type for row vectors which was explicitly requested by a maintainer, which hasn't even gotten a comment since for two weeks.
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A Rust client library for interacting with Microsoft Airsim https://github.com/Sollimann/airsim-client
nalgebra (similar to Eigen in cpp)
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What crates are considered as de-facto standard?
nalgebra
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Science-related crates that I should have a look at?
nalgebra is for linear algebra.
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Hey Rustaceans! Got a question? Ask here! (31/2022)!
Take a look into math libraries, like glam, nalgebra, and cgmath. I've only used these through game engines, though, so I can't offer per-basis reviews/advice.
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C++ is making me depressed / CUDA question
If you do not need GPU then I would recommend looking into Eigen in C++, nalgebra in Rust (with a BLAS in both cases for improved performance) or one of the above options (Julia / Python+JAX).
- Lightning talk: Stop writing Rust
What are some alternatives?
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
cgmath-rs - A linear algebra and mathematics library for computer graphics.
image - Encoding and decoding images in Rust
glam-rs - A simple and fast linear algebra library for games and graphics
neuronika - Tensors and dynamic neural networks in pure Rust.
rust-blas - BLAS bindings for Rust
utah - Dataframe structure and operations in Rust
rulinalg - A linear algebra library written in Rust
linfa - A Rust machine learning framework.
scirust - Scientific Computing Library in Rust
dasp - The fundamentals for Digital Audio Signal Processing. Formerly `sample`.
arrayfire-rust - Rust wrapper for ArrayFire