rust-ndarray
nshare
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| rust-ndarray | nshare | |
|---|---|---|
| 20 | 1 | |
| 2,948 | 30 | |
| 2.1% | - | |
| 0.0 | 0.0 | |
| 14 days ago | 8 months ago | |
| Rust | Rust | |
| Apache License 2.0 | MIT License |
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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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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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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.
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Rust or C/C++ to learn as a secondary language?
ndarray and numpy crates provide good way to operate on numpy ndarrays from python
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Enzyme: Towards state-of-the-art AutoDiff in Rust
I don't think any of the major ML projects have GPU acceleration because ndarray doesn't support it.
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Announcing Rust CUDA 0.2
Not sure about ndarray: https://github.com/rust-ndarray/ndarray/issues/840
nshare
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Making Maintaining Easier
First, let me explain a crate, where old dependencies do matter. There is one very handy crate called nshare. It's used for converting images into ndarrays. The libraries between nshare converts are regularly updated, for example to support new formats or to make the usage more convenient. If one writes a new software there could be conflicts in versions of ndarray (for example), so we make sure, that nshare is always up-to-date and works with the newest versions in order to build the best code on it. Another example could be everything cryptographic. If there would be a security issue with ring we really want every dependent to be updated.
What are some alternatives?
nalgebra - Linear algebra library for Rust.
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
image - Encoding and decoding images in Rust
neuronika - Tensors and dynamic neural networks in pure Rust.
utah - Dataframe structure and operations in Rust
linfa - A Rust machine learning framework.
dasp - The fundamentals for Digital Audio Signal Processing. Formerly `sample`.
PySCIPOpt - Python interface for the SCIP Optimization Suite
fundsp - Library for audio processing and synthesis
rust-dsp - A library for sound Digital Signal Processing, written in Rust
traceroute - Rust traceroute
mypyc - Compile type annotated Python to fast C extensions