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Rust-ndarray Alternatives
Similar projects and alternatives to rust-ndarray
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nshare
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NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better rust-ndarray alternative or higher similarity.
rust-ndarray reviews and mentions
Posts with mentions or reviews of rust-ndarray.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-11-06.
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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
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Signal processing library
I used basic_dsp a while back and found it lacking. I was hoping to find something that uses the ndarray datatype but i'm not seeing this yet. If you're primarily trying to learn though it might not really matter which library you contribute to. As for myself, I just picked the one that was most used and actively worked on at the time. However I keep an eye out on other libraries; if I see something take off, I might switch over. Either way you'll learn and can point to it as work accomplished.
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Searching help for Rust Image Manipulation
Most libraries use either an older C/C++ dynamically or statically. I was looking into maybe rewriting some image manipulation functions in Rust using ndarray (which would then allow for both hardware, gpu and multithreading acceleration), but it would take a very long to get fully into the subject.
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Dumb question: I want to overload operators like add on structs, what is the name for what I'm trying to do?
The best way would to use a crate for this, such as ndarray, which provides some good ways to do this.
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www.sonarqube.org | 26 Mar 2023
Stats
Basic rust-ndarray repo stats
15
2,679
7.5
15 days ago
rust-ndarray/ndarray is an open source project licensed under Apache License 2.0 which is an OSI approved license.
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