neuronika
rust-ndarray
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neuronika | rust-ndarray | |
---|---|---|
19 | 15 | |
911 | 2,679 | |
4.2% | 3.4% | |
0.0 | 7.5 | |
6 months ago | 14 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.
neuronika
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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Deep Learning in Rust: Burn 0.4.0 released and plans for 2023
Also perhaps comparing to Neuronika.
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Making a better Tensorflow thanks to strong typing
how does it compare with https://github.com/spearow/juice, https://github.com/neuronika/neuronika and https://github.com/spearow/juice?
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[D] To what extent can Rust be used for Machine Learning?
Check where and how this struct is used. https://github.com/neuronika/neuronika/blob/variable-rework/neuronika-variable/src/history.rs
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Enzyme: Towards state-of-the-art AutoDiff in Rust
I have a question: as the maintainer of [neuronika](https://github.com/neuronika/neuronika), a crate that offers dynamic neural network and auto-differentiation with dynamic graphs, I'm looking at a future possible feature for such framework consisting in the possibility of compiling models, getting thus rid of the "dynamic" part, which is not always needed. This would speed the inference and training times quite a bit.
- Any role that Rust could have in the Data world (Big Data, Data Science, Machine learning, etc.)?
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What sort of mature, open-source libraries do you feel Rust should have but currently lacks?
If you like autograd you will love neuronika
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bhtsne 0.5.0, now 5.6x faster on a 4 core machine, plus a summary of my Rust journey (so far)
After reading most of the book, I wanted to get my hands dirty. My initial idea was to build a small machine learning framework but I deemed it to be too difficult if not impossible for me at the time. (Now, neuronika would have something to say). When gathering the bibliography for my thesis, I recalled to have stumbled upon a particular algorithm, t-SNE, whom I liked very much. I found the idea behind it to be very clever and elegant (t-SNE it's still one of my favorite algorithms, together with backprop and SOM, I find manifold learning fascinating in general). "So be it", I said, and I began writing a mess of a code, that was basically a translation of the C++ implementation. Boy was it bad.
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What are you using Rust for?
me and a colleague of mine are developing neuronika
rust-ndarray
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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.
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
utah - Dataframe structure and operations in Rust
dasp - The fundamentals for Digital Audio Signal Processing. Formerly `sample`.
linfa - A Rust machine learning framework.
are-we-learning-yet - How ready is Rust for Machine Learning?
autograph - Machine Learning Library for Rust
nshare - Provides an interface layer to convert between n-dimensional types in different Rust crates
tonic - A native gRPC client & server implementation with async/await support.
clblast-rs - clblast bindings for rust
tractjs - Run ONNX and TensorFlow inference in the browser.