rust-ndarray
tch-rs
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rust-ndarray | tch-rs | |
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20 | 37 | |
3,319 | 3,824 | |
2.9% | - | |
8.2 | 7.7 | |
12 days ago | 8 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.
tch-rs
- Tch-Rs
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Llama2.rs: One-file Rust implementation of Llama2
I wanted to do something like this but then I would miss on proper CUDA acceleration and lose performance compared to using torchlib.
I wrote a forgettable llama implementation for https://github.com/LaurentMazare/tch-rs (pytorch's torchlib rust binding).
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Playing Atari Games in OCaml
I first encountered OCaml's PyTorch bindings because apparently they generate a C wrapper around PyTorch's C++ API, and Rust's PyTorch bindings use OCaml's C wrapper. See: https://github.com/LaurentMazare/tch-rs
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llm: a Rust crate/CLI for CPU inference of LLMs, including LLaMA, GPT-NeoX, GPT-J and more
You could try looking at the min-GPT example of tch-rs. I'd also strongly suggest watching Karpathy's video to understand what's going on.
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Simply explained: How does GPT work?
If you pefer to see it in code there's a succint gpt implementation here https://github.com/LaurentMazare/tch-rs/blob/main/examples/m...
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Will I ever need python again if I learn rust other than for AI stuff?
Rust is fully compatible w/ C bindings, so even Python libraries written in C can be easily set up to work in Rust (and have been). For example, see PyTorch Rust bindings, which actually works faster than in Python because all of the glue code around the C++ API is in Rust instead of Python.
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A Rust client library for interacting with Microsoft Airsim https://github.com/Sollimann/airsim-client
Pytorch
- [D] HuggingFace in Julia or Rust ?
- This year I tried solving AoC using Rust, here are my impressions coming from Python!
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[Help Needed] Deployment of torchscript using rust
I have looked into this a bit and found some crates which help in loading torchscript models called tch-rs
What are some alternatives?
nalgebra - Linear algebra library for Rust.
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
Rust-CUDA - Ecosystem of libraries and tools for writing and executing fast GPU code fully in Rust.
candle - Minimalist ML framework for Rust
image - Encoding and decoding images in Rust
cbindgen - A project for generating C bindings from Rust code
neuronika - Tensors and dynamic neural networks in pure Rust.
wtpsplit - Code for Where's the Point? Self-Supervised Multilingual Punctuation-Agnostic Sentence Segmentation
utah - Dataframe structure and operations in Rust
veloren - An open world, open source voxel RPG inspired by Dwarf Fortress and Cube World. This repository is a mirror. Please submit all PRs and issues on our GitLab page.
linfa - A Rust machine learning framework.
burn - Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals. [Moved to: https://github.com/Tracel-AI/burn]