rust-numpy
tokenizers
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BSD 2-clause "Simplified" License | Apache License 2.0 |
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rust-numpy
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Numba: A High Performance Python Compiler
On the contrary, it can use and interface with numpy quite easily: https://github.com/PyO3/rust-numpy
- Carefully exploring Rust as a Python developer
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Hmm
Once I figured out the right tools, it was easy. Its just "maturin new". It automatically converts python floats and strings. Numpy arrays come through as a special Pyarray type, that you need to unwrap, but that's just one builtin function. Using pyo3, maturin and numpy, https://github.com/PyO3/rust-numpy it's fairly easy.
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Man, I love this language.
If I'm understanding this documentation correctly then you may be able to pass the numpy array directly with func(df['col'].to_numpy) which may save some conversion.
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[D] Is Rust stable/mature enough to be used for production ML? Is making Rust-based python wrappers a good choice for performance heavy uses and internal ML dependencies in 2021?
Otherwise, though, Rust is an excellent choice. The many advantages of Rust (great package manager, memory safety, modern language features, ...) are already well documented so I won't repeat them here. Specifically for writing Python libraries, check out PyO3, maturin, and rust-numpy, which allow for seamless integration with the Python scientific computing ecosystem. Dockerizing/packaging is a non-issue, with the aforementioned libraries you can easily publish Rust libraries as pip packages or compile them from source as part of your docker build. We have several successful production deployments of Rust code at OpenAI, and I have personally found it to be a joy to work with.
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Writing Rust libraries for the Python scientific computing ecosystem
Integration with numpy uses the rust-numpy crate: Example of method that accepts numpy arrays as arguments Example of a method that returns a numpy array to Python (this performs a copy, there ought to be a way to avoid it but the current implementation has been plenty fast for my use case so far)
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Feasibility of Using a Python Image Super Resolution Library in My Rust App
This example maybe helpful.
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Julia is the better language for extending Python
Given that it's via pyO3, you could even pass the numpy arrays using https://github.com/PyO3/rust-numpy and get ndarrays at the other side.
Same no copy, slightly more user friendly approach.
Further criticism of the actual approach - even if we didn't do zero copy, there's no preallocation for the vector despite the size being known upfront, and nested vectors are very slow by default.
So you could speed up the entire thing by passing it to ndarray, and then running a single call to sum over the 2D array you'd find at the other end. (https://docs.rs/ndarray/0.15.1/ndarray/struct.ArrayBase.html...)
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Parsing PDF Documents in Rust
I believe converting between pandas Series (e.g. columns) and numpy ndarrays can be pretty cheap, right? Once they're in that format, you can use rust to work directly on the numpy memory buffer with rust-numpy. Otherwise, feather is a format designed for IPC of columnar data; pyarrow is in pandas (might be an optional dependency) and may be pretty quick for that, and rust has an arrow implementation too.
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PyO3: Rust Bindings for the Python Interpreter
https://github.com/PyO3/rust-numpy
tokenizers
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HF Transfer: Speed up file transfers
Hugging Face seems to like Rust. They also wrote Tokenizers in Rust.
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LLM custom dictionary
Your intuition is right. There are two ways (in increasing order of result performance) : 1. You can simply extend vocab file of the tokenizer and test the predictions 2. You can extend the vocab file and re-train your model on custom data which has these new tokens. Check the following issue on GitHub : https://github.com/huggingface/tokenizers/issues/247
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[D] SentencePiece, WordPiece, BPE... Which tokenizer is the best one?
SentencePiece -> implementation of some algorithms (there are several others, https://github.com/microsoft/BlingFire https://github.com/glample/fastBPE https://github.com/huggingface/tokenizers )
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Portability of Rust in 2021
In sum I would like the idea to go with Rust as I more or less got to rewrite the whole thing anyway, but I am a bit skeptical if I will be able to interface with everything that might come up at some point. Or probably end up in a wrapper hell if I got to use more C++ libraries. On the other hand there are definitely a few Rust projects out there that might come in handy (for example https://github.com/huggingface/tokenizers). And the build process is pretty awful right now (CMake it is but with lots of hacks).
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[D] What's going to be the dominant language for machine learning in 5 years?
A full machine learning pipeline usually comprises far more than just the model, and this is the area where Rust may shine (the recent work by HuggingFace and their https://github.com/huggingface/tokenizers library is a good example)
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substitute for tokenizer in torchtext
As for other tokenizers, you can take a look at - Huggingface tokenizers library: https://github.com/huggingface/tokenizers - NLTK tokenize: https://www.nltk.org/api/nltk.tokenize.html - Polygot: https://pypi.org/project/polyglot/
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PyO3: Rust Bindings for the Python Interpreter
Huggingface Tokenizers (https://github.com/huggingface/tokenizers), which are now used by default in their Transformers Python library, use pyO3 and became popular due to the pitch that it encoded text an order of magnitude faster with zero config changes.
It lives up to that claim. (I had issues with return object typing when going between Python/Rust at first but those are more consistent now)
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Rusticles #19 - Wed Nov 11 2020
huggingface/tokenizers (Rust): ๐ฅFast State-of-the-Art Tokenizers optimized for Research and Production
What are some alternatives?
RustPython - A Python Interpreter written in Rust
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
julia - The Julia Programming Language
onnx-tensorflow - Tensorflow Backend for ONNX
polars - Dataframes powered by a multithreaded, vectorized query engine, written in Rust
setuptools-rust - Setuptools plugin for Rust support
rayon - Rayon: A data parallelism library for Rust
BlingFire - A lightning fast Finite State machine and REgular expression manipulation library.
image-super-resolution - ๐ Super-scale your images and run experiments with Residual Dense and Adversarial Networks.
PyO3 - Rust bindings for the Python interpreter
tch-rs - Rust bindings for the C++ api of PyTorch.