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Arraymancer
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
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Impulse will be a collection of primitives for signal processing (FFT, Convolutions, ...) (by SciNim)
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It is a small DSL written using macros at https://github.com/mratsim/Arraymancer/blob/master/src/array....
Nim has pretty great meta-programming capabilities and arraymancer employs some cool features like emitting cuda-kernels on the fly using standard templates depending on backend !
I am one of the Arraymancer contributors. I believe that what mratsim (Arraymancer’s creator) has done is pretty amazing but I agree that the scope is a quite ambitious. There’s been some talk about separating the deep learning bits into its own library (which I expect would be done in a backwards compatible way). Recently we worked on adding FFT support but instead of adding it to Arraymancer it was added to “impulse” (https://github.com/SciNim/impulse) which is a separate, signal processing focused library. There is also Vindaar’s datamancer (a pandas like dataframe library) and ggplotnim (a plotting library inspired by R’s ggplot). The combination of all of these libraries makes nim a very compelling language for signal processing, data science and ML.
Personally I’d like Arraymancer to be a great tensor library (basically a very good and ideally faster alternative to numpy and base Matlab). Frankly I think that it’s nearly there already. I’ve been using Arraymancer to port a 5G physical layer simulator from Matlab to nim and it’s been a joy. It’s not perfect by any means but it’s already very good. And given how fast nim’s scientific ecosystem keeps improving it will only get much better.
Jupyter notebook is indeed very important. It mainly provides data scientists with two things: a literate programming environment (mixing text, code and outputs) and a way to hold state of data in memory (so that you can perform computation interactively).
As a different take to literate programming we have created a library and an ecosystem around it: https://github.com/pietroppeter/nimib
For holding state a Nim repl (which is on the roadmap as secondary priority after completing incremental compilation) is definitely an option.
Another option could be to create a library framework for caching (or be able to serialize and deserialize quickly) large data and objects. One way to see it, could be to build something similar to streamlit cache (streamlit indeed provides great interactivity)
I just had a look, and there does seem to be a Jupyter kernel at https://github.com/stisa/jupyternim
