futhark
hasktorch
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futhark | hasktorch | |
---|---|---|
52 | 15 | |
2,291 | 1,013 | |
2.2% | 0.9% | |
9.8 | 7.2 | |
3 days ago | 3 months ago | |
Haskell | Haskell | |
ISC License | BSD 3-clause "New" or "Revised" License |
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futhark
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What downsides exist to Futhark? Seems almost too good to be true?
Why Futhark? (futhark-lang.org)
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GPU Programming: When, Why and How?
There is no on-going work to support Metal apart from the work done by Miles. There's an old issue about it: https://github.com/diku-dk/futhark/issues/853#issuecomment-5...
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Is Parallel Programming Hard, and, If So, What Can You Do About It? v2023.06.11a
Functional programming can be a great way to handle parallel programming in a sane way. See the Futhark language [1], for example, that accepts high-level constructs like map and convert them to the appropriate machine code, either on the CPU or the GPU.
[1] https://futhark-lang.org/
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Is there a programming language that will blow my mind?
Futhark - use a functional language to program the gpu
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Does This Language Exist?
You might want to look into Futhark, although it's mainly designed for writing GPU code.
- Learn WebGPU
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Two-tier programming language
Futhark https://futhark-lang.org/
- Best book on writing an optimizing compiler (inlining, types, abstract interpretation)?
- Functional GPU programming: what are alternatives or generalizations of the idea of "number of cycles must be known at compile time"?
- APL: An Array Oriented Programming Language (2018)
hasktorch
- BLAS GPU bindings
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Trying out Hasktorch but ghc supported versions conflicts on MacOS M1/2
I assume you are getting https://github.com/hasktorch/hasktorch/issues/631? I suspect you need to upgrade to GHC 9.2 to work reliably on M1.
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Is Haskell okay for prototyping machine learning models for research (discovery and exploration)
You might find the Deep Learning From The First Principles tutorials by Bogdan Penkovsky an interesting survey of native Haskell implementations of deep neural networks, and a bit more. It demonstrates some native charting capabilities, and Day 9 uses Hasktorch.
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Need help Integrating Hasktorch into my Haskell Jupyter environment using Nix
I'm new to Nix and I'm trying to set up a Jupyter notebook environment for Haskell that includes the Hasktorch package. I'm using the jupyenv project from Tweag as the foundation, and I've been able to get it working with some basic Haskell packages. However, I'm running into issues when I try to add Hasktorch to the mix.
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[D] Have their been any attempts to create a programming language specifically for machine learning?
That said, there are some things that try to do this. Haskell has a port of torch called HaskTorch that includes this kind of typed tensor shapes, and calls the Z3 theorem prover on the backend to solve type inference. It can get away with this because of the LiquidHaskell compiler extension, which has refinement types capable of solving this kind of typing problem, and is already pretty mature. Dex is a research language from Google that's based on Haskell and built to explore this kind of typechecking. Really you'd want to do this in Rust, because that's where the tradeoff of speed and safety for convenience makes the most sense, but rust is just barely on the edge of having a type system capable of this. You have to get really clever with the type system to make it work at all, and there's been no sustained push from any company to develop this into a mature solution. Hopefully something better comes along soon
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Haskell deep learning tutorials [Blog]
As rightfully pointed u/gelisam, both Hasktorch and Pytorch are essentially the same things (bindings to existing Torch library). Therefore, it should be generally possible to use existing pretrained models. Here is an example.
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base case
I think it's likely that http://hasktorch.org/ is the library you will want to use for AI models, once you feel comfortable with Haskell.
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looking for simple regression (or classification) library
IF (big if) it turns out you do need deep learning then doing it in Hasktorch http://hasktorch.org/ could be a fun learning project. The team making it is super nice and responsive, too
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Haskell for Artificial Intelligence?
This said, if you want do do deep learning Python is the obvious choice atm, if only for copy-pasting code from examples (however do you know HaskTorch? https://github.com/hasktorch/hasktorch/ )
- GPU-based deep learning in Haskell
What are some alternatives?
arrayfire-rust - Rust wrapper for ArrayFire
grenade - Deep Learning in Haskell
dex-lang - Research language for array processing in the Haskell/ML family
Halide - a language for fast, portable data-parallel computation
finito - A constraint solver for finite domains, written in Haskell.
julia - The Julia Programming Language
tensor-safe - A Haskell framework to define valid deep learning models and export them to other frameworks like TensorFlow JS or Keras.
BQN - An APL-like programming language. Self-hosted!
Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.
kompute - General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.
hnn - haskell neural network library