dex-lang
hasktorch
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dex-lang | hasktorch | |
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25 | 15 | |
1,534 | 1,013 | |
0.1% | 0.9% | |
8.8 | 7.2 | |
14 days ago | 3 months ago | |
Haskell | Haskell | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
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dex-lang
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Thinking in an Array Language
A really nice approach to this I've seen recently is Google's research on [Dex](https://github.com/google-research/dex-lang).
- Function Composition in Programming Languages – Conor Hoekstra – CppNorth 2023 [video]
- Dex Lang: Research language for array processing in the Haskell/ML family
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[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
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[D] PyTorch 2.0 Announcement
Have you tried Dex? https://github.com/google-research/dex-lang It is in a relatively early stage, but it is exploring some interesting parts of the design space.
- Mangle, a programming language for deductive database programming
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Looking for languages that combine algebraic effects with parallel execution
I think [Dex](https://github.com/google-research/dex-lang) might be along the lines of what you're looking for, although its focus is on SIMD GPU-style parallelism rather than thread-level parallelism.
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“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
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Haskell for Artificial Intelligence?
In case you want to see one research direction that's combining practical machine learning and functional programming, one of the authors of JAX (and the main author of its predecessor, Autograd) is writing Dex (https://github.com/google-research/dex-lang), a functional language for array processing. The compiler itself is written in Haskell. JAX is one of the most popular libraries for doing a lot of machine learning these days, along with Tensorflow and PyTorch. You might also want to see the bug in the JAX repo about adding Haskell support, for some context: https://github.com/google/jax/issues/185
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?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
grenade - Deep Learning in Haskell
futhark - :boom::computer::boom: A data-parallel functional programming language
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.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
CIPs
Etage - A general data-flow framework featuring nondeterminism, laziness and neurological pseudo-terminology.
tutorials - PyTorch tutorials.
HaVSA - HaVSA (Have-Saa) is a Haskell implementation of the Version Space Algebra Machine Learning technique described by Tessa Lau.