dex-lang
tensor_annotations
Our great sponsors
dex-lang | tensor_annotations | |
---|---|---|
25 | 2 | |
1,534 | 159 | |
0.0% | 0.0% | |
8.8 | 5.8 | |
14 days ago | 10 months ago | |
Haskell | Python | |
BSD 3-clause "New" or "Revised" License | 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.
dex-lang
-
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
-
[D] Have their been any attempts to create a programming language specifically for machine learning?
Dex
-
[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
-
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.
-
“Why I still recommend Julia”
Dex proves indexing correctness without a full dependent type system, including loops.
-
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
tensor_annotations
-
[D] Have their been any attempts to create a programming language specifically for machine learning?
Not really an answer to your question, but there are Python packages that try to solve the problem of tensor shapes that you mentioned, e.g. https://github.com/patrick-kidger/torchtyping or https://github.com/deepmind/tensor_annotations
-
Matrix Multiplication Inches Closer to Mythic Goal
I've explored this space quite a bit. In my view, static checking should be the goal.
https://github.com/deepmind/tensor_annotations and tsastanley seem to be the most far along. I've developed a mypy plugin that does similarly off of the "Named Tensor" dynamic feature (which isn't well supported yet), but haven't released it yet.
What are some alternatives?
jax - Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
torchtyping - Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.
futhark - :boom::computer::boom: A data-parallel functional programming language
miniF2F - Formal to Formal Mathematics Benchmark
julia - The Julia Programming Language
TablaM - The practical relational programing language for data-oriented applications
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
tiny-cuda-nn - Lightning fast C++/CUDA neural network framework
hasktorch - Tensors and neural networks in Haskell
MindsDB - The platform for customizing AI from enterprise data
CIPs
FL - FL language specification and reference implementations