mzguntalan/neptune
[WIP] Neptune: JAX iterop-able library in Haskell.
This library is a highly specialized tool for developers who write numerical and machine learning code in Haskell and need it to work seamlessly with models built using JAX in Python. It takes Haskell code defining computations and translates it into a JAX-compatible format, allowing for interoperability between these two distinct environments. The primary users are Haskell developers working on advanced numerical projects who need to integrate with the broader JAX ecosystem.
No commits in the last 6 months.
Use this if you are a Haskell developer building numerical or machine learning applications and require direct compatibility and interoperability with JAX models and computations.
Not ideal if you are not a Haskell developer or if your primary goal is to run existing JAX models without needing to build new computation graphs in Haskell.
Stars
9
Forks
1
Language
Haskell
License
Apache-2.0
Category
Last pushed
Feb 01, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/mzguntalan/neptune"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
explosion/thinc
🔮 A refreshing functional take on deep learning, compatible with your favorite libraries
google-deepmind/optax
Optax is a gradient processing and optimization library for JAX.
patrick-kidger/diffrax
Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable....
google/grain
Library for reading and processing ML training data.
patrick-kidger/equinox
Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/