EnzymeAD/Enzyme.jl
Julia bindings for the Enzyme automatic differentiator
This package helps Julia developers efficiently calculate derivatives of complex mathematical functions. You input your Julia code, and it provides the gradient values, essential for optimization or scientific modeling. This is for scientific programmers, data scientists, and engineers who write performance-critical Julia code for tasks like machine learning, simulation, or statistical inference.
556 stars. Actively maintained with 18 commits in the last 30 days.
Use this if you need to compute derivatives of your Julia functions with high performance for optimization or model training.
Not ideal if you are not a Julia developer or if your differentiation needs are simple enough for manual calculation.
Stars
556
Forks
90
Language
Julia
License
MIT
Category
Last pushed
Mar 12, 2026
Commits (30d)
18
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/EnzymeAD/Enzyme.jl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related frameworks
CliMA/Oceananigans.jl
🌊 Julia software for fast, friendly, flexible, ocean-flavored fluid dynamics on CPUs and GPUs
JuliaLang/julia
The Julia Programming Language
WassimTenachi/PhySO
Physical Symbolic Optimization
FluxML/Flux.jl
Relax! Flux is the ML library that doesn't make you tensor
astroautomata/SymbolicRegression.jl
Distributed High-Performance Symbolic Regression in Julia