oxide-enzyme VS Infiltrator.jl

Compare oxide-enzyme vs Infiltrator.jl and see what are their differences.

oxide-enzyme

Enzyme integration into Rust. Experimental, do not use. (by EnzymeAD)

Infiltrator.jl

No-overhead breakpoints in Julia (by JuliaDebug)
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oxide-enzyme Infiltrator.jl
4 5
102 368
- 3.8%
2.9 7.1
about 1 year ago about 2 months ago
Rust Julia
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
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.

oxide-enzyme

Posts with mentions or reviews of oxide-enzyme. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2021-12-11.

Infiltrator.jl

Posts with mentions or reviews of Infiltrator.jl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-06-25.

What are some alternatives?

When comparing oxide-enzyme and Infiltrator.jl you can also consider the following projects:

Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.

DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)

Debugger.jl - Julia debugger

mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.

ResultTypes.jl - A Result type for Julia—it's like Nullables for Exceptions

Diffractor.jl - Next-generation AD

black - The uncompromising Python code formatter

DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

ModelingToolkit.jl - An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

FrechetDiff.jl - FrechetDiff is an experimental Julia package for automatic differentiation (AD).

Pluto.jl - 🎈 Simple reactive notebooks for Julia