Infiltrator.jl
oxide-enzyme
| Infiltrator.jl | oxide-enzyme | |
|---|---|---|
| 5 | 4 | |
| 379 | 102 | |
| 2.9% | - | |
| 7.1 | 2.9 | |
| 17 days ago | about 1 year ago | |
| Julia | Rust | |
| MIT License | Apache License 2.0 |
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Infiltrator.jl
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I can never debug codes in Julia without issues. Help?
Also Infiltrator is very fast and useful but don't try to use it from the Vscode integrated terminal.
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Just downloaded Julia, what packages/other things do I need to download to have it all work properly?
The package Infiltrator.jl might be what you seek. It's not as good as inserting breakpoints like in Matlab but it's still better than printing everywhere haha
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Julia 1.7 has been released
Yes, it uses Debugger.jl, which relies on JuliaInterpreter.jl under the hood, so while you can tell the debugger to compile functions in certain modules, it will mostly interpret your code.
You might be interested in https://github.com/JuliaDebug/Infiltrator.jl, which uses an approach more similar to what you describe.
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Error handling and unwinding stacks in Julia
Another small thing is in the REPL when you trigger an error in Common lisp it drops you into the debugger where you can redefine code and retry directly from the stack without unwinding the entire stack. Does Julia have functionality similar to this? Currently when I trigger an error Julia just throw the error and goes right back to the top level prompt. To resolve this issue I've tried sprinkling my code with a combination of GitHub - JuliaDebug/Infiltrator.jl + Stack Traces · The Julia Language wrapped in try catch blocks so that if an error is singled it drops into a debugger of sorts. This is ok and it works but it isn't really as good. Is there a current package that can emulate what I am trying to do? I think that the REPL workflow is good in julia but the workflow stalls out when you run into errors that don't drop into debuggers and such.
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Why is piping so well-accepted in the R community compared to those in Julia and Python?
Have you ever tried Infiltrator.jl and Chain.jl?
oxide-enzyme
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Enzyme: towards state-of-the-art AutoDiff in Rust
Afterwards, you can have a look at https://github.com/rust-ml/oxide-enzyme, where I published some toy examples. The current approach has a lot of limitations, mostly due to using the ffi / c-abi to link the generated functions. @bytesnake and I are already looking at an alternative implementation which should solve most, if not all issues. For the meantime, we hope that this already helps those who want to do some early testing. This link might also help you to understand the Rust frontend a bit better. I will add a larger blog post once oxide-enzyme is ready to be published on crates.io.
- Oxide-Enzyme: Integrating LLVM's Static Automatic Differentiation Plugin
- Julia 1.7 has been released
What are some alternatives?
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
Enzyme - High-performance automatic differentiation of LLVM and MLIR.
mujoco - Multi-Joint dynamics with Contact. A general purpose physics simulator.
Diffractor.jl - Next-generation AD
FrechetDiff.jl - FrechetDiff is an experimental Julia package for automatic differentiation (AD).
ResultTypes.jl - A Result type for Julia—it's like Nullables for Exceptions
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.