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FluxML
Zygote.jl
Julia2018–2023
FluxML / Zygote.jl

Zygote.jl

21st century AD

Languages & CompilersAI & Machine LearningLearning & Reference
Explore on GitHubRead Hacker News discussions
17Distinct people
23Mentions
0Explicit recommenders
1.6KGitHub stars
REPOSITORY AT A GLANCE

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What it is

21st century AD

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

No recommendation phrase was found in the indexed comments.

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Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 10:01:16 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year23 dated mentions

Observed mentions: 2018-10-22 16:04:07 UTC to 2023-12-04 14:08:04 UTC.

02468102018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 4 mentions; 2 submissions, 2 comments20182019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 9 mentions; 0 submissions, 9 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments20202021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 2 mentions; 0 submissions, 2 comments2023

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC422
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC909
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC303
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC505
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC000
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC202

What Hacker News says

9 sources
ViralBShah·Dec 16, 2019
I am happy to share my perspective on this. I feel a really good blog on this topic is Bret Victor's blog post - What can a technologist do about climate change? http://worrydream.com/ClimateChange/ One of the clear recommendations is - contribute to Julia. A lot of what Bret said in 2015 has actually panned out. Julia has become a powerful language for scientific computing and machine learning. As a result it is being used in climate projects such as Climate Machine (MIT and Caltech). The Julia Lab at MIT participates in this project: Climate Machine: https://clima.caltech.edu/ Github: https://github.com/climate-machine/ Julia Lab: http://julia.mit.edu/ Contributions to Julia packages (compiler, stdlibs, math packages, ML packages, parallel computing) will end up finding their way into climate research, because of the extensive reuse of
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code within the Julia ecosystem. Specifically capabilities such as Zygote.jl (https://github.com/FluxML/Zygote.jl) for differentiable programming have the potential to dramatically make it easy to apply ML techniques to scientific codebases. Compiler contributors are hard to come along, so all contributions to compiler technology are incredibly valuable. The DiffEqFlux.jl ecosystem in Julia is a good example of combining mechanistic models with ML (https://github.com/JuliaDiffEq/DiffEqFlux.jl). Hop on to the Julia slack channel or discourse to dig in deeper. Another thing Bret Victor speaks about in his blog post is working with agencies such as ARPA-E on advanced projects. Julia Computing is participating in an ARPA-E project to bring these capabilities to many energy related simulation and development technologies. This press release gives a broad idea: ARPA-E press release: https://www.energy.gov/articles/department-energy-announces-... Funded projects: https://arpa-e.energy.gov/sites/default/files/documents/file... Julia Computing press release: https://juliacomputing.com/communication/2019/12/09/arpa-e.h...
In Jeff Dean interview: Machine learning trends in 2020
ddragon·Nov 13, 2019
1) It's not restricted to the variables being passed, for example Zygote [1] has an example in the README with IO gradient(x -> fs[readline()](x), 1), and it's not using numerical differentiation (varying inputs to check the output), but finding a formula (approximate, not a closed form like a symbolic differentiation) of how changing the output affects the input. Genetic algorithms is a black box metaheuristic, so people would favor using gradient descent given that the code is differentiable, but how the model is optimized is technically open to any method. 2) Yes, it can differentiate all control flow. What happens is that each forward/backward pass will have a different graph based on what branches it would follow (effectively a subderivative). Each one of these graphs is independently differentiable and do not contain the control flo
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w by themselves. 3) It doesn't need a language extension (Julia doesn't, but that's because you can access Julia IR using the language itself, not many language support this level of metaprogramming). The OOP strategy (like Pytorch) overload methods so every time you call the overloaded method it builds the graph. It depends on customized types and methods and does not support code that isn't using those custom types that can hold the graph (native/custom types), native control flow (only implicitly by changing what graph is being created), reduces the possible optimizations (you only have one subderivative, not the complete graph to optimize) and end up creating a DSL with it's own error messages and quirks that is less natural than just using the host language. I can't say how it would look using monads though. [1] https://github.com/FluxML/Zygote.jl
In Differentiable Programming Manifesto
Certhas·Aug 18, 2019
Indeed, and while there is plenty to still be worried about, a lot has been addressed, too. Further we are now starting to see the pay-off with amazing algorithmic innovations being implemented in Julia. We have working code to code automatic differentiation: https://github.com/FluxML/Zygote.jl Finally, the core team is paying attention to the problems and addressing them. All my top complaints are already mentioned in the laundry list: https://www.youtube.com/watch?v=TPuJsgyu87U
In Giving up on Julia (2016)
ddragon·Jun 10, 2019
Yes, there are already a couple. The most well known is a 100% Julia neural network library called Flux.jl [1], which aims to become what Swift for Tensorflow wants as well (to make the entire Julia language a fully differentiable language) through Zygote.jl [2], and even without it has already great integration with the ecosystem, for example with the differentiable equations library through DiffEqFlux.jl [3]. Plus the source code is very high level (while being high performance, including easy GPU support), so you can easily see what each component does and implement any extension directly on your code without worrying about performance. There is also another feature complete native library that allows some very concise code, Knet.jl [4], and the Tensorflow bindings [5]. [1] https://github.com/FluxML/Flux.jl [2] https://github.com/FluxM
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L/Zygote.jl [3] https://julialang.org/blog/2019/01/fluxdiffeq [4] https://github.com/denizyuret/Knet.jl [5] https://github.com/malmaud/TensorFlow.jl
In Matlab–Python–Julia Cheatsheet

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