SIsischoel·Jun 28, 2019
In A general-purpose probabilistic programming system with programmable inferencemalmaud
TensorFlow.jl
Julia2016–2019
malmaud / TensorFlow.jl
TensorFlow.jl
A Julia wrapper for TensorFlow
10Distinct people
10Mentions
0Explicit recommenders
883GitHub stars
What to know before exploring
What it is
A Julia wrapper for TensorFlow
GitHub
Where it is used
No documented product use has been verified yet.
What people are saying
No recommendation phrase was found in the indexed comments.
No classified warning is attached to this page. Read the discussions before treating silence as approval.
Also in these discussions
- denizyuret/knet.jl · linked in 2 of the same 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, 12:45:45 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 year10 dated mentions
Observed mentions: 2016-09-29 14:43:26 UTC to 2019-06-28 11:51:37 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 2 | 2 | 0 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 3 | 0 | 3 |
What Hacker News says
3 sourcesYes, 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