Debian / Ubuntu is still on 1.4.1, so I guess not soon. 
1 Like
When I download the Windows 64-bit installer on Windows 10, it gets flagged as dangerous.
Version 1.4.2 was fine.
It just says it isn’t commonly downloaded. Which makes sense since it is a new release.
7 Likes
Very nice.
The usual house-cleaning with a new version, but very little house-cleaning related to Julia 1.5 itself. It keeps getting easier.
4 posts were split to a new topic: Stable hashing across Julia versions
The repl terminates immediately after being launched in julia-v1.5.0. Here is the platform info
Platform Info:
OS: Linux (x86_64-pc-linux-gnu)
CPU: Intel(R) Core(TM) i7-6700 CPU @ 3.40GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-8.0.1 (ORCJIT, skylake)
There is nothing wrong in the previous julia versions.
Probably Does using Revise cause julia 1.5.0-rc1 to crash?. (You need to start Julia as julia --startup=no and then update Revise.)
4 Likes
That was exactly the case. The solution you suggested worked. Thank you @simeonschaub
3 Likes
Excellent work, the new behaviour of immutable structs is a major enabler for us and we are seeing some performance wins already. I did, however, also find one performance regression:
Julia 1.4: 1.583 μs
Julia 1.5: 2.607 μs
in
using LightGraphs
using BenchmarkTools
using Random
Random.seed!(3)
### Defining the graph
N = 100 # nodes
g = barabasi_albert(N, 5)
B = incidence_matrix(g, oriented=true)
B_t = transpose(B)
x0 = rand(N)
@btime B_t * x0
instead using
@btime B * sin.(B_t * x0)
has:
Julia 1.4: 5.638 μs (6 allocations: 8.69 KiB)
Julia 1.5: 8.850 μs (6 allocations: 8.69 KiB)
Edit: Filled an issue: https://github.com/JuliaLang/julia/issues/36941
2 Likes
Do you still find that if you interpolate correctly with $?
4 Likes
Yes:
hellmann@clara:~/Nextcloud/Playground/NDBench$ julia bench.jl
Activating environment at `~/Nextcloud/Playground/NDBench/Project.toml`
3.009 μs (1 allocation: 3.88 KiB)
8.658 μs (4 allocations: 9.50 KiB)
hellmann@clara:~/Nextcloud/Playground/NDBench$ ~/julia-14/bin/julia bench.jl
Activating environment at `~/Nextcloud/Playground/NDBench/Project.toml`
1.753 μs (1 allocation: 3.88 KiB)
6.187 μs (4 allocations: 9.50 KiB)
3 Likes
I confirm that I see a performance regression for this code on Mac OS.
4 Likes
is on “Latest/stable 1.0.4”, which isn’t even true for latest LTS. I’m not sure where/how to contact anyone to update this, and the unofficial snap is not limited to 32-bit like the official one. And since Jeff recommended snap at some point it should happen. [EDIT: I’m not saying Jeff recommended 32-bit Julia snap, I don’t recall, there may have been no Julia snap at that point, he was recommending snap for distributing Julia.]
The unofficial better one is at: GitHub - mrcinv/julia-snap: Snapcraft configuration for julia
wait, you telling us Ubuntu has 32-bit Julia in the official snap store?
Yes, exactly (I just checked again be removing and reinstalling it and see “OS: Linux (i686-pc-linux-gnu)”, unlike for the unofficial snap), and while that works for many (and only option if you don’t have 64-bit), I had to debug that misguided choice when calling to 64-bit libraries (was using PyCall). I thought it might have been intentional to cover 32-bit and 64-bit platforms with one binary, but see now you’re not limited to just one arch.
Here’s the unofficial snap, up-to-date for Julia 1.5 (supporting 32- and 64-bit, also ARM):
https://github.com/mrcinv/julia-snap
I see the file the official one needs (just don’t know where to make a PR for it for the official snap): https://github.com/mrcinv/julia-snap/blob/master/snap/snapcraft.yaml
There don’t appear to be any plans to keep the official Julia snap up to date:
https://github.com/JuliaLang/julia/issues/32639#issuecomment-515080786
I really think jill doesn’t get enough love in these threads. If you are on Linux, do yourself a favor and use jill.
3 Likes
Interesting that Microsoft seems to track download URLs to warn the user. 
2 Likes
I personally prefer environment modules. I only have to maintain one system to install things which are either not present in the distributions repository or too outdated for my taste.
But jill seems also be quite useful.
Oh that’s pretty cool. I guess my HPC cluster uses that or something like it. I never thought about doing it on my own system.
If I used anything other than the latest Julia release that might be useful.
1 Like