Julia v1.6.0-rc3 is now available

The Julia developers are pleased to announce that the third release candidate for Julia v1.6.0 is now available. You can download binaries at https://julialang.org/downloads in the “upcoming release” section for macOS, FreeBSD (x86-64), Windows (32-, 64-bit), glibc Linux (i686, x86-64, AArch64, PowerPC), and musl Linux (x86-64). Check out the NEWS file to see what will be new in 1.6.0.

As a release candidate, this should not be considered production-ready. Rather, it’s intended to give users, especially package developers, a chance to try our their code with 1.6.0 prior to a full release. Note that 1.6 on Travis, AppVeyor, and Cirrus now refers to 1.6.0-rc3. This will be the case for GitHub Actions soon as well.

You may have noticed the short turnaround time since the previous release candidate. We did this to address a number of particularly pressing bugs. You can view the list of commits included since RC2 here.

Let us know in the issue tracker if you run into any issues. Please note that any bugs you may encounter should be posted there rather than being discussed in this thread to ensure visibility to the developers.

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info: new docker :whale: images have been created:

  • docker pull julia:1.6.0-rc3-buster
  • docker pull julia:1.6.0-rc3-alpine3.13 (musl)

https://hub.docker.com/_/julia?tab=tags&page=1&ordering=last_updated&name=rc3

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I’ve created a container that installs Python, Jupyter Lab, and Pluto on top of the image. There’s another built on top of 1.5.4. When you start it, you’ll get Jupyter Lab where you can select what you want as follows.

There are also two Jupyter Lab notebooks included. One is in Julia; it plots an example with Plots.jl and calls Python with PyCall. The other is in Python; it plots an image with Seaborn and calls Julia with PyJulia.

Glances is system monitoring tool so you can see how much CPU is being used, etc.

https://hub.docker.com/repository/docker/statisticalmice/julia-jupyter

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I created a new Dockerfile that installs Julia 1.6 on top of nvidia/cudagl:11.2.2-devel by copy&pasting the official Julia Dockerfile contents with minor modifications, and then my 1.6 things on top of that.

It seems to work, I tried the notebooks. At the moment I don’t know if CUDA works, but the Nvidia base is probably still working. Feel free to try, and please tell the results.

https://github.com/StatisticalMice/julia-jupyter-docker/blob/main/1.6-cudagl/Dockerfile