I want to use gitLab-CI for a Qt-project, but i can't figure out, what I need to do so. I understand, that the whole pipeline process takes place on the CI-Server, but how do I setup the needed requirements like the qt-environment?
Solution:
Ok now I got it! You just use the Runner for it, if you do not have a Server, you can use a VM.
For GitLab.com
The runners are already set up (shared runners).
You need to use a Qt SDK Docker image or install it yourself:
Use image: <image-name> for .gitlab-ci.yml
Use apt or some other package manager (not recommended)
Once you got a Qt SDK environment set up inside .gitlab-ci.yml, make sure to add a command to build/compile/run/test it.
For non-GitLab.com
The runners may or may not be set up, but you do not need to do any specific changes (other than using faster machines with bigger memory, etc for building in necessary).
You need to use a Qt SDK Docker image or install it yourself:
Use image: <image-name> for .gitlab-ci.yml
Use apt or some other package manager (not recommended)
Once you got a Qt SDK environment set up inside .gitlab-ci.yml, make sure to add a command to build/compile/run/test it.
Other Helpful Comments
This is from Josh Peak's comment:
Ok that answers my question. I'm going to have to preconfigure a VM image and/or a Docker image with the QT SDK that the rest of my dev team can leverage. Thanks for the quick response.
This answer is from ManuelP.'s question:
Ok now I got it! You just use the Runner for it, if you do not have a Server, you can use a VM.
Related
I've been using wp-env for a while now for running local WordPress environments for development on my Mac. With the introduction of Monterey, Apple removed PHP from MacOS. There are a couple of ways I can think of to handle this situation. Many people seem to be using Homebrew and MAMP. However, I'd prefer not to have to use Homebrew, both because of past personal experience, but also because going down this path seems to create a whole other mess for how to handle PHP and Composer (see, for example, Using PHPCS with Homebrew On MacOS Monterey).
So, my thought was, maybe I can just start doing development inside of the docker container. The questions then:
how do I extend the wp-env npm module to add things by default to the docker container, without modifying the wp-env source? i.e., does docker have some sort of config I can write that will run wp-env and then add some other stuff to the image? (e.g., npm, git, eslint, etc... so that the docker container itself becomes a development environment).
as I'm actually writing this question, does it even make sense to do it this way? I've found hints that a few people are doing it this way (e.g., a commenter on Using Docker in development the right way talked about his setup where he has vim/tmux/vscode/zsh configuration and shortcuts baked in, and recommends running all services as dockers inside that volume (which he claims is a huge performance increase over host bind mount). Unfortunately, he linked to a git repo that either no longer exists or is at least no longer public.)
While I cannot assist you specifically with wp-env I would recommend using DDev https://ddev.readthedocs.io/en/stable/ As you will basically have the freedom of choosing custom PHP environments, plus it comes with pre defined configurations to use specific stacks e.g. Laravel, WordPress, Drupal, and is dead simple to use.
I understand you might like to continue with wp-env but maybe this will help you out.
My problem is that I can't use R-studio at my work place as the IT does not support it . I want to use R and R-studio that installed on my personnel laptop on my company laptop ( using a modern browser which is behind firewall ) . Some of the options I am thinking of two two things
should I need to build a docker for R and R-studio (I see base images are already available) , I am mostly interested in basic R , Dplyr (haven ,xporter, and Reticulate ) packages .
Should I have to use a binder . I am not technical person and my programming skills are very limited can any one suggest me way .
What exactly are the difference between using Docker option vs Binder ?
I know I can use R-Studio online and get my work done but with the new paid account I am running out of project hours and very slow sometimes . Thanks in advance
Here are some examples beyond the modern RStudio MyBinder example:
https://github.com/fomightez/pythonista_skewedf
https://github.com/fomightez/r_phylogenetics_worshop
https://github.com/fomightez/chapter7/tree/master/binder
The modern RStudio MyBinder example has been set as a template on GitHub so you can use
The first one is for a special use of a package not on conda. And I started that one from square one.
The other two were converted from content by others to aid in making them Binder-ready.
You essentially list everything you need from conda in the environment.yml along with the appropriate channels. If you need special stuff not on conda, you need the other configuration files included there.
Getting everything working can take some iterations on adding things, letting the image get built, and testing your libraries are available. Although you seem to think your situation is not overly complex.
The binder launch badges you see are just images where you modify the URL to point the MyBinder federation site at your repository. Look at the URL and you should see the pattern where you put studio at the end of the URL pointing at your repo. The form at MyBinder.org site can help with this; however, most often it is easier to just adapt a working launch badge's code copied from elsewhere. The form isn't set up at this time for making the URLs for launching to RStudio.
Download anything useful your create in a running session. The sessions timeout after 10 minutes, although RStudio usually keeps them active.
Lack of Persistence and limited memory, storage, & power can be drawbacks. The inherent reproducibility and portability are advantages.
MyBinder.org doesn't work with private repos. If you have code you don't want to share, you can upload it to the temporary session, using the repo for specifying the environment. You could host a private binderhub that does allow the use of private git repositories; however, that is probably overkill for your use case and exceed your ability level at this time.
GitHub isn't the only place to host repositories that can be pointed at the MyBinder system. If you go to the MyBinder.org page and click where it says 'GitHub' on the left side of the top line of the form, you can see a list of the sources at which you can host a repository and point the system to build an image and launch a container with that specified image.
Building the image from a source repository takes some minutes the first time. Once the image is built though on the service, launch is typically less than 30 seconds. Each time you make a change on the source repo, a build is necessary. Some changes don't cause the new build to be as long as the initial one as some optimizing is done to only build what is necessary after a change. Keep in mind there are several members of the federation around the workd and if traffic on the internet gets sent to where the built image isn't yet available, it will be built from scratch again first.
The Holepunch project is out there to offer some help for users working in the R ecosystem; however, with the R-Conda system that is now integrated into MyBinder it is pretty much as easy to do it the way I described. Last I knew, the Holepunch route makes a Dockerfile that isn't as easy to troubleshoot as using the current the R-Conda system route. Dockerfiles are essentially a last ditch configuration file that MyBinder can handle. The reason being the other configuration files are much easier and don't require knowing Dockerfile syntax. MyBinder aims to offer the ability to take advantage of Docker offering containers with a specified environment without users needing to know anything about Docker.
There is a Binder Help category for posting to get help at the Jupyter Discourse Forum. Some other examples of posts already there may help you troubleshoot.
Notice of a common pitfall
Most of the the configuration files for making a repository Binder-ready are simply text and can be edited right in the GitHub browser interface, without need to git or even cloning the repo locally.
Last I knew, there are two exceptions to this. The postBuild and start configuration files have settings that allow them to be run as scripts and these get altered in a way they no longer work if you edit them via the GitHub browser interface. (This was my experience when last I tried. Your mileage may vary or things may have changed now.) To edit those, you have to have git available on a system you have and pull one from some other source. Then edit that on your machine that has git working & add it your repo and push it back up from your local computer.
(If this is a problem, you can post in the Jupyter Discourse Forum Binder help category and you and I could coordinate where I fork and edit those files in your repo to your specifications and then make a pull request to update your source of the fork with those changes.)
If you are using Jupyter notebooks extensively then it may make sense to use Binder
But if you simply want to use R and Rstudio, then all you need is docker. A good resource is
https://github.com/rocker-org/rocker
I want to use Python3.6 in windows sandbox, I installed the same in my windows host machine at
C:\Users\Local_Admin\AppData\Local\Programs\Python\Python36\
I tried passing python to sandbox through configuration file as mapped folder but no luck.
<HostFolder>C:\Users\Local_Admin\AppData\Local\Programs\Python\Python36\</HostFolder>
<SandboxFolder>C:\Users\Local_Admin\AppData\Local\Programs\Python\Python36\</SandboxFolder>
I can use python36 in host machine as I set environment variable but not in sandbox, can anyone suggest why else need to be done?
To make things easier on you, I would suggest you try using Virtualenv. It allows you to create a Sandbox environment so you can run python code without breaking anything. It has documentation and is easy to setup. I have used it in the past for windows.
Customer wants to containerize the website.But I didnot find any options available to add docker support over there in vs2017.Is there any way available?
I recommend going through the very brief and thorough Getting Started tutorial for Docker: it has you build a website from scratch and shouldn't take you longer than two hours from start to finish. By going through this tutorial, I think you will find many of your and your client's (and other's) concerns addressed.
In summary, you will:
Add a Dockerfile to the top level of your project directory
Define the image with all of it's dependencies
Build the image using your source files and produce a container
Iterate over steps 2 and 3 (here) until you have a correctly build the image
Run the container and check out your website running on your localhost
Done!
Be sure to follow Docker's best practices.
There is already an official .Net image, use it (not some uncertified image)!
I am not completely sure if Docker is enough for R development or I should use in in conjunction with Packrat. I have read several posts that state that docker is sufficient. The only place that support this claim is this post. However I was not able to build that example due to errors in the git2r installation.
My overall goal is to have full control of the package versions I use, so my analysis will still work even if the package is later upgraded.
You need both. Think that the docker image is just the final product of your source code, including the Dockerfile and every piece of data used to build the final image.
You should pin the docker (avoid FROM blah:latest) base image to be sure that the underlying libraries and tools will be always the same. Don't use base images such as debian/testing that may change on every run of apt-get install.
If you don`t use packrat when you need to rebuild your image you may get a new piece of code from some library that is not working anymore, for instance, think about a deprecated function you may have used.
And of course version your own code, at least tag it to be able to easily go back in time and start a new build again.
This is the minimum you can do because things like broken Dockerhub or CRAN repositories still can happen. Saving a versioned docker image in a private docker registry is just the final step.
Let's say you use a certain docker image to do your analysis now. If you later start the same docker image, i.e. not just the same name (e.g. rocker/rstudio) or the same version (e.g. rocker/rstudio:3.5.0) but the same image id, you are guaranteed to get the exact same versions of R, R packages and system libraries. This is more than what packrat offers (same R package versions), but requires you to save the docker image.