I am going to simplify installation method to just single golang binary , coming soon

Hey 👋 and thanks for your interest. Yes it can be self hosted - please read this - http://deadsimpleci.sparrowhub.io/doc/forgejo-setup , right now you need to build your own container image and run it with docker/podman. Please let me know if I can help

just test

just use dedicated alpine image, pretty easy:

https://deadsimpleci.sparrowhub.io/doc/cookbook

In what environment is the CI

By default this is alline:latest docker container , however one can use custom docker images ( to be documented, but let me know if you are interested ), on the demo server , Ubuntu image is used

run_task comes as a part of dsci SDK for Python . What do you mean by build time dependencies? You are free put anything into Python / Bash tasks that is called by run_task function in job file … if you point your repo demo server I can help you with that …

Update for new users: Just create some repo and add http://127.0.0.1:4000/forgejo_hook to a repo web hook, then create some dsci pipeline and it will gets triggered

Actually just enabled self registration , no need to ask me, but still will be happy to see anyone in discord )

It uses yaml only for configuration part, but pipeline itself is far more then that . Not sure what do you mean by “middle ground”, could you please elaborate? Thanks

Just added the feature of running jobs on localhost for debugging:

cd .dsci/job_one; docker run -it -v $PWD:/opt/job --entrypoint /bin/bash dsci -c "cd /opt/job/; s6 --task-run ."

deleted by creator

deleted by creator

Feedback are welcome , the project is in very early stage …

TLTR;

Sparky is a distributed jobs framework that allows orchestration of remote tasks on cluster of nodes. It’s simple to set up and easy to use. This post is a brief overview of Sparky architecture design.

Sparky targeted audience is:

cloud providers to manage underlying multiple hosts infrastructure

data scientists to process data in distributed manner (aka data pipelines)

software engineers and devops doing any tasks with distributed nature in mind

You may try out https://github.com/melezhik/sparky which is a local / remote task runner with nice front end and scripts could be written on many languages

Yep. Fancy devs watching me coding some Rakulang in nano 😂

Ok. “I am a good FOSS developer”

Thanks, will take a look

nano is the best (imho) for up to medium size files. It’s preinstalled in most Linux boxes , it’s simple and flexible enough, takes a minimal amount of time to learn basic for keys and then use them all the time

Not generator, validator. It validates configuration files . Ansible is not flexible in comparison with Sparrow, you’d need to write more boilerplate code to do the same … Also core ansible modules search is limited by “one line” mode, thus it does not allow to search for example within nested structures, like if we want something in between or in nested blocks, or search for sequences, like when we want to search a sequence of strings, a,b,c,d etc, Sparrow does allow al thatl as it has ranges/sequential/SLN search by design. Sparrow allows to generate check rules in runtime as well, Ansible can’t

fair enough, however the intention is to show how one could create rules on Sparrow/Raku, not to show rules … Maybe I should have mentioned that …

for example this is more interesting example evaluation of net.ipv4.tcp_synack_retries"

regexp: ^^ "net.ipv4.tcp_synack_retries" \s* "=" \s* (\d+) \s* $$

generator: <<RAKU
!raku
if matched().elems {
  my $v = capture()[];
  say "note: net.ipv4.tcp_synack_retries={$v}";
  if $v >= 3 && $v <= 5 {
     say "assert: 1 net.ipv4.tcp_synack_retries in [3..5] range"
  } else {
     say "assert: 0 net.ipv4.tcp_synack_retries in [3..5] range"
  }
} else {
  say "note: net.ipv4.tcp_synack_retries setting not found"
}
RAKU