Adding CI

[theZiz]

Some examples for implementation CI:

CPU example: https://github.com/pngwriter/pngwriter/blob/master/.travis.yml GPU example (CUDA 5.5.): https://github.com/ComputationalRadiationPhysics/mallocMC/blob/dev/.travis.yml GPU example (CUDA 7.5 & cache): https://github.com/wjakob/pybind11/pull/72/files

[ax3l]

Newer CUDA example for travis: https://github.com/ComputationalRadiationPhysics/cupla/blob/dev/.travis.yml

[psychocoderHPC]

@FelixTUD Let me push this issue up. We should soon use the HZDR CI. Please do not integrate travis or github actions because the HZDR CI gives us the possibility to perform runtime tests too. The required amount of work to add a CI is equal in all cases (travis, github actions, HZDR CI)

[aktech]

Hi @theZiz I am the creator of Cirun.io, "GPU" caught my eye.

FWIW I'll share my two cents. I created a service for problems like these, which is basically running custom machines (including GPUs) in GitHub Actions: https://cirun.io/

It is used in multiple open source projects needing GPU/custom machine support like the following:

• https://github.com/pystatgen/sgkit/
• https://github.com/qutip/qutip-cupy

It is fairly simple to setup, all you need is a cloud account (AWS or GCP) and a simple yaml file describing what kind of machines you need and Cirun will spin up ephemeral machines on your cloud for GitHub Actions to run. It's native to GitHub ecosystem, which mean you can see logs/trigger in the Github's interface itself, just like any Github Action run.

Also, note that Cirun is free for Open source projects. (You only pay to your cloud provider for machine usage)

[psychocoderHPC]

@aktech Thanks for pointing us to your service. We will have a look into it. ISAAC is developed by a German research center therefore the requirement of having an AWS or GCP account is a challenge :-(