1 point by curiousml 11 months ago flag hide 15 comments
johnsmith 11 months ago next
I've heard great things about GitHub Actions for ML projects. It integrates well with other GitHub services, and has a lot of popular ML-focused templates to choose from.
newcoderinaction 11 months ago next
I would agree with johnsmith, GitHub Actions is a solid choice! I especially like its compatibility with various ML frameworks and languages.
cloudxpert 11 months ago next
I would second the suggestion for GitHub Actions. It fits into most of the ML project scenarios and provides a consistent, automated CI/CD experience.
smartprogrammer 11 months ago prev next
CircleCI does have linear pricing and doesn't charge extra when running complex jobs making it a more cost-effective choice for ML pipelines.
mlhacker 11 months ago prev next
Another vote for GitHub Actions here. It's simple, flexible, fast, and can scale to handle even rather large ML projects.
technicalguy 11 months ago prev next
I personally use CircleCI for my projects. It has strong GPU support and its IaC model allows for more control and customization.
randomuser33 11 months ago next
Something to consider with CircleCI is its costliness when using more complex and powerful machines. Be cautious of ballooning expenses!
developerxpto 11 months ago prev next
I am using Jenkins for a large ML project with Airflow and I have just added Kubernetes for even bigger scale. Works nicely.
analytics007 11 months ago next
Kubernetes integration sounds really powerful. Care to share more details regarding its setup and dimensioning?
aigradstudent 11 months ago prev next
Don't forget about using Travis CI! It's pretty user-friendly, has a permissive free plan and provides a docker based service good for ML workloads.
futuremlengineer 11 months ago next
Travis CI does support parallel builds, but it can be quite complex to configure. GitHub Actions has the edge in terms of ease of use there.
open-source-enthusiast 11 months ago next
Sure, I can share some guides and tutorials on integrating Kubernetes with Jenkins!
dataforlife 11 months ago prev next
Jenkins can have a steep learning curve and requires on-going maintenance but is an extensible option. Its compatibility with various plugins makes it a desirable choice for my project.
machinelearningprogress 11 months ago next
I found Jenkins' community, plugins and resources to be incredibly helpful when I encountered issues during setup. Shout-out to their developers!.
integrated-systems 11 months ago prev next
There is also Codefresh and GitLab CI/CD pipelines that offer good ML integration, auto-parallelism and GPU instances.