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XGBoost #134
Description
Please describe the project
XGBoost is an optimized distributed gradient boosting library. It implements machine learning algorithms under the Gradient Boosting framework.
URL for the project
https://github.com/dmlc/xgboost
Describe current CI/CD setup
GitHub Action based on both GitHub runners and AWS runners (self-hosted). The AWS runners are enabled by runs-on.
Describe the primary use case for the Github Action Runner
The primary use case is running the C++ and Python builds and tests on ppc64le for both pull requests and releases (if we could get all tests to pass).
Paste a link to the existing actions workflow file(s) or directory
https://github.com/dmlc/xgboost/tree/master/.github/workflows
How often do you plan on executing the runner?
Every commit and every pull request.
What is the primary programming language for the project?
- C++/CUDA
- Python
Other languages are considered optional at the moment, including R, Java, and Scala bindings.
Please select desired hardware
- Power 9 (ppc64le)
- IBM Z / LinuxONE (s390x)
Account names of the GitHub repo admins that will need access to setting up the runner
@trivialfis is the primary admin/contact for this setup. @chyunsu3 and @RAMitchell are also active admins for the xgboost repo.
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