
Kaiyi Liu contributed to the sustainable-computing-io/kepler and kepler-metal-ci repositories by building features that enhanced power metrics observability, CI/CD reliability, and data visualization. He developed an experimental hwmon power metrics collector in Go, enabling configurable power usage monitoring for supported architectures. In kepler, he delivered a dashboard for node-level energy metrics, using Python and backend development skills to support historical and real-time analysis. For kepler-metal-ci, Kaiyi improved AWS runner management, integrated Prometheus-based monitoring, and stabilized CI environments with Ansible and shell scripting. His work demonstrated depth in configuration management, testing, and system monitoring, addressing both reliability and maintainability.
January 2026 (2026-01) monthly summary for sustainable-computing-io/kepler. Delivered the Experimental Hwmon Power Metrics feature, enabling power metrics collection via the hwmon subsystem and an experimental config toggle. Implemented a hwmon device reader for architectures with hwmon sensors to acquire watts, added docs and unit tests, and established configuration support to enable the feature. This work increases observability of power usage and provides a foundation for power-aware optimizations. Focus this month was on instrumentation, configuration, and test coverage to drive reliability and business value.
January 2026 (2026-01) monthly summary for sustainable-computing-io/kepler. Delivered the Experimental Hwmon Power Metrics feature, enabling power metrics collection via the hwmon subsystem and an experimental config toggle. Implemented a hwmon device reader for architectures with hwmon sensors to acquire watts, added docs and unit tests, and established configuration support to enable the feature. This work increases observability of power usage and provides a foundation for power-aware optimizations. Focus this month was on instrumentation, configuration, and test coverage to drive reliability and business value.
In May 2025, delivered the Energy Usage Dashboard in sustainable-computing-io/kepler to visualize node-level power metrics across energy zones and instances, including historical, total, average, and current Watts. Fixed the CI workflow to correctly fetch the head commit from forked PRs, enabling accurate dependency analysis for external contributions. Added tests for the node power metrics dashboard to improve reliability and prevent regressions. These changes enhance data-driven energy optimization capabilities and strengthen external contribution workflows, with measurable improvements in monitoring, quality assurance, and security posture.
In May 2025, delivered the Energy Usage Dashboard in sustainable-computing-io/kepler to visualize node-level power metrics across energy zones and instances, including historical, total, average, and current Watts. Fixed the CI workflow to correctly fetch the head commit from forked PRs, enabling accurate dependency analysis for external contributions. Added tests for the node power metrics dashboard to improve reliability and prevent regressions. These changes enhance data-driven energy optimization capabilities and strengthen external contribution workflows, with measurable improvements in monitoring, quality assurance, and security posture.
November 2024 performance highlights for sustainable-computing-io/kepler-metal-ci. Delivered end-to-end CI/CD improvements across AWS runners, observability, training log management, and CI stability. Implementations included reusable AWS EC2 runner workflows with key-name authentication, Prometheus-based monitoring enhancements, standardized training log naming and dated archival, and CI environment stability fixes. Additionally, AWS-trained model artifacts were reorganized under a dedicated train-validate-e2e-aws path to improve artifact traceability and provider separation.
November 2024 performance highlights for sustainable-computing-io/kepler-metal-ci. Delivered end-to-end CI/CD improvements across AWS runners, observability, training log management, and CI stability. Implementations included reusable AWS EC2 runner workflows with key-name authentication, Prometheus-based monitoring enhancements, standardized training log naming and dated archival, and CI environment stability fixes. Additionally, AWS-trained model artifacts were reorganized under a dedicated train-validate-e2e-aws path to improve artifact traceability and provider separation.

Overview of all repositories you've contributed to across your timeline