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Kaiyi

PROFILE

Kaiyi

Kaiyi Liu developed and enhanced CI/CD workflows and monitoring solutions for the sustainable-computing-io/kepler-metal-ci and kepler repositories. Over two months, Kaiyi delivered an end-to-end AWS EC2 runner management system, integrated Prometheus-based observability, and stabilized CI environments using Ansible and Shell scripting. In kepler, Kaiyi built an Energy Usage Dashboard to visualize node-level power metrics, leveraging Go and Python for backend development and data visualization. The work included standardizing training log management, improving artifact traceability, and fixing CI workflows for external contributions. These contributions demonstrate depth in cloud automation, system monitoring, and robust testing practices across complex DevOps pipelines.

Overall Statistics

Feature vs Bugs

71%Features

Repository Contributions

20Total
Bugs
2
Commits
20
Features
5
Lines of code
1,017
Activity Months2

Work History

May 2025

2 Commits • 1 Features

May 1, 2025

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

18 Commits • 4 Features

Nov 1, 2024

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.

Activity

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Quality Metrics

Correctness86.0%
Maintainability91.0%
Architecture84.0%
Performance83.0%
AI Usage20.0%

Skills & Technologies

Programming Languages

BashGoJSONPythonShellYAML

Technical Skills

AWSAnsibleBackend DevelopmentCI/CDCloud ComputingConfigurationDashboard DevelopmentData VisualizationDevOpsGitHub ActionsPrometheusShell ScriptingSystem AdministrationSystem ConfigurationSystem Monitoring

Repositories Contributed To

2 repos

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

sustainable-computing-io/kepler-metal-ci

Nov 2024 Nov 2024
1 Month active

Languages Used

BashJSONShellYAML

Technical Skills

AWSAnsibleCI/CDCloud ComputingConfigurationDevOps

sustainable-computing-io/kepler

May 2025 May 2025
1 Month active

Languages Used

GoPythonShellYAML

Technical Skills

Backend DevelopmentCI/CDDashboard DevelopmentData VisualizationGitHub ActionsSystem Monitoring

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