ML-TANGO/TANGO
Oct 2024 – Sep 2025
4 Months active
Languages Used
PythonDockerfileKotlinShell
Technical Skills
Backend DevelopmentComputer VisionAndroid DevelopmentCI/CDCloud DeploymentCode Generation
giantlkh
PROFILE
Giantlkh
Kyunghee contributed to the ML-TANGO/TANGO repository by developing and stabilizing deployment pipelines for machine learning workloads across Android and cloud environments. She implemented a frame-based method for camera dimension retrieval, improving UI reliability by reading actual video frames instead of relying on metadata. Her work included end-to-end EdgeTPU-accelerated Android deployment, ONNX model conversion, and integration of dynamic input handling. Kyunghee enhanced Docker-based build reproducibility by pinning framework versions and optimizing Python environments, reducing deployment variability. Using Python, Dockerfile, and Kotlin, she addressed cross-environment compatibility, streamlined CI/CD workflows, and enabled safer rollbacks, demonstrating depth in DevOps and machine learning deployment.
Overall Statistics
Feature vs Bugs
57%Features
Repository Contributions
11Total
Bugs
3
Commits
11
Features
4
Lines of code
695
Activity Months4
Your Network
18 peopleWork History
September 2025
1 Commits
Sep 1, 2025September 2025 - ML-TANGO/TANGO: Stabilized deployment environment to ensure reproducible builds and reduce deployment-time issues. Implemented exact version pinning for Docker and ML frameworks (TensorFlow, ONNX, TVM) in the Dockerfile, addressing cross-environment compatibility risks and improving reliability for CI/CD pipelines. This baseline enables safer rollbacks and faster onboarding for new team members.
1 Commits
Sep 1, 2025September 2025 - ML-TANGO/TANGO: Stabilized deployment environment to ensure reproducible builds and reduce deployment-time issues. Implemented exact version pinning for Docker and ML frameworks (TensorFlow, ONNX, TVM) in the Dockerfile, addressing cross-environment compatibility risks and improving reliability for CI/CD pipelines. This baseline enables safer rollbacks and faster onboarding for new team members.
September 2025
August 2025
2 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 (ML-TANGO/TANGO): Focused on stabilizing and enabling reproducible Docker-based builds for TVM. Implemented a stable Python image and noninteractive installations, and pinned TVM to a specific version via tarball to ensure deterministic deployments. Commits contributing to this work include 36e562f714fe9f9433053e408d50d2c870784863 (modify Dockerfile python3.9-bullseye) and d47324b943cdfb5d1c5adfa1fd3ee9a7e68e1560 (Dockerfile 수정 중). Major bugs fixed: none reported this month. Impact: reduced build flakiness, improved reproducibility across dev/test/prod environments, enabling faster, more reliable deployments. Technologies/skills demonstrated: Docker, Python environment management, tarball-based TVM version pinning, noninteractive installations, and containerized build pipelines.
August 2025
2 Commits • 1 Features
Aug 1, 2025Monthly summary for 2025-08 (ML-TANGO/TANGO): Focused on stabilizing and enabling reproducible Docker-based builds for TVM. Implemented a stable Python image and noninteractive installations, and pinned TVM to a specific version via tarball to ensure deterministic deployments. Commits contributing to this work include 36e562f714fe9f9433053e408d50d2c870784863 (modify Dockerfile python3.9-bullseye) and d47324b943cdfb5d1c5adfa1fd3ee9a7e68e1560 (Dockerfile 수정 중). Major bugs fixed: none reported this month. Impact: reduced build flakiness, improved reproducibility across dev/test/prod environments, enabling faster, more reliable deployments. Technologies/skills demonstrated: Docker, Python environment management, tarball-based TVM version pinning, noninteractive installations, and containerized build pipelines.
November 2024
7 Commits • 2 Features
Nov 1, 2024Monthly performance summary for 2024-11 focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated for ML-TANGO/TANGO. The month emphasized edge deployment optimization, cross-cloud deployment scalability, and reliability improvements to the code generation and container workflows.
7 Commits • 2 Features
Nov 1, 2024Monthly performance summary for 2024-11 focusing on key features delivered, major bugs fixed, overall impact, and technologies demonstrated for ML-TANGO/TANGO. The month emphasized edge deployment optimization, cross-cloud deployment scalability, and reliability improvements to the code generation and container workflows.
November 2024
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 - ML-TANGO/TANGO: Delivered a frame-based approach to determine camera dimensions for Streamer, replacing reliance on metadata from get_meta_data() to read an actual video frame. This change improves the reliability of width/height detection for Streamer in web and trtweb index.db files, leading to more accurate rendering and fewer dimension-related UI issues across platforms.
October 2024
1 Commits • 1 Features
Oct 1, 2024October 2024 - ML-TANGO/TANGO: Delivered a frame-based approach to determine camera dimensions for Streamer, replacing reliance on metadata from get_meta_data() to read an actual video frame. This change improves the reliability of width/height detection for Streamer in web and trtweb index.db files, leading to more accurate rendering and fewer dimension-related UI issues across platforms.
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Quality Metrics
Correctness81.8%
Maintainability81.8%
Architecture74.6%
Performance69.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
DockerfileKotlinPythonShell
Technical Skills
Android DevelopmentBackend DevelopmentBuild SystemsCI/CDCloud DeploymentCode GenerationComputer VisionContainerizationDebuggingDependency ManagementDeployment ConfigurationDevOpsDockerEdge TPUEdgeTPU
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