openvinotoolkit/model_server
Nov 2024 – Feb 2025
4 Months active
Languages Used
C++DockerfileMakefilePythonShellMarkdownBashBatch
Technical Skills
API DevelopmentBuild System ConfigurationBuild SystemsC++ DevelopmentCI/CDCross-Platform Compatibility
Bartosz Strzelecki
PROFILE
Bartosz Strzelecki
Bartosz Strzelecki contributed to the openvinotoolkit/model_server repository by developing features that enhanced model serving flexibility, observability, and cross-platform support. He unified input parsing for embedding endpoints, enabling both string and tokenized integer inputs, and improved build systems to support deployment on Ubuntu, Red Hat, and Windows. Using C++ and Python, Bartosz implemented new MediaPipe metrics, including graph processing time and error tracking, and integrated these with Grafana dashboards for real-time performance monitoring. His work also included refining demo setups, updating documentation, and stabilizing test infrastructure, resulting in more reliable deployments and streamlined onboarding for diverse production environments.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
13Total
Bugs
3
Commits
13
Features
6
Lines of code
2,131
Activity Months4
Your Network
1980 people
Work History
February 2025
5 Commits • 2 Features
Feb 1, 2025February 2025: OpenVINO Model Server delivered Windows-friendly real-time streaming demos and enhanced observability. Key features include Windows deployment guidance, correct RTSP port (8554), robust video download options, and alternate installation paths for RTSP server to reduce onboarding errors. Additionally, MediaPipe performance metrics and dashboards were introduced, including a latency metric, timestamp handling refinements, and Grafana dashboards to improve visibility and performance tuning.
5 Commits • 2 Features
Feb 1, 2025February 2025: OpenVINO Model Server delivered Windows-friendly real-time streaming demos and enhanced observability. Key features include Windows deployment guidance, correct RTSP port (8554), robust video download options, and alternate installation paths for RTSP server to reduce onboarding errors. Additionally, MediaPipe performance metrics and dashboards were introduced, including a latency metric, timestamp handling refinements, and Grafana dashboards to improve visibility and performance tuning.
February 2025
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for openvinotoolkit/model_server: Delivered a new graph processing time metric (ovms_graph_processing_time_us) to measure MediaPipe graph processing time in microseconds. Implemented timer logic, histogram metric, and metric reporting configurations, with accompanying documentation updates. This enables precise performance visibility and faster root-cause analysis for graph execution latency.
January 2025
1 Commits • 1 Features
Jan 1, 2025January 2025 monthly summary for openvinotoolkit/model_server: Delivered a new graph processing time metric (ovms_graph_processing_time_us) to measure MediaPipe graph processing time in microseconds. Implemented timer logic, histogram metric, and metric reporting configurations, with accompanying documentation updates. This enables precise performance visibility and faster root-cause analysis for graph execution latency.
December 2024
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly work summary for openvinotoolkit/model_server focusing on delivering enhanced observability, robust test infrastructure, and clear business value through improved monitoring and reliability.
3 Commits • 1 Features
Dec 1, 2024December 2024 monthly work summary for openvinotoolkit/model_server focusing on delivering enhanced observability, robust test infrastructure, and clear business value through improved monitoring and reliability.
December 2024
November 2024
4 Commits • 2 Features
Nov 1, 2024Month: 2024-11 — OpenVINO Model Server development. Key features delivered this month include enabling Embeddings Endpoint to accept tokenized input as direct integer token arrays and unifying input parsing for strings vs. integers, increasing flexibility and robustness of embedding generation. Additionally, cross-OS build support was added to the demos by introducing BASE_OS in Makefiles for face_blur and onnx demos, expanding deployment options to Ubuntu or Red Hat environments. Major bugs fixed include correcting the Mediapipe demo’s setup path so the hand_landmark module is placed under mediapipe/modules/hand_landmark/ and updating the CPU extension discovery path by moving the compiled extension to /opt and adjusting tests to locate it there, improving reliability and discoverability. Overall impact: these changes reduce setup friction, improve reliability of demos and extensions in production deployments, and broaden platform compatibility, enabling easier adoption by a wider set of customers. Technologies and skills demonstrated: input parsing unification, tokenized input handling for embeddings, cross-OS build configuration, path management for demos and extensions, and test updates to reflect new file locations.
November 2024
4 Commits • 2 Features
Nov 1, 2024Month: 2024-11 — OpenVINO Model Server development. Key features delivered this month include enabling Embeddings Endpoint to accept tokenized input as direct integer token arrays and unifying input parsing for strings vs. integers, increasing flexibility and robustness of embedding generation. Additionally, cross-OS build support was added to the demos by introducing BASE_OS in Makefiles for face_blur and onnx demos, expanding deployment options to Ubuntu or Red Hat environments. Major bugs fixed include correcting the Mediapipe demo’s setup path so the hand_landmark module is placed under mediapipe/modules/hand_landmark/ and updating the CPU extension discovery path by moving the compiled extension to /opt and adjusting tests to locate it there, improving reliability and discoverability. Overall impact: these changes reduce setup friction, improve reliability of demos and extensions in production deployments, and broaden platform compatibility, enabling easier adoption by a wider set of customers. Technologies and skills demonstrated: input parsing unification, tokenized input handling for embeddings, cross-OS build configuration, path management for demos and extensions, and test updates to reflect new file locations.
Activity
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Quality Metrics
Correctness85.4%
Maintainability81.6%
Architecture75.4%
Performance74.6%
AI Usage20.0%
Skills & Technologies
Programming Languages
BashBatchC++ConsoleDockerfileMakefileMarkdownPythonShell
Technical Skills
API DevelopmentAPI IntegrationBuild System ConfigurationBuild SystemsC++C++ DevelopmentCI/CDCross-Platform CompatibilityData StructuresData VisualizationDemo SetupDocumentationJSON ParsingMediaPipeMetrics
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