nndeploy/nndeploy
Nov 2024 – Mar 2026
17 Months active
Languages Used
C++CMakeGitMarkdownPythonCUDAShellBash
Technical Skills
Build System ConfigurationC++C++ DevelopmentCI/CDCMakeCode Formatting
Always
PROFILE
Always
Over 17 months, Zhou Haoyi contributed deeply to the nndeploy/nndeploy repository, building and evolving a robust AI deployment framework focused on neural network inference, dynamic graph execution, and cross-platform packaging. Zhou engineered core features such as dynamic shape inference, DAG-based pipeline orchestration, and advanced tensor manipulation, using C++, Python, and CMake to ensure high performance and maintainability. His work included extensive documentation, CI/CD automation, and integration of technologies like ONNX and OpenCV, which improved deployment reliability and developer onboarding. Zhou’s technical approach emphasized modular design, code quality, and scalable architecture, resulting in a maintainable, production-ready machine learning infrastructure.
Overall Statistics
Feature vs Bugs
91%Features
Repository Contributions
816Total
Bugs
33
Commits
816
Features
339
Lines of code
458,907
Activity Months17
Your Network
24 peopleWork History
March 2026
1 Commits • 1 Features
Mar 1, 2026Concise monthly summary for 2026-03 highlighting delivered framework enhancements, alignment with business value, and technical craftsmanship for the nndeploy repository.
1 Commits • 1 Features
Mar 1, 2026Concise monthly summary for 2026-03 highlighting delivered framework enhancements, alignment with business value, and technical craftsmanship for the nndeploy repository.
March 2026
February 2026
8 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) monthly summary for nndeploy/nndeploy: Key features delivered include deployment framework documentation upgrades and codebase cleanup, as well as dynamic shape inference and tensor management enhancements. Major bugs fixed: none reported; targeted cleanups and refactors to align with deployment framework standards. Overall impact: improved deployment readiness, clearer docs with new model commands, faster and more reliable neural network inference, and lower maintenance burden. Technologies/skills demonstrated: documentation, codebase cleanup, dynamic shape inference, tensor allocation/reshape, memory management, and inference pipeline improvements; emphasis on performance, reliability, and onboarding.
February 2026
8 Commits • 2 Features
Feb 1, 2026February 2026 (2026-02) monthly summary for nndeploy/nndeploy: Key features delivered include deployment framework documentation upgrades and codebase cleanup, as well as dynamic shape inference and tensor management enhancements. Major bugs fixed: none reported; targeted cleanups and refactors to align with deployment framework standards. Overall impact: improved deployment readiness, clearer docs with new model commands, faster and more reliable neural network inference, and lower maintenance burden. Technologies/skills demonstrated: documentation, codebase cleanup, dynamic shape inference, tensor allocation/reshape, memory management, and inference pipeline improvements; emphasis on performance, reliability, and onboarding.
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for nndeploy/nndeploy focused on delivering enhanced tensor operation capabilities and improved observability. Implemented casting, shape-aware constant creation, and tensor expansion to streamline tensor manipulation, alongside logging and robust error handling improvements to boost debugging efficiency and user experience. This work establishes a reliable foundation for expanded tensor utilities and reduces debugging time in deployment workflows.
1 Commits • 1 Features
Jan 1, 2026January 2026 monthly summary for nndeploy/nndeploy focused on delivering enhanced tensor operation capabilities and improved observability. Implemented casting, shape-aware constant creation, and tensor expansion to streamline tensor manipulation, alongside logging and robust error handling improvements to boost debugging efficiency and user experience. This work establishes a reliable foundation for expanded tensor utilities and reduces debugging time in deployment workflows.
January 2026
December 2025
10 Commits • 4 Features
Dec 1, 2025December 2025: nndeploy/nndeploy delivered significant UX and performance improvements with a focus on documentation, deployment robustness, and streamlined operations. Highlights include comprehensive documentation enhancements, core deployment and model interpretation improvements, removal of the scheduler component to reduce complexity, and a release upgrade to 3.0.8. No major bug fixes were recorded in this month; work focused on feature delivery and reliability.
December 2025
10 Commits • 4 Features
Dec 1, 2025December 2025: nndeploy/nndeploy delivered significant UX and performance improvements with a focus on documentation, deployment robustness, and streamlined operations. Highlights include comprehensive documentation enhancements, core deployment and model interpretation improvements, removal of the scheduler component to reduce complexity, and a release upgrade to 3.0.8. No major bug fixes were recorded in this month; work focused on feature delivery and reliability.
November 2025
46 Commits • 18 Features
Nov 1, 2025Monthly summary for 2025-11 focused on delivering business value through modernization, reliability improvements, and clear documentation across the nndeploy/nndeploy repo. The month emphasized reducing maintenance overhead, stabilizing builds, and enabling faster deployments by tightening dependencies, upgrading core components, and improving developer onboarding.
46 Commits • 18 Features
Nov 1, 2025Monthly summary for 2025-11 focused on delivering business value through modernization, reliability improvements, and clear documentation across the nndeploy/nndeploy repo. The month emphasized reducing maintenance overhead, stabilizing builds, and enabling faster deployments by tightening dependencies, upgrading core components, and improving developer onboarding.
November 2025
October 2025
112 Commits • 48 Features
Oct 1, 2025October 2025 monthly summary for nndeploy/nndeploy highlighting a CI/CD and tooling uplift, platform-wide Android and LLM/MNN integration enhancements, and DAG/Node processing improvements. The release tightened build reliability, accelerated cross-platform AI deployment, and reduced operational friction through tooling, docs, and maintenance improvements.
October 2025
112 Commits • 48 Features
Oct 1, 2025October 2025 monthly summary for nndeploy/nndeploy highlighting a CI/CD and tooling uplift, platform-wide Android and LLM/MNN integration enhancements, and DAG/Node processing improvements. The release tightened build reliability, accelerated cross-platform AI deployment, and reduced operational friction through tooling, docs, and maintenance improvements.
September 2025
88 Commits • 38 Features
Sep 1, 2025September 2025 monthly summary for nndeploy/nndeploy focusing on stability, packaging, cross‑platform improvements, and graph/diffusion enhancements to support scalable deployments and reliable releases.
88 Commits • 38 Features
Sep 1, 2025September 2025 monthly summary for nndeploy/nndeploy focusing on stability, packaging, cross‑platform improvements, and graph/diffusion enhancements to support scalable deployments and reliable releases.
September 2025
August 2025
128 Commits • 54 Features
Aug 1, 2025Month: 2025-08 in nndeploy/nndeploy concentrated on reliability, automation, and platform-wide improvements to accelerate safe releases and broaden deployment reach. Delivered Node Management Improvements to remove legacy Node and strengthen Node handling, key for scaling deployments. Expanded CI/CD maturity with GitHub Actions and automation enhancements, enabling faster feedback and more reliable builds. Strengthened PyPI deployment workflows and test pipelines (Test PyPI and Test4PyPI) to improve pre-release validation. Implemented cross-platform packaging and OpenCV integration improvements for Windows, Linux, and macOS, alongside documentation and ReadTheDocs enhancements to improve onboarding and maintenance. Also focused on stability across critical areas by fixing internal indexing, graph runner deep copy behavior, and Windows-specific LLm/sd handling, reducing risk in production pipelines and runtime behavior.
August 2025
128 Commits • 54 Features
Aug 1, 2025Month: 2025-08 in nndeploy/nndeploy concentrated on reliability, automation, and platform-wide improvements to accelerate safe releases and broaden deployment reach. Delivered Node Management Improvements to remove legacy Node and strengthen Node handling, key for scaling deployments. Expanded CI/CD maturity with GitHub Actions and automation enhancements, enabling faster feedback and more reliable builds. Strengthened PyPI deployment workflows and test pipelines (Test PyPI and Test4PyPI) to improve pre-release validation. Implemented cross-platform packaging and OpenCV integration improvements for Windows, Linux, and macOS, alongside documentation and ReadTheDocs enhancements to improve onboarding and maintenance. Also focused on stability across critical areas by fixing internal indexing, graph runner deep copy behavior, and Windows-specific LLm/sd handling, reducing risk in production pipelines and runtime behavior.
July 2025
124 Commits • 55 Features
Jul 1, 2025July 2025 Monthly Summary for nndeploy/nndeploy focusing on business value and technical achievements. Key features delivered include: Status Update feature (UI/logic for system status indicator; commit 21e829ab6f4495e683106bb06c89e6d07d9b1406), Face Swap Improvements (enhanced face swap algorithm and video processing; commits 814852cba9e7874f10814b2a976f6e5bee860449 and 8f45e10f197f34b5740041dc6e5ab81b800b3511), Python Edge Handling Improvements (improved edge handling for Python components in the pipeline; commits 2ba80747378d83ed5bbf408df84918e40060ca3e and d55053395c6022cc9db588e399242322c7392f5b), Python DAG and DAG Parallel Enhancements (improvements to DAG logic and parallel execution in Python; commits 9e7ed2f06d0b3b6ad8722bb02009ce8c62080cb2, f3766948a22853e255955f8e7cb3e0c624c6d2df, f57a64383420b9cd4a27f1547a80f8a856e7b995, 08ccb9fd9b9a25db35ad810b25760a774df402df), Python Pipeline and Parallel Processing Enhancements (pipeline parallelism and related improvements; commits 9557f2828adb70a0fb5681df2926a62b1a9b0290 and 8907f5abf2712599b2167f7b12f0b0261b161df0), Memory management and reliability improvements (Memory Leak Fix and Memory Leak Improvements; commits d648f6e0e58de681f10502ec92e0ac9cf51765b2 and 4394827fee1d5b0b996f10db7ecf2377c31a6968), ONNX Runtime Inference Improvements (improved ONNX runtime inference; commits d2ad5e97899c08fb42ed05acf2f39affd8d22259 and da008a90771663f8b3e5a4bc7c22ad46e7b37c37)
124 Commits • 55 Features
Jul 1, 2025July 2025 Monthly Summary for nndeploy/nndeploy focusing on business value and technical achievements. Key features delivered include: Status Update feature (UI/logic for system status indicator; commit 21e829ab6f4495e683106bb06c89e6d07d9b1406), Face Swap Improvements (enhanced face swap algorithm and video processing; commits 814852cba9e7874f10814b2a976f6e5bee860449 and 8f45e10f197f34b5740041dc6e5ab81b800b3511), Python Edge Handling Improvements (improved edge handling for Python components in the pipeline; commits 2ba80747378d83ed5bbf408df84918e40060ca3e and d55053395c6022cc9db588e399242322c7392f5b), Python DAG and DAG Parallel Enhancements (improvements to DAG logic and parallel execution in Python; commits 9e7ed2f06d0b3b6ad8722bb02009ce8c62080cb2, f3766948a22853e255955f8e7cb3e0c624c6d2df, f57a64383420b9cd4a27f1547a80f8a856e7b995, 08ccb9fd9b9a25db35ad810b25760a774df402df), Python Pipeline and Parallel Processing Enhancements (pipeline parallelism and related improvements; commits 9557f2828adb70a0fb5681df2926a62b1a9b0290 and 8907f5abf2712599b2167f7b12f0b0261b161df0), Memory management and reliability improvements (Memory Leak Fix and Memory Leak Improvements; commits d648f6e0e58de681f10502ec92e0ac9cf51765b2 and 4394827fee1d5b0b996f10db7ecf2377c31a6968), ONNX Runtime Inference Improvements (improved ONNX runtime inference; commits d2ad5e97899c08fb42ed05acf2f39affd8d22259 and da008a90771663f8b3e5a4bc7c22ad46e7b37c37)
July 2025
June 2025
79 Commits • 35 Features
Jun 1, 2025June 2025 performance summary for nndeploy/nndeploy. Highlights include major DAG core improvements with edge handling and Python integration, extensive enhancements to Python tooling, serialization, and JSON handling, and significant DAG/detection pipeline improvements enabling more robust, parallelized workflows (including YOLO and Python DAG integration). The Detection Demo flow was improved and JSON generation was disabled to prevent interference, increasing demo stability. Windows-specific fixes (API exports, compile issues) and Python/build environment updates improved cross-platform reliability. Documentation and PyPI packaging improvements were completed to improve onboarding and distribution. Overall, the month delivered stronger pipeline reliability, faster iteration cycles, and a solid foundation for scalable AI deployments across Linux and Windows.
June 2025
79 Commits • 35 Features
Jun 1, 2025June 2025 performance summary for nndeploy/nndeploy. Highlights include major DAG core improvements with edge handling and Python integration, extensive enhancements to Python tooling, serialization, and JSON handling, and significant DAG/detection pipeline improvements enabling more robust, parallelized workflows (including YOLO and Python DAG integration). The Detection Demo flow was improved and JSON generation was disabled to prevent interference, increasing demo stability. Windows-specific fixes (API exports, compile issues) and Python/build environment updates improved cross-platform reliability. Documentation and PyPI packaging improvements were completed to improve onboarding and distribution. Overall, the month delivered stronger pipeline reliability, faster iteration cycles, and a solid foundation for scalable AI deployments across Linux and Windows.
May 2025
68 Commits • 23 Features
May 1, 2025May 2025 highlights: Major expansion of the DAG/graph subsystem with dynamic graphs, improved visualization and JSON serialization, enabling more flexible and observable workflows. Introduced Composite Node feature and pipeline-parallel execution to accelerate AI workflows. ONNX static and editing improvements to improve model interoperability and editing workflow. Build and runtime stack enhancements including CMake/config improvements, OpenCL/OpenCV updates, and Python integration, boosting performance, portability, and developer productivity. Comprehensive documentation overhaul with extensive docs, readme and changelog updates, plus JSON schema and DAG JSON interoperability improvements. Stabilized builds with critical fixes including protobuf conflict resolution and codec debugging improvements to reduce noise and regressions.
68 Commits • 23 Features
May 1, 2025May 2025 highlights: Major expansion of the DAG/graph subsystem with dynamic graphs, improved visualization and JSON serialization, enabling more flexible and observable workflows. Introduced Composite Node feature and pipeline-parallel execution to accelerate AI workflows. ONNX static and editing improvements to improve model interoperability and editing workflow. Build and runtime stack enhancements including CMake/config improvements, OpenCL/OpenCV updates, and Python integration, boosting performance, portability, and developer productivity. Comprehensive documentation overhaul with extensive docs, readme and changelog updates, plus JSON schema and DAG JSON interoperability improvements. Stabilized builds with critical fixes including protobuf conflict resolution and codec debugging improvements to reduce noise and regressions.
May 2025
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 focused on delivering two high-impact NDDeploy features that improve deployment readiness and runtime efficiency for neural network deployments. The work emphasizes IR/ONNX compatibility, end-to-end demo orchestration, and resource-aware execution across multiple networks. Overall, these efforts reduce deployment risk, accelerate integration, and improve demo fidelity in production-like scenarios.
April 2025
7 Commits • 2 Features
Apr 1, 2025April 2025 focused on delivering two high-impact NDDeploy features that improve deployment readiness and runtime efficiency for neural network deployments. The work emphasizes IR/ONNX compatibility, end-to-end demo orchestration, and resource-aware execution across multiple networks. Overall, these efforts reduce deployment risk, accelerate integration, and improve demo fidelity in production-like scenarios.
March 2025
51 Commits • 26 Features
Mar 1, 2025March 2025 performance summary for nndeploy/nndeploy: Delivered substantial DAG and dynamic graph enhancements, expanded Python bindings, and pipeline/runtime optimizations, improving deployment reliability, throughput, and developer experience. Focused on core stability, testing, and maintainability through cleanup and documentation improvements.
51 Commits • 26 Features
Mar 1, 2025March 2025 performance summary for nndeploy/nndeploy: Delivered substantial DAG and dynamic graph enhancements, expanded Python bindings, and pipeline/runtime optimizations, improving deployment reliability, throughput, and developer experience. Focused on core stability, testing, and maintainability through cleanup and documentation improvements.
March 2025
February 2025
52 Commits • 23 Features
Feb 1, 2025February 2025 (Month: 2025-02) monthly summary for nndeploy/nndeploy highlighting business value and technical achievements across core ML deployment, tooling, and developer experience. The month delivered significant model and pipeline improvements, stronger bindings and Python interfaces, and improved reliability and UX, enabling faster deployment cycles and more scalable inference. Summary of impact: - Strengthened ML deployment capabilities with a modernized Classification/Detect/Segment model pipeline, including a comprehensive refactor for maintainability and faster iteration. - Expanded capabilities for streaming and event-driven workflows with CUDA stream/event optimizations, enabling higher throughput and lower latency in end-to-end deployment pipelines. - Extended Python bindings and interfaces, including Pybind11 device bindings, Python-facing detection utilities, and enhanced core bindings/docs, improving accessibility for data scientists and engineers. - Hardened graph execution with IR and graph-core improvements, including edge handling, DAG scheduling, thread pool enhancements, and improved dynamic graph support, enabling more scalable and reliable model serving. - Strengthened reliability and developer experience with Protobuf dependency conflict resolution, directory checks validation, and accompanying documentation/UX improvements to speed onboarding and reduce churn.
February 2025
52 Commits • 23 Features
Feb 1, 2025February 2025 (Month: 2025-02) monthly summary for nndeploy/nndeploy highlighting business value and technical achievements across core ML deployment, tooling, and developer experience. The month delivered significant model and pipeline improvements, stronger bindings and Python interfaces, and improved reliability and UX, enabling faster deployment cycles and more scalable inference. Summary of impact: - Strengthened ML deployment capabilities with a modernized Classification/Detect/Segment model pipeline, including a comprehensive refactor for maintainability and faster iteration. - Expanded capabilities for streaming and event-driven workflows with CUDA stream/event optimizations, enabling higher throughput and lower latency in end-to-end deployment pipelines. - Extended Python bindings and interfaces, including Pybind11 device bindings, Python-facing detection utilities, and enhanced core bindings/docs, improving accessibility for data scientists and engineers. - Hardened graph execution with IR and graph-core improvements, including edge handling, DAG scheduling, thread pool enhancements, and improved dynamic graph support, enabling more scalable and reliable model serving. - Strengthened reliability and developer experience with Protobuf dependency conflict resolution, directory checks validation, and accompanying documentation/UX improvements to speed onboarding and reduce churn.
January 2025
10 Commits • 4 Features
Jan 1, 2025January 2025 delivered a major DAG API overhaul for nndeploy/nndeploy, including a complete refactor of node/graph constructors, added inputs/outputs accessors, and improved error handling, culminating in a refined classification graph workflow for image classification. Documentation and demos were updated to reflect the new API and model usage, with model references and download links for ModelScope and Hugging Face (e.g., yolov8n/yolov5s). Build, contribution, and cross-platform testing improved through a new CONTRIBUTING.md, SSH-clone guidance for Ascend servers, and enhanced CMake/test configurations. Time profiler enhancements added detailed call timings, exclusion of warmup iterations, new print options, and warmup data removal, along with a transpose documentation fix and updated environment settings. These changes improve pipeline reliability, accelerate onboarding, and amplify performance visibility to drive faster feature delivery and better operational efficiency.
10 Commits • 4 Features
Jan 1, 2025January 2025 delivered a major DAG API overhaul for nndeploy/nndeploy, including a complete refactor of node/graph constructors, added inputs/outputs accessors, and improved error handling, culminating in a refined classification graph workflow for image classification. Documentation and demos were updated to reflect the new API and model usage, with model references and download links for ModelScope and Hugging Face (e.g., yolov8n/yolov5s). Build, contribution, and cross-platform testing improved through a new CONTRIBUTING.md, SSH-clone guidance for Ascend servers, and enhanced CMake/test configurations. Time profiler enhancements added detailed call timings, exclusion of warmup iterations, new print options, and warmup data removal, along with a transpose documentation fix and updated environment settings. These changes improve pipeline reliability, accelerate onboarding, and amplify performance visibility to drive faster feature delivery and better operational efficiency.
January 2025
December 2024
14 Commits • 3 Features
Dec 1, 2024Month 2024-12 performance-focused summary for nndeploy/nndeploy. Delivered substantial feature enhancements and stability improvements across Ascend CL deployment, ONNX tooling, and core operator implementations, with a strong emphasis on deployment reliability, model compatibility, and maintainability. The work enabled faster, more reliable model deployment on Ascend hardware, improved static-shape inference for ONNX models, and reinforced the robustness of core operators used in production deployments.
December 2024
14 Commits • 3 Features
Dec 1, 2024Month 2024-12 performance-focused summary for nndeploy/nndeploy. Delivered substantial feature enhancements and stability improvements across Ascend CL deployment, ONNX tooling, and core operator implementations, with a strong emphasis on deployment reliability, model compatibility, and maintainability. The work enabled faster, more reliable model deployment on Ascend hardware, improved static-shape inference for ONNX models, and reinforced the robustness of core operators used in production deployments.
November 2024
17 Commits • 2 Features
Nov 1, 2024Month: 2024-11 | Repository: nndeploy/nndeploy Overview: Delivered core modernization of serialization workflows and expanded model/demos, while stabilizing the build and dependencies. The work strengthens business value by enabling consistent model formats, faster iteration, and richer demo capabilities, setting the stage for scalable deployments of NN inference features.
17 Commits • 2 Features
Nov 1, 2024Month: 2024-11 | Repository: nndeploy/nndeploy Overview: Delivered core modernization of serialization workflows and expanded model/demos, while stabilizing the build and dependencies. The work strengthens business value by enabling consistent model formats, faster iteration, and richer demo capabilities, setting the stage for scalable deployments of NN inference features.
November 2024
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Quality Metrics
Correctness84.8%
Maintainability85.2%
Architecture82.6%
Performance74.2%
AI Usage21.6%
Skills & Technologies
Programming Languages
BashBatchCC++CMakeCMakeScriptCSSCUDADockerfileDoxyfile
Technical Skills
AI AccelerationAI DeploymentAI IntegrationAI deploymentAI deployment frameworkAI frameworksAI integrationAI model deploymentAI/ML IntegrationAPI ConfigurationAPI DesignAPI DevelopmentAPI DocumentationAPI IntegrationAPI Refactoring
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