Concise monthly summary for openvinotoolkit/training_extensions - May 2026. Focused on reliability, stability, and user experience improvements, with clear business value from training reliability, API stability, and security hardening.

April 2026 focused on stabilizing and modernizing the training extensions across two repos, delivering robust training/configuration handling, reliable image execution, and improved model loading with internal mirrors and API upgrades. Added configurable data intensity mapping and maintained code health through comprehensive docs and dependency cleanup. These changes reduce training-time errors, improve reproducibility, and enable smoother scaling of model training workflows.

March 2026: Delivered substantial enhancements to training configuration, stabilized runtime processes, and improved installer/demo UX for open-edge-platform/training_extensions. Strengthened reliability with critical bug fixes and upgraded core dependencies to support longer-term product durability.

February 2026 performance summary focusing on data integrity, API usability, and CI/CD improvements across training_extensions and datumaro. Key features delivered include dataset revisions robustness, default media review status on upload, API/model naming clarity, and metadata capabilities via StringField. Major bugs fixed include 404 errors when listing dataset revisions after deletion and edge-case annotation validation. The work accelerates reliability, developer productivity, and richer metadata support, with strong business value from more robust data workflows and smoother releases.

January 2026: Delivered robust dev workflows, API capabilities, and compatibility updates for open-edge-platform/training_extensions, translating enhancements into measurable developer productivity and product stability.

December 2025 monthly summary for open-edge-platform/training_extensions. Delivered a set of stability, data integrity, and training enhancements across CI/CD, data conversion, API, and cross‑platform support, enabling faster deployment cycles and more reliable model training. Key outcomes include robust CI/CD workflow improvements with SI-sized Docker image reporting and streamlined Python setup, upgrades to datumaro/OTX converter libraries for a more robust import structure, an API-level user-reviewed annotations flag to improve data integrity, macOS Torch/torchvision dependency fixes for reliable installs, and Geti Tune training loop plus OTX framework upgrades to boost training capabilities and performance. Impact: reduced deployment fragility, improved data quality and traceability, and accelerated model development cycles across teams. These changes also enhance cross‑platform usability and developer productivity by providing clearer reporting and more stable tooling.

November 2025 performance summary: Delivered cross-repo capabilities for training, deployment, and SDK usability, with strong focus on business value and reliability. Highlights include enabling CPU/XPU/CUDA training via Geti Tune with new OTX dependency, adding Docker image targets for CPU/GPU/XPU, extending prediction datasets with confidence scores for better evaluation, publicly exposing DatasetFormat in the Geti SDK, and modernizing Datumaro Python version support (3.13/3.14) with restoration of Python 3.10 to maintain compatibility across dependencies. In addition, ongoing improvements to code quality and CI/CD pipelines strengthened release reliability and overall developer velocity.

October 2025 (2025-10) monthly summary for open-edge-platform/training_extensions: Delivered a set of setup, compatibility, and quality improvements designed to reduce friction for users and accelerate production deployments while strengthening model evaluation and maintenance. Key feature deliveries include: automatic asset downloads and a CPU-only installation mode to boost setup flexibility and enable CPU-only environments; dependency and environment upgrades (Datumaro, OpenVINO Model API, NumPy) to improve compatibility and performance; API and configuration cleanup to streamline the API surface; expanded evaluation metrics and evaluators for multi-class, multi-label, detection, and instance segmentation with supporting precision, recall, and mAP measures; comprehensive documentation for model management and training features to support onboarding and lifecycle management; robustness improvements for model loading with unified path resolution and explicit file existence checks, backed by unit tests; PR template simplification to accelerate contributor onboarding and governance. The work collectively reduces maintenance burden, broadens deployment options across CPU environments, and enhances model evaluation fidelity, contributing to faster time-to-value for customers and more reliable, scalable training extensions.

September 2025 highlights across two training_extensions repositories focused on increasing development velocity, improving code quality, and strengthening CI/CD reliability, while enhancing API documentation and data integrity. Key outcomes include cross-repo CI/CD automation hardening, expanded code review coverage for frontend changes, and robust shutdown handling for worker processes, enabling safer scaling and fewer production incidents. In addition, comprehensive Geti Tune API documentation was delivered to improve developer onboarding and integration, and a data collection bug fix ensures correct annotation shapes for bounding boxes across datasets. These efforts contributed to faster PR cycles, reduced manual toil, and stronger governance and maintainability for future iterations.

August 2025: Focused on stabilizing and modernizing the Geti SDK (open-edge-platform/geti-sdk). Delivered upgraded training configuration capabilities, improved model introspection, and ensured reliability of model group retrieval. Implemented dependency cleanup by removing Datumaro, dropped Python 3.9 support, and aligned CI/CD for Python 3.13. Strengthened security with updated numpy/urllib3 versions and improved code quality with Ruff config and formatting. Updated documentation, onboarding, and versioning to v2.13.0, setting the stage for forward-looking features and safer deployments. Business value: clearer model parameter reporting, fewer runtime errors, easier contributor onboarding, and improved security posture.

Month: 2025-07 | Focused on delivering a stable SDK release, improving deployment reliability, and streamlining distribution artifacts. Key outcomes include SDK 2.12.0 release with dependency upgrades and artifact cleanup, plus fixes to race conditions post-import and removal of a legacy deployment check. These efforts reduce deployment failures, improve runtime stability, and offer a cleaner, maintainable release channel.

June 2025 monthly summary: Delivered tooling modernization and release readiness improvements for geti-sdk, consolidating code quality checks and stabilizing dependencies. Implemented IPython/protobuf compatibility fixes and updated release artifacts to reflect features and security updates.

May 2025 performance summary for open-edge-platform/geti-sdk: Key features delivered: - SDK Release: Version 2.10.0 released; CHANGELOG updated to reflect milestone, enabling stable migration and clear release notes for downstream users. - Import to existing projects: Enhanced data import workflow with new JobType values for preparing and performing imports into existing projects; DatasetMetadata extended with optional use_for_training and creation_time fields to support experimentation and tracking. - Project labels: Added update_labels API to ProjectClient to modify label colors and hotkeys with validation to preserve data integrity. Major bugs fixed: - Model group parsing: lifecycle_stage support: Fixed model group response parsing by adding lifecycle_stage to ModelSummary and improving deserialization of nested dictionaries within lists; tests updated. - Version parsing bug fix: prerelease handling: Ensure build_tag initializes correctly for version tags with and without prerelease components; new unit tests verify the fix. Overall impact and accomplishments: - Strengthened data onboarding, metadata accuracy, and release confidence. The changes reduce downstream errors, improve data integrity, and streamline model management for users integrating early-stage datasets and labels across projects. Release readiness improved through automated version handling and robust parsing. Technologies/skills demonstrated: - Python data modeling and deserialization, REST API client design, unit testing, and release tooling. Emphasis on data integrity (label validation, dataset metadata), edge-case handling in version parsing, and comprehensive test coverage. Commit highlights: - 0b2084b24821e3ee3c7196dd8b950553a9af9b0f: update version and changelog (#592) - 01d9f0a561b1c057e92914ee89111b7f132835bd: Bugfix: unrecognized JobType for import to existing dataset (#594) - 36aa851bfef0c99544c6729a6763eeb6e703841c: Bugfix: unrecognized 'lifecycle_stage' key in model group response (#593) - b25fd74981168709d9d6ae8e45d180204e866f52: Bugfix: build_tag not properly initialized when version tag does not include prerelease (#596) - 307cde9d455689b6af1d263fab5209edbe24757c: ProjectClient: add method to update label colors/hotkeys (#599)

April 2025 monthly summary for open-edge-platform/geti-sdk focused on improving submission clarity and triage efficiency through repository template standardization. Implemented dedicated templates for PRs, bug reports, feature requests, and user questions to ensure consistent data capture and faster contributor onboarding.

March 2025 monthly summary for open-edge-platform/geti-sdk: Delivered release-readiness improvements and networking reliability through dynamic proxy configuration, with a focused effort on versioning, changelogs, and observability to accelerate go-to-market and stable deployments.

February 2025: Focused on reliability, deployment readiness, and user guidance for the geti-sdk. Delivered a bugfix for notebook visualization (correct color channel order) and refactored notebook to streamline inference setup on local CPU and OVMS. Rolled out SDK version bumps to 2.7.0 and 2.8.0 with updated CHANGELOG and release notes. Implemented robust workspace ID handling with a get_workspace_id function and clear guidance via MultipleWorkspacesException. These changes improve visual accuracy, reduce setup friction, enhance release traceability, and improve multi-workspace workflows across deployments.

December 2024: Delivered stability and resilience improvements in geti-sdk, focusing on API compatibility, robust proxy configuration, and maintainability. Key outcomes include enhanced HTTP response parsing to tolerate extraneous keys and updated endpoint paths for training/optimization; strengthened VCR proxy configuration to support partial proxy settings; and a code quality cleanup in the Model data model to improve readability. These changes reduce integration risk with evolving API surfaces, lower support overhead, and lay groundwork for future features.