2026-05 performance summary for openvinotoolkit/training_extensions. Delivered a suite of features that improve export quality, deployment flexibility, observability, and training efficiency, while upgrading dependencies and stabilizing streaming. Includes a targeted bugfix for instance segmentation export and broader improvements across data handling, visualization, and runtime configuration.

In April 2026, delivered cross-repo data interoperability improvements, enhanced media handling, and stronger observability, focused on Datumaro, training_extensions, and related tooling. The work improved data import fidelity, dataset export reliability, and CI/test quality, enabling faster iteration and more robust data pipelines for production use.

March 2026 performance summary for Datumaro and training_extensions focused on delivering cross-repo data tooling improvements that unlock broader data formats, media types, and safer workflows while accelerating delivery pipelines. Key outcomes include automatic dataset import across multiple formats, video-enabled data handling, safer and unified export/import workflows, VOC annotation and labeling enhancements, and CI/CD/build efficiency improvements that shorten feedback loops and increase reliability. These changes reduce manual data preparation, mitigate data loss risks, enable mixed media datasets, and accelerate experimentation and deployment.

February 2026 monthly highlights for the development team. Delivered robust data handling and extensibility across Datumaro and training_extensions, with a focus on reliability, performance, and usability for data pipelines and model training workflows.

January 2026 performance summary focused on strengthening data workflows and expanding dataset format support to improve training reliability and speed. Delivered end-to-end Datumaro integration for training extensions, enabling dataset handling for object detection, instance segmentation, and keypoint detection. Achieved significant performance improvements through multiprocessing-based data loading in OTXDataModule and enabling multi-worker test execution, reducing CI/test times. Improved code quality and developer experience via naming consistency refactor and Bash 3.x compatibility updates, along with documentation enhancements for the datumaro.experimental module. Expanded dataset format support and data integrity improvements in Datumaro, including Polars-based serialization improvements, COCO multi-layout support, category propagation during conversions, 16-bit image support, and YOLO format I/O.

December 2025 monthly summary for open-edge-platform/datumaro: Delivered robust color handling, COCO format support, expanded data converters, memory-efficient image loading, and improved dataset detection across the codebase. These changes enhance interoperability with common datasets, accelerate data import/export, and reduce memory footprint in large-scale workflows.

2025-11 Monthly Summary — Datumaro (open-edge-platform/datumaro) Key features delivered: - Dataset Import/Export Functionality: Added multi-format import/export capability with full dataset content (images, metadata, categories, schema) and robust serialization; introduced module-level export_dataset/import_dataset APIs and Dataset.export/from_file methods; support for exporting to ZIP; Parquet-backed Polars data for handling large datasets. Serializable via to_dict/from_dict for Categories, Fields, Schema. Groundwork for robust dataset interchange across formats and future compatibility. Major bugs fixed: - Documentation Build Fix and AttributeRename: Resolved failing docs builds and renamed AttributeInfo.annotation to AttributeInfo.field for clarity and consistency, including a version bump to 2.0.0. Overall impact and accomplishments: - Significantly improved data interoperability and workflow automation for dataset management, enabling reliable cross-format interchange with metadata and schema, and simplifying batch export/import in production pipelines. The changes enable teams to share and reuse datasets with richer context and versioning, reducing onboarding and integration time. Technologies/skills demonstrated: - Python, serialization/deserialization patterns, dataclasses, and module-level API design; Polars DataFrame integration with Parquet backups; type hints and union/typed arrays; numpy interoperability; documentation build processes and release versioning. Commits delivering these changes: - e7e3b42f76ccd4c6bdfc5330e8d4d6bb71f9ed2a - 8598868d1549887be6a73f18495cc750b0347e31 - abd3ad26e5757b67d0b79db28c01c6cfc4eadbf9 - 3cbb81cfafb0f7c2489b3d7c191d57b385d07059

October 2025 monthly summary for open-edge-platform/datumaro focusing on delivering business value through robust CI/CD modernization, feature-rich data converters, improved dataset handling, and enabling dynamic tile information updates. Highlights include CI/CD modernization replacing tox with uv, enhancements to experimental converter framework, standardized subset handling, and enabling TileInfo mutability.

September 2025 monthly summary (datapoints are across open-edge-platform/datumaro and open-edge-platform/geti). The team delivered significant features that improve data reliability, model evaluation readiness, and developer efficiency, while also enhancing video data handling and release processes.

August 2025 monthly summary: Delivered targeted robustness improvements and feature extensions across open-edge-platform/geti and open-edge-platform/datumaro, focusing on reliability, data interoperability, and API ergonomics. Key deliveries include a guard and fallback for asynchronous media preprocessing to prevent using unavailable previews; expanded Datumaro Core with polygons/ellipses/rotated bounding boxes and image type conversions across numpy, PIL, and Torch; refactored the experimental converter and type registry with enhanced error handling and multi-label support; Polars LabelField optimizations for efficient to_polars/from_polars flows and multi-label handling; and a dataset API restructuring to accept category dictionaries with label_group, along with packaging cleanup. These changes reduce runtime exceptions, improve data compatibility, and lay groundwork for scalable feature work.

July 2025 monthly review: Delivered tangible business value through CI/CD stabilization, robust data integrity validation, dynamic observability improvements, and cross-repo platform stability enhancements. Key outcomes include faster and more reliable deployments, improved data validation and auditing, runtime-configurable logging, and unified AI visualization with clearer operator experiences. Demonstrated strengths in cross-functional collaboration, dependency management, and observability engineering.

June 2025 monthly summary for open-edge-platform/geti: Focused delivery on API enhancements, security hardening, testing improvements, and CI/CD reliability to drive business value and developer efficiency. Key features delivered include a new Job Filtering API that enables retrieval of jobs by a creation_time range; expanded hardware support for the OTX v2 trainer with XPU/GPU compatibility and expanded testing configurations; and systematic cleanup and security improvements across the API surface and logging. Major bugs fixed include deprecated endpoint removal, log sanitization to prevent injection, and AWS KMS-based flows removal. In addition, CI/CD reliability improvements and QA/testing enhancements substantially improved build stability and test coverage.

Month: 2025-05 Overview: In May, the Geti platform delivered notable improvements in build reliability, end-to-end test coverage, GPU training security posture, and CI efficiency. These efforts reduce release risk, accelerate validation, and support scalable, secure model workflows across internal libraries and services. Key features delivered: - Dependency locking and pre-commit reliability: Introduced uv-based dependency locking for internal libraries, fixed pre-commit hook configuration, added uv.lock files for grpc_interfaces and interactive_ai/data_migration, and aligned libs/media_utils with the new locking mechanism, improving reproducible builds and developer experience. (Commit: 37be47c30ecd053c80a68e40a8b224d583bab672) - Geti Platform End-to-End Testing (BDD) Suite and CI Workflow: Implemented a comprehensive E2E/BDD suite (covering media annotation, dataset import/export, project management, model training, optimization, predictions) and introduced a GitHub Actions workflow for BDD checks; integrated static code analysis into the e2e Makefile for release-aligned validation. (Commits: 9970916045786ab16fdf3eab39dab84490d08dd4; c2d79d3ebdcc7ade53bff487bd96e32fd89d8362) - Intel GPU Training Security Context: Added capability to pass security context for Intel GPU-based training jobs, configured pod security context and render_gid in the trainer image to improve security and correct execution. (Commit: 594685458ced4eb36e11f25f756b84ac69854986) - Dockerfile Dependency Version Wildcards: Updated Dockerfiles across services to use wildcard versions for libgl and libglib2.0, boosting build stability and patch-version flexibility. (Commit: cd15fe138c396ab79746f815ff5ce8efbbd79256) Major bugs fixed: - Pre-commit CUDA-less Systems Fix: Fixed pre-commit failures on systems without CUDA bindings by conditionally skipping cuda-bindings installation during virtualenv creation, ensuring pre-commit succeeds across environments. (Commit: 9464a0e36f5bd885f7e68014fb0f4cfdbf8c73b1) - Proxy Configuration Revert: Reverted a change that added proxy awareness to build scripts; removed explicit proxy build args and env vars to restore previous build environment behavior. (Commit: 9282f21ea30c3d9a5bb9888493346dd6530ed476) Overall impact and accomplishments: - Build reliability and determinism: Dependency locking and pre-commit hardening reduced environment-related failures and enabled reproducible local and CI builds, accelerating onboarding and reducing time-to-ship. - Quality and confidence in releases: The E2E/BDD suite with CI workflow provides end-to-end validation and static analysis, enabling safer releases and faster feedback to teams. - Secure and scalable GPU workflows: Security context for Intel GPU training reduces risk and ensures correct execution in GPU-backed workloads. - Build stability and consistency: Dockerfile wildcard versions and environment fixes contribute to more stable and predictable container builds across services. Technologies and skills demonstrated: - Dependency management and pre-commit tooling (uv, pre-commit hooks) - End-to-end testing, BDD, and CI via GitHub Actions - Static analysis integration into release validation pipelines - Kubernetes security contexts and container image hardening for GPU workloads - Dockerfile best practices and build stability improvements

April 2025 monthly summary for open-edge-platform/geti: Delivered a reliability improvement for video handling in the dataset pipeline. Fixed missing videos during dataset import by ensuring ffmpeg is installed with the correct version in the Dockerfile and eliminated an unnecessary video_root initialization in the export task. These changes reduce import/export failures, improve data integrity, and stabilize end-to-end video workflows across the platform.