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Create projects, manage labels, and annotate media in the Geti application via its REST API. Use when a user wants to create a project with a task type and label set, add/edit/remove labels, upload images or videos, draw or set annotations (classification…
원문 언어: 영어
Develop and validate changes in `application/backend/` for the FastAPI `geti` service. Use when changing backend source or tests as well as packaging and configuration. This includes API routers and schemas; services and repositories; database code; UI-facing…
원문 언어: 영어
Update documentation (READMEs, application docs, or inline docstrings) to reflect changes, fixes, or new features. Use when a PR modifies behavior that should be documented for users or developers.
원문 언어: 영어
Import and export datasets in the Geti application via its REST API. Use when a user wants to bring an existing dataset into Geti (COCO/YOLO/VOC/Datumaro), migrate data from another tool, export a project's dataset or a dataset revision, remap labels or…
원문 언어: 영어
Regenerate and validate the OpenAPI contract between `application/backend/` and `application/ui/`. Use when backend endpoints, schemas, request or response models, or API surface change, or when `application/ui/src/api/openapi-spec.json` or…
원문 언어: 영어
Configure and validate the Geti runtime inference pipeline in application mode. Use when a user needs to set up or troubleshoot source → model → sink configuration, tune pipeline parameters, diagnose bad predictions or throughput issues, verify deployment…
원문 언어: 영어
Run and operate live inference in the Geti application pipeline. Use when a user wants to start, monitor, stop, or recover source -> model -> sink runtime execution, verify production readiness, or troubleshoot live pipeline behavior such as stalls, dropped…
원문 언어: 영어
Develop and validate changes in `application/ui/` for the React and TypeScript frontend. Use when touching `application/ui/src/**`, frontend tests, RSBuild or Vitest config, Playwright setup, package scripts, or generated API typings under `src/api`. Helps…
원문 언어: 영어
Use the Geti application end to end through its REST API — the project → dataset → annotate → train → deploy pipeline served by the FastAPI backend in `application/backend/`. Use when a user (not a contributor) wants to create a project, upload media, add…
원문 언어: 영어
Develop and validate changes in `library/` for the `getitune` Python package. Use when changing library source or tests as well as packaging, recipes and model manifests. This includes Python APIs and CLI behavior across training and export. Covers…
원문 언어: 영어
Discover which models, recipes, and tasks the getitune library (the Geti training library) supports before training. Use when a user asks what models are available, how to list recipes, how to filter by task or name pattern, how `list_models(...)` and…
원문 언어: 영어
Export a trained getitune model (the Geti training library) to a deployable format. Use when a user wants to run `engine.export(...)` or `getitune export`, choose between OpenVINO IR and ONNX, set FP32 vs FP16 precision with `ExportFormat` / `Precision`, or…
원문 언어: 영어
Optimize an exported getitune model (the Geti training library) with post-training quantization. Use when a user wants to run `OVEngine.optimize()` / `engine.optimize()` to produce an INT8 model via NNCF, understands calibration-set requirements, or needs to…
원문 언어: 영어
Prepare and point datasets at the getitune library (the Geti training library) for training, testing, and prediction. Use when a user asks which dataset formats are supported, how the `data=` argument of `create_engine(...)` / `--data_root` works, why format…
원문 언어: 영어
Run inference and evaluation with a getitune model (the Geti training library). Use when a user wants to call `engine.predict()` / `engine.test()` or `getitune predict` / `getitune test`, run inference with a PyTorch checkpoint versus an exported OpenVINO IR…
원문 언어: 영어
Train a computer-vision model with the getitune library (the Geti training library) using its Python API or CLI. Use when a user wants to train, fine-tune, or evaluate a model with `create_engine(...)` and `engine.train()/engine.test()`, run `getitune…
원문 언어: 영어
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