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Found 4 Skills
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 understand where exported artifacts are written and how they load back for inference. Covers the export/load contract between training and OpenVINO/ONNX inference.
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 re-validate and run inference with a quantized model versus the original FP32/FP16 model. Covers OpenVINO NNCF post-training quantization and the accuracy/size trade-off.
Loads and validates policies exported from Physical AI Studio for Runtime deployment. Use when working on InferenceModel, InferenceModel.from_pretrained, manifest.json, adapter auto-detection (onnx, openvino), backend/device kwargs, Hugging Face Hub policy packages, or the Runtime side of the export/load contract that Studio produces with physicalai export.
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 (`.xml`) or ONNX (`.onnx`) model, or understand how `OVEngine` loads deployed models via ModelAPI. Covers PyTorch, OpenVINO, and ONNX inference backends.