getitune-exporting-a-model

Exporting a model with getitune

After training, export a model to a deployable format with engine.export(...) (Python API) or getitune export (CLI). getitune exports to OpenVINO IR (default) or ONNX, each at FP32 (default) or FP16 precision. Exported artifacts load back for inference via the OpenVINO/ONNX path (see the getitune-running-inference skill).

Run everything from library/.

Python API workflow

from getitune.engine import create_engine
from getitune.types import ExportFormat, Precision

engine = create_engine( model="efficientnet_b0", data="/path/to/dataset", work_dir="./my_workspace", ) engine.train(max_epochs=50)

FP32 OpenVINO IR (default) -> returns the .xml path

ov_ir_path = engine.export()

FP32 ONNX

onnx_path = engine.export(export_format=ExportFormat.ONNX)

FP16 ONNX (same pattern works for OpenVINO IR)

onnx_fp16 = engine.export(export_format=ExportFormat.ONNX, export_precision=Precision.FP16)

1. **Train or load a model** into the engine first (export operates on the
   engine's current model).
   - Done when: `engine.test()` produces sensible metrics before you export.
2. **Choose format and precision.** Default is FP32 OpenVINO IR. Use
   `export_format=ExportFormat.ONNX` for ONNX; `export_precision=Precision.FP16`
   to halve size for supported hardware. Both enums live in `getitune.types`.
   - Done when: `engine.export(...)` returns a path to the written artifact.
3. **Confirm the artifact exists** under `work_dir` (`.xml` + `.bin` for
   OpenVINO IR, `.onnx` for ONNX).
   - Done when: the returned path exists on disk.
4. **Validate parity** by loading the exported model back and running
   `engine.test()` — accuracy should closely match the trained model (small
   FP16 drift is expected). See `getitune-running-inference`.
   - Done when: exported-model metrics are within tolerance of the trained model.

## CLI workflow

```bash
# from library/
getitune export --data_root /path/to/dataset --model efficientnet_b0
# use --help -v for export-format / precision flags

Export/load contract

• Each model implements forward_for_tracing(...) under library/src/getitune/backend/lightning/models/<task>/; that is what defines the exported graph. If you change model I/O, keep this method in sync or export parity breaks.
• Exported OpenVINO IR / ONNX models are loaded for inference through the OpenVINO backend (OVEngine) using ModelAPI.
• Each task also ships an openvino_model.yaml recipe for loading a pre-exported IR model directly.

Verify

# from library/
just lint
just test-unit -- -k export        # when you touched export/tracing code

Related skills

• getitune-training-a-model — produce the checkpoint to export.
• getitune-running-inference — load and validate the exported model.
• getitune-optimizing-a-model — quantize an exported OpenVINO model to INT8.