getitune-running-inference

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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.

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Sourceopen-edge-platform/geti

Added on2026-08-20

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Running inference with getitune

getitune

runs inference through

engine.predict()

(per-item predictions) and

engine.test()

(metrics on the test subset). The same calls work whether the engine holds a PyTorch model or an exported OpenVINO/ONNX model — the backend is selected from what you pass to

model=

.

Run everything from

library/

.

PyTorch inference (trained model)

python

from getitune.engine import create_engine

engine = create_engine(
    model="efficientnet_b0",
    data="/path/to/dataset",
)
test_metrics = engine.test()      # metrics on the test subset
predictions = engine.predict()    # predictions on the test subset

OpenVINO / ONNX inference (exported model)

python

from getitune.engine import create_engine

# OpenVINO IR — pass the .xml
ov_engine = create_engine(model="/path/to/exported_model.xml", data="/path/to/dataset")
ov_engine.test()
ov_engine.predict()

# ONNX — pass the .onnx
onnx_engine = create_engine(model="/path/to/exported_model.onnx", data="/path/to/dataset")
onnx_engine.test()
onnx_engine.predict()

Passing an

.xml

or

.onnx

path builds an

OVEngine

, which loads the model via ModelAPI.

Workflow

Pick the model surface. Use a model name/checkpoint for PyTorch inference, or an exported
```
.xml
```
/
```
.onnx
```
for deployed inference.
- Done when:
```
create_engine(...)
```
  returns the expected engine type.
Point
data=
at a dataset with a test subset (see
```
getitune-preparing-datasets
```
).
- Done when:
```
engine.test()
```
  runs without a data/format error.
Run
test()
for metrics or
predict()
for per-item outputs.
- Done when: metrics are produced, or predictions are returned for each item.
Compare backends when validating an export. PyTorch vs OpenVINO/ONNX metrics should closely match (small numeric drift is expected).
- Done when: exported-model metrics are within tolerance of the PyTorch model.

CLI

bash

# from library/
getitune predict --data_root /path/to/dataset --model efficientnet_b0
getitune test    --data_root /path/to/dataset --model /path/to/exported_model.xml

Related skills

```
getitune-exporting-a-model
```
— produce the
```
.xml
```
/
```
.onnx
```
used here.
```
getitune-optimizing-a-model
```
— run inference with an INT8 quantized model.
```
getitune-preparing-datasets
```
— the
```
data=
```
half of inference.