f7fbaa4af00336eebc0c9c0f31ee4f72c158d653,tests/onnx/test_onnx_model_export.py,,test_model_log_evaluate_pyfunc_format,#Any#Any#Any#,351

Before Change

            if should_start_run:
                mlflow.start_run()
            artifact_path = "onnx_model"
            mlflow.onnx.log_model(onnx_model=onnx_model, artifact_path=artifact_path)
            model_uri = "runs:/{run_id}/{artifact_path}".format(
                run_id=mlflow.active_run().info.run_id, artifact_path=artifact_path
            )

After Change

def test_model_log_evaluate_pyfunc_format(onnx_model, data, predicted):
    x = data[0]

    with mlflow.start_run() as run:
        artifact_path = "onnx_model"
        mlflow.onnx.log_model(onnx_model=onnx_model, artifact_path=artifact_path)
        model_uri = "runs:/{run_id}/{artifact_path}".format(
            run_id=run.info.run_id, artifact_path=artifact_path
        )

        // Loading pyfunc model
        pyfunc_loaded = mlflow.pyfunc.load_pyfunc(model_uri=model_uri)
        assert np.allclose(
            pyfunc_loaded.predict(x).values.flatten(), predicted, rtol=1e-05, atol=1e-05
        )
        // test with a single numpy array
        np_ary = x.values

        // NB: Onnx wrapper returns a dictionary for non-dataframe inputs, we want to get the
        // numpy array belonging to the first (and only) model output.
        def get_ary_output(args):
            return next(iter(pyfunc_loaded.predict(args).values())).flatten()

        assert np.allclose(get_ary_output(np_ary), predicted, rtol=1e-05, atol=1e-05)
        // test with a dict with a single tensor
        assert np.allclose(get_ary_output({"input": np_ary}), predicted, rtol=1e-05, atol=1e-05)


@pytest.mark.large
def test_model_save_evaluate_pyfunc_format_multi_tensor(
    multi_tensor_onnx_model, data, multi_tensor_model_prediction
):

Instances