MONAI Bundle Inference Operator

Authors: Holoscan SDK Team (NVIDIA)
Supported platforms: x86_64, aarch64
Language: Python
Last modified: August 5, 2025
Latest version: 1.1.0
Minimum Holoscan SDK version: 1.0.3
Tested Holoscan SDK versions: 2.2.0, 3.2.0
Contribution metric: Level 2 - Trusted

This operator performs inference using MONAI Bundles for medical imaging tasks.

Overview

The MonaiBundleInferenceOperator loads a MONAI Bundle model and applies it to input medical images for inference, supporting flexible deployment in Holoscan pipelines.

Requirements

• Holoscan SDK Python package
• MONAI
• torch

Example Usage

from pathlib import Path
from holoscan.core import Fragment
from operators.medical_imaging.monai_bundle_inference_operator import MonaiBundleInferenceOperator
from operators.medical_imaging.core import AppContext, IOMapping, IOType, Image

fragment = Fragment()
app_context = AppContext({})  # Initialize with empty args dict

bundle_op = MonaiBundleInferenceOperator(
    fragment,
    name="monai_bundle",  # Optional operator name
    app_context=app_context,
    input_mapping=[
        IOMapping(
            label="image",
            data_type=Image,
            storage_type=IOType.IN_MEMORY
        )
    ],
    output_mapping=[
        IOMapping(
            label="pred",
            data_type=Image,
            storage_type=IOType.IN_MEMORY
        )
    ],
    model_name="model",  # Name of the model in the bundle
    bundle_path=Path("model/model.ts")  # Path to the MONAI bundle
)

API Reference

Python

MonaiBundleInferenceOperator

Inherits from: InferenceOperator

This inference operator automates the inference operation for a given MONAI Bundle.

Methods

Method	Description
__init__(fragment)	Create an instance of this class, associated with an Application/Fragment.
model_name()
model_name(name)
bundle_path()	The path of the MONAI Bundle model.
bundle_path(bundle_path)
parser()	The ConfigParser object.
parser(parser)
setup(spec)
compute(op_input, op_output, context)	Infers with the input(s) and saves the prediction result(s) to output
predict(data)	Predicts output using the inferer.
pre_process(data)	Processes the input dictionary with the stored transform sequence self._preproc.
post_process(data)	Processes the output list/dictionary with the stored transform sequence self._postproc.