Example Applications for the onnxruntime_go Library

This repository contains a collection of (mostly) simple standalone examples using the onnxruntime_go library to run neural-network applications.

Prerequisites

You will need to be using a version of Go with cgo enabled---meaning that on Windows you'll need to have gcc available on your PATH.

If you wish to use hardware acceleration such as CUDA, you'll need to have a compatible version of the onnxruntime library compiled with support for your platform of choice. CoreML should almost always be available on Apple hardware, but other supported acceleration frameworks (e.g., TensorRT or CUDA) may have additional prerequisites, which are documented in the official onnxruntime documentation. Note that not all execution providers supported by onnxruntime itself are supported by onnxruntime_go.

The onnxruntime shared libraries for some common platforms are included under the third_party/ directory in this repository.

Usage

Navigate to any one of the subdirectories, and run go build to produce an executable on your system. Many executables will provide a mechanism for specifying a path to an onnxruntime shared library file. For example:

cd sum_and_difference
go build

# You can specify any version of the onnxruntime library here, but this would
# be the correct library version on 64-bit AMD or Intel Linux systems.
./sum_and_difference --onnxruntime_lib ../third_party/onnxruntime.so

Be aware that different examples may use different mechanisms for locating the correct shared library version.

List of Examples

• sum_and_difference: This is the simplest example, copied from a unit test in the onnxruntime_go library. It uses a basic neural network (trained using a pytorch script contained in the directory) on a tiny amount of hardcoded data. The source code is very heavily commented for reference.

• mnist: This example runs a CNN trained to identify handwritten digits from the MNIST dataset. It processes a single input image, and outputs the digit it is most likely to contain.

• mnist_float16: This example is identical to the plain mnist example, except it uses a 16-bit network, including 16-bit inputs and outputs. It is intended to illustrate how to use a float16 CustomDataTensor.

• onnx_list_inputs_and_outputs: This example prints the inputs and outputs of a user-specified .onnx file to stdout. It is intended to illustrate the usage of the onnxruntime_go.GetInputOutputInfo function.

• image_object_detect: This example uses the YOLOv8 network to detect a list of objects in an input image. It also attempts to use CoreML if the USE_COREML environment variable is set to true.