This repository contains a collection of (mostly) simple standalone examples
using the onnxruntime_go library
to run neural-network applications.
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.
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.soBe aware that different examples may use different mechanisms for locating the correct shared library version.
-
sum_and_difference: This is the simplest example, copied from a unit test in theonnxruntime_golibrary. 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.