Bindings to 'ONNX' Runtime

Provides native access to the 'Open Neural Network Exchange' (ONNX) Runtime < https://onnxruntime.ai/>, which is a performant engine for running machine learning models that are saved to a standardized format. Rather than interfacing with 'ONNX' via 'Python', as in the official 'onnx' package, 'onnxr' directly interfaces with the runtime's 'C++' API via 'cpp11'. Models saved to '.onnx' files can be loaded and run on various backends, including CPUs and Apple's 'CoreML' library.

onnxr

The onnxr package provides native R access to the Open Neural Network Exchange (ONNX) Runtime, which is a performant engine for running machine learning models that are saved to a standardized format. Rather than interfacing with ONNX via Python, as in the official onnx R package, this package directly interfaces with the runtime’s C++ API via cpp11. This uses far less memory and does not require a Python and TensorFlow installation.

Models saved to .onnx files can be loaded and run on various backends, including CPUs and Apple’s CoreML library.

Installation

You can install onnxr from CRAN with:

install.packages("onnxr")

Or install the development version from GitHub with:

# install.packages("pak")
pak::pak("CoryMcCartan/onnxr")

Getting started

On first load of the package, you will need to call onnx_install() if libonnxruntime is not already installed on your system. This will download and install the latest version of the ONNX Runtime library, which is roughly 35 MB.

onnxr::onnx_install()

The package comes with an example .onnx GLM model for predicting versicolor vs virginica in the iris data.

library(onnxr)

model_path <- system.file("extdata", "glm_iris.onnx", package = "onnxr")
model <- onnx_model(model_path)

By default, the model will run on the CPU. Other backends are available with the backend argument to onnx_model(). For example, setting onnx_model(model_path, backend = "coreml") would load the model to run via Apple’s CoreML library on Apple Silicon devices.

Printing the model object shows information on the input and output arrays.

model
#> onnxr model
#>   model:   /private/var/folders/64/lv8c__115kj6hxqc1f9sq5zr0000gn/T/Rtmpacscxg/temp_libpath8f8e2fc81c86/onnxr/extdata/glm_iris.onnx 
#>   backend: cpu  threads: 1 
#>   input:  X [?, 4] <float>
#>   output: label [?] <int64>
#>   output: probabilities [?, 2] <float>

The model can be called by using onnx_run(), which accepts named or positional arguments matching the model’s inputs.

X = model.matrix(Species ~ 0 + ., data = iris)
output = onnx_run(model, X = X)
str(output)
#> List of 2
#>  $ label        : num [1:150(1d)] 0 0 0 0 0 0 0 0 0 0 ...
#>  $ probabilities: num [1:150, 1:2] 1 1 1 1 1 1 1 1 1 1 ...

See the vignettes for a more involved end-to-end example.

Prior Art

This package is based off of the nativeORT package by Caleb Carr.