ONNXRunTime

ONNXRunTime provides inofficial julia bindings for onnxruntime. It exposes both a low level interface, that mirrors the official C-API, as well as an high level interface.

Contributions are welcome.

Usage

The high level API works as follows:

julia> import ONNXRunTime as ORT

julia> path = ORT.testdatapath("increment2x3.onnx"); # path to a toy model

julia> model = ORT.load_inference(path);

julia> input = Dict("input" => randn(Float32,2,3))
Dict{String, Matrix{Float32}} with 1 entry:
  "input" => [1.68127 1.18192 -0.474021; -1.13518 1.02199 2.75168]

julia> model(input)
Dict{String, Matrix{Float32}} with 1 entry:
  "output" => [2.68127 2.18192 0.525979; -0.135185 2.02199 3.75168]

For GPU usage simply do:

pkg> add CUDA

julia> import CUDA

julia> ORT.load_inference(path, execution_provider=:cuda)

The low level API mirrors the offical C-API. The above example looks like this:

using ONNXRunTime.CAPI
using ONNXRunTime: testdatapath

api = GetApi();
env = CreateEnv(api, name="myenv");
so = CreateSessionOptions(api);
path = testdatapath("increment2x3.onnx");
session = CreateSession(api, env, path, so);
mem = CreateCpuMemoryInfo(api);
input_array = randn(Float32, 2,3)
input_tensor = CreateTensorWithDataAsOrtValue(api, mem, vec(input_array), size(input_array));
run_options = CreateRunOptions(api);
input_names = ["input"];
output_names = ["output"];
inputs = [input_tensor];
outputs = Run(api, session, run_options, input_names, inputs, output_names);
output_tensor = only(outputs);
output_array = GetTensorMutableData(api, output_tensor);

Alternatives

• Use the onnxruntime python bindings via PyCall.jl.
• ONNX.jl
• ONNXNaiveNASflux.jl