Getting Started

Installation

Install from source:

pip install .

Or with Pixi:

pixi install
pixi run install

Or build with CMake (C++ only):

cmake -S . -B build -DONNX_LIGHT_CPU_BUILD_TESTS=ON \
      -DONNX_LIGHT_CPU_BUILD_PYTHON=OFF \
      -DONNX_LIGHT_CPU_WITH_ONNX_LIGHT=ON
cmake --build build

The C++ quick start requires the onnx-light C++ package to be installed so find_package(onnx_light) can locate it.

Quick Start

Register the optimized kernels, then execute the graph through onnx-light’s runtime. Runtime dispatch selects the registered onnx-light-cpu kernel and its best available SIMD path:

Python

from onnx_light_cpu import detect_simd_level, has_cpu_kernels, register_kernels

# Check what SIMD level is available:
print("CPU kernels available:", has_cpu_kernels())
print("SIMD level:", detect_simd_level())

# When onnx-light is installed, register the optimized kernels globally so
# ReferenceEvaluator uses them for supported ONNX operators.
register_kernels()

C++

#include <onnx_light_cpu/kernels/register_kernels.h>

#include <onnx_core/runtime/kernels/kernel_context.h>
#include <onnx_core/runtime/memory/simple_tensor.h>
#include <onnx_core/runtime/runtime_context.h>
#include <onnx_core/runtime/runtime_session.h>
#include <onnx_proto/onnx_helper.h>

int main() {
    namespace rt = ONNX_LIGHT_NAMESPACE::core::runtime;

    onnx_light_cpu::RegisterAllKernels();

    ONNX_LIGHT_NAMESPACE::GraphProto graph;
    graph.ref_node().push_back(
        ONNX_LIGHT_NAMESPACE::MakeNode("Abs", {"x"}, {"y"}));

    rt::RuntimeContext context(rt::KernelContext(rt::DefaultOpset(18)));
    context.Set(
        "x", rt::Tensor::FromFloat("x", {4}, {-1.0f, 2.0f, -3.0f, 4.0f}));
    rt::RuntimeSession session(context.GetExecutionPlan(graph));
    session.Run(context);

    const float *output = context.Get("y").AsFloat();
    // output = {1.0f, 2.0f, 3.0f, 4.0f}
}