🧩 ORP: a Lightweight Framework for Building ONNX Runtime Pipelines with ORT

💬 Introduction

orp is a lightweight framework designed to simplify the creation and execution of ONNX Runtime Pipelines. Built on top of ort, it provides an simple way to handle data pre- and post-processing, chain multiple ONNX models together, while encouraging code reuse and clarity.

🔨 Sample Use-Cases

• gline-rs: an inference engine for GLiNER models
• more to come...

⚡️ GPU/NPU Inferences

The execution providers available in ort can be leveraged to perform considerably faster inferences on GPU/NPU hardware.

The first step is to pass the appropriate execution providers in RuntimeParameters. For example:

let rtp = RuntimeParameters::default().with_execution_providers([
    CUDAExecutionProvider::default().build()
]);

The second step is to activate the appropriate features (see related section below), otherwise ir may silently fall-back to CPU. For example:

$ cargo run --features=cuda ...

Please refer to doc/ORT.md for details about execution providers.

📦 Crate Features

This create mirrors the following ort features:

• To allow for dynamic loading of ONNX-runtime libraries: load-dynamic
• To allow for activation of execution providers: cuda, tensorrt, directml, coreml, rocm, openvino, onednn, xnnpack, qnn, cann, nnapi, tvm, acl, armnn, migraphx, vitis, and rknpu.

⚙️ Dependencies

• ort: the ONNX runtime wrapper
• composable: this crate is used to actually define the pre- and post-processing pipelines by composition or elementary steps, and can in turn be used to combine mutliple pipelines.