C API

Features

• Creating an InferenceSession from an on-disk model file and a set of SessionOptions.
• Registering customized loggers.
• Registering customized allocators.
• Registering predefined providers and set the priority order. ONNXRuntime has a set of predefined execution providers, like CUDA, MKLDNN. User can register providers to their InferenceSession. The order of registration indicates the preference order as well.
• Running a model with inputs. These inputs must be in CPU memory, not GPU. If the model has multiple outputs, user can specify which outputs they want.
• Converting an in-memory ONNX Tensor encoded in protobuf format to a pointer that can be used as model input.
• Setting the thread pool size for each session.
• Setting graph optimization level for each session.
• Dynamically loading custom ops. Instructions
• Ability to load a model from a byte array. See OrtCreateSessionFromArray in onnxruntime_c_api.h.

Usage Overview

1. Include onnxruntime_c_api.h.
2. Call OrtCreateEnv
3. Create Session: OrtCreateSession(env, model_uri, nullptr,...)
   • Optionally add more execution providers (e.g. for CUDA use OrtSessionOptionsAppendExecutionProvider_CUDA)
4. Create Tensor
   1. OrtCreateMemoryInfo
   2. OrtCreateTensorWithDataAsOrtValue
5. OrtRun

Sample code

The example below shows a sample run using the SqueezeNet model from ONNX model zoo, including dynamically reading model inputs, outputs, shape and type information, as well as running a sample vector and fetching the resulting class probabilities for inspection.

• ../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp