onnxruntime/docs/C_API.md

# 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](/docs/AddingCustomOp.md)

## Usage Overview

1. Include [onnxruntime_c_api.h](/include/onnxruntime/core/session/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) OrtCreateAllocatorInfo
   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](../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)