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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, DNNL. 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.
  • Global/shared threadpools: By default each session creates its own set of threadpools. In situations where multiple sessions need to be created (to infer different models) in the same process, you end up with several threadpools created by each session. In order to address this inefficiency we introduce a new feature called global/shared threadpools. The basic idea here is to share a set of global threadpools across multiple sessions. Typical usage of this feature is as follows
    • Populate ThreadingOptions. Use the value of 0 for ORT to pick the defaults.
    • Create env using CreateEnvWithGlobalThreadPools()
    • Create session and call DisablePerSessionThreads() on the session options object
    • Call Run() as usual

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.

Deployment

Windows 10

Your installer should put the onnxruntime.dll into the same folder as your application. Your application can either use load-time dynamic linking or run-time dynamic linking to bind to the dll.

Dynamic Link Library Search Order

This is an important article on how Windows finds supporting dlls: Dynamic Link Library Search Order.

There are some cases where the app is not directly consuming the onnxruntime but instead calling into a DLL that is consuming the onnxruntime. People building these DLLs that consume the onnxruntime need to take care about folder structures. Do not modify the system %path% variable to add your folders. This can conflict with other software on the machine that is also using the onnxruntme. Instead place your DLL and the onnxruntime DLL in the same folder and use run-time dynamic linking to bind explicity to that copy. You can use code like this sample does in GetModulePath() to find out what folder your dll is loaded from.

Telemetry

To turn on/off telemetry collection on official Windows builds, please use Enable/DisableTelemetryEvents() in the C API. See the Privacy page for more information on telemetry collection and Microsoft's privacy policy.