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30 lines
1.7 KiB
Markdown
30 lines
1.7 KiB
Markdown
# C API
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## Features
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* Creating an InferenceSession from an on-disk model file and a set of SessionOptions.
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* Registering customized loggers.
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* Registering customized allocators.
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* 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.
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* 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.
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* Converting an in-memory ONNX Tensor encoded in protobuf format to a pointer that can be used as model input.
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* Setting the thread pool size for each session.
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* Setting graph optimization level for each session.
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* Dynamically loading custom ops. [Instructions](/docs/AddingCustomOp.md)
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## Usage Overview
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1. Include [onnxruntime_c_api.h](/include/onnxruntime/core/session/onnxruntime_c_api.h).
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2. Call OrtCreateEnv
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3. Create Session: OrtCreateSession(env, model_uri, nullptr,...)
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- Optionally add more execution providers (e.g. for CUDA use OrtSessionOptionsAppendExecutionProvider_CUDA)
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4. Create Tensor
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1) OrtCreateAllocatorInfo
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2) OrtCreateTensorWithDataAsOrtValue
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5. OrtRun
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## Sample code
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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.
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* [../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp](../csharp/test/Microsoft.ML.OnnxRuntime.EndToEndTests.Capi/C_Api_Sample.cpp)
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