onnxruntime gpu c++

ONNX Runtime是一个用于高性能推理的机器学习推理引擎，支持在GPU上加速推理任务。通过使用ONNX Runtime GPU C API，开发者可以直接在C程序中使用ONNX Runtime进行在GPU上进行推理。

ONNX Runtime GPU C API提供了一系列函数和数据结构，开发者可以用它来加载已经训练好的模型，并将输入数据传入模型进行推理。在GPU上进行推理可以显著提高推理的速度和效率，特别是对于模型和数据较大的情况。

为了使用ONNX Runtime GPU C API，开发者需要将其包含在自己的C程序中，并按照指定的接口进行调用。开发者需要连接到GPU并创建一个ONNX Runtime的推理环境，然后加载模型，并为输入数据分配内存并传入模型进行推理。最后，开发者可以获取输出的预测结果。

通过使用ONNX Runtime GPU C API，开发者可以有效地利用GPU的并行计算能力来加速推理任务。这对于需要处理大规模数据或复杂模型的机器学习应用来说是非常有益的。同时，ONNX Runtime还支持跨多个GPU的分布式推理，可以进一步提高推理的性能和吞吐量。

总之，ONNX Runtime GPU C API提供了一个高性能的机器学习推理引擎，支持在GPU上加速推理任务。开发者可以使用它来加速推理过程，提高效率，并应用在各种机器学习应用中。

相关问题

onnxruntime gpu推理 c++

### ONNX Runtime GPU Inference Using C++

To perform GPU inference with ONNX Runtime using C++, several components and configurations are necessary to ensure that the environment is set up correctly, including CUDA support and proper linking of libraries.

#### Environment Setup

For setting up an environment capable of performing GPU-based inference:

- Ensure installation of NVIDIA drivers compatible with your hardware.
- Install CUDA Toolkit version matching the requirements of ONNX Runtime. For instance, when working under specific versions like CUDA 10.2 as mentioned previously[^3].
- Installation or update of cuDNN library which works alongside CUDA for optimized deep learning operations.
- Building ONNX Runtime from source might be required to enable GPU providers such as TensorRT or CUDA depending on needs. This can involve configuring build options specifically enabling these features during compilation[^1].

#### Code Example for Performing GPU Inference

Below demonstrates a simplified example illustrating how one could initialize ONNX Runtime session configured for GPU execution in C++. Note this assumes all prerequisites have been met regarding software installations and environmental variables properly set pointing towards installed binaries/libraries paths.

#include "onnxruntime_cxx_api.h"
#include <iostream>
#include <vector>

int main() {
    Ort::Env env(ORT_LOGGING_LEVEL_WARNING, "test");
    
    // Specify use of CUDA Execution Provider
    std::vector<const char*> providers{"CUDAExecutionProvider", "CPUExecutionProvider"};
    Ort::SessionOptions so;
    so.SetIntraOpNumThreads(1);
    so.AddExecutableModelPath("path_to_your_model.onnx");

    Ort::Session session(env, "path_to_your_model.onnx", so);

    // Set preferred providers (GPU first then fallback CPU)
    session.DisableFallback();
    session.SetProviders(providers);

    // Prepare input data...
    // Perform actual inference here...

    return 0;
}

This code snippet initializes an `Ort::Env` object representing the global state shared across multiple sessions within the same process. The key part lies in specifying `"CUDAExecutionProvider"` before `"CPUExecutionProvider"` ensuring attempts at utilizing available GPUs over CPUs whenever possible. Additionally disabling fallback prevents automatic switching back to less efficient alternatives should initialization fail unexpectedly.

#### Linking Libraries During Compilation

When compiling applications intended to leverage ONNX Runtime's capabilities along with OpenCV functionalities, it becomes crucial to link against both sets of libraries accurately. Within project settings concerning linker inputs, adding dependencies explicitly includes but not limited to `opencv_world480.lib`, besides those provided by ONNX Runtime itself[^2].

--related questions--

1. What considerations must be taken into account while choosing between different execution providers offered by ONNX Runtime?
2. How does one troubleshoot common issues encountered during setup involving CUDA compatibility checks?
3. Can you provide guidance on optimizing performance parameters related to threading inside ORT Session Options?
4. Are there any notable differences observed running models originally trained elsewhere through ONNX format conversion processes?

c++ onnxruntime gpu推理

ONNXRuntime是一个开源的高性能深度学习模型运行库，它支持多种深度学习框架的模型转换，并能够在CPU、GPU以及各种硬件上运行。如果你想在C++中利用GPU资源进行ONNX模型的推理，ONNXRuntime提供了相应的API。

首先，你需要安装包含GPU支持的ONNXRuntime版本，这通常涉及到构建过程时选择GPU构建选项。然后，你可以通过`ORT::SessionOptions`设置GPU相关的属性，例如指定设备类型(`ORT::ExecutionProvider::CUDA`)，并创建一个支持GPU的会话。

下面是一个简单的示例：

#include "onnxruntime/capi/ort_inference.h"

// 设置GPU模式
 OrtSessionOptions* options = ortCreateSessionOptions();
 ORT_SET_SESSION_LOGSeverity(options, ORT_LOG_INFO); // 更改日志级别
 options->SetExecutionProviderPreference(ORT_EXECUTION_PROVIDER_CUDA, 1);

// 加载ONNX模型
std::string model_path = "path_to_your_onnx_model.onnx";
std::unique_ptr<OrtSession> session(ortCreateSession(model_path.c_str(), options));

// 执行推理
std::vector<float> input_data; // 输入数据准备
std::vector<float> output_buffer;
std::vector<OrtValue> inputs{{OrtValue::CreateTensor<float>(input_data)}};
session->Run(inputs, {}, &output_buffer); // 第二个参数为空表示无输入名称

// 获取输出结果
for (const auto& tensor : output_buffer) {
    const float* data = tensor.Get<Tensor>().Data<float>();
    // 处理输出...
}

// 清理资源
ortReleaseSession(session.get());
ortReleaseSessionOptions(options);