ONNX Runtime is a high-performance inference engine for machine learning models in the Open Neural Network Exchange (ONNX) format. It is designed to accelerate the deployment of machine learning models across various platforms and devices. By supporting multiple execution providers, ONNX Runtime allows developers to leverage hardware-specific optimizations to improve inference performance.
When using ONNX Runtime, you may encounter the following error message: ONNXRuntimeError: [ONNXRuntimeError] : 23 : FAIL : Execution provider error. This error indicates that there is an issue with the execution provider being used for model inference.
The error code 23 signifies a failure related to the execution provider. Execution providers in ONNX Runtime are responsible for executing the model on specific hardware, such as CPUs, GPUs, or specialized accelerators. A failure in this context usually means that the execution provider is either not installed correctly or not configured properly.
To resolve the execution provider error, follow these steps:
Ensure that the execution provider is installed on your system. For example, if you are using the CUDA execution provider for GPU acceleration, verify that the necessary CUDA toolkit and cuDNN libraries are installed.
nvcc --version
nvidia-smi
Review the configuration settings for the execution provider. Ensure that the environment variables and paths are correctly set. For CUDA, verify that the CUDA_HOME and LD_LIBRARY_PATH environment variables are configured properly.
Ensure that you are using a compatible version of ONNX Runtime with your execution provider. You can update ONNX Runtime using pip:
pip install --upgrade onnxruntime-gpu
Refer to the official ONNX Runtime documentation for detailed instructions on setting up and configuring execution providers. This resource provides comprehensive guidance on supported providers and their requirements.
By following these steps, you should be able to resolve the execution provider error in ONNX Runtime. Proper installation and configuration are crucial for leveraging the full potential of ONNX Runtime's performance optimizations. For further assistance, consider reaching out to the ONNX Runtime community on GitHub.
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