ONNX Runtime is an open-source library designed to accelerate machine learning model inference. It supports models in the ONNX (Open Neural Network Exchange) format, providing a high-performance engine that can run on various platforms and hardware accelerators. By leveraging optimizations like MKL-DNN, ONNX Runtime aims to deliver fast and efficient model execution.
When using ONNX Runtime, you might encounter the following error message:
ONNXRuntimeError: [ONNXRuntimeError] : 16 : FAIL : MKL-DNN error
This error indicates a failure during the execution of a model optimized with MKL-DNN, a library that enhances performance on Intel CPUs.
MKL-DNN, now known as oneDNN, is a performance library for deep learning applications. It provides highly optimized implementations of various neural network operations, specifically targeting Intel architectures.
This error typically arises when there is a misconfiguration or an issue with the MKL-DNN library during the execution of an ONNX model. It could be due to an incorrect installation, incompatible versions, or missing dependencies.
Ensure that MKL-DNN (oneDNN) is correctly installed on your system. You can check the installation by running:
pip show onednn
If it is not installed, you can install it using:
pip install onednn
Ensure you are using a compatible version of ONNX Runtime that supports MKL-DNN. You can check your current version with:
pip show onnxruntime
Consider upgrading to the latest version if necessary:
pip install --upgrade onnxruntime
Ensure that your environment variables are correctly set up to use MKL-DNN. You might need to set the MKL_THREADING_LAYER environment variable:
export MKL_THREADING_LAYER=GNU
This can be added to your shell configuration file (e.g., .bashrc or .zshrc) for persistence.
For more information on ONNX Runtime and MKL-DNN, consider visiting the following resources:
By following these steps, you should be able to resolve the MKL-DNN error and continue leveraging ONNX Runtime for efficient model inference.
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