ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 16 : FAIL : MKL-DNN error

What is ONNX Runtime ONNXRuntimeError: [ONNXRuntimeError] : 16 : FAIL : MKL-DNN error

Understanding ONNX Runtime

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.

Identifying the Symptom

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.

Exploring the Issue

What is MKL-DNN?

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.

Why Does This Error Occur?

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.

Steps to Resolve the MKL-DNN Error

Step 1: Verify MKL-DNN Installation

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

Step 2: Check ONNX Runtime Version

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

Step 3: Validate Environment Configuration

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.

Additional Resources

For more information on ONNX Runtime and MKL-DNN, consider visiting the following resources:

ONNX Runtime Official WebsiteoneDNN DocumentationONNX Runtime GitHub Repository

By following these steps, you should be able to resolve the MKL-DNN error and continue leveraging ONNX Runtime for efficient model inference.