The oneAPI Deep Neural Network Library (oneDNN) is an innovative, open-source cross-platform toolkit designed to aid in the performance enhancement of deep learning applications. This handy guide will lead you through the installation, configuration, and optimization of oneDNN to supercharge your deep learning workflows.

Table of Contents

• Documentation
• Installation
• System Requirements
• Applications Enabled with oneDNN
• Support
• Troubleshooting

Documentation

The oneDNN documentation has two main resources:

• Developer Guide – Explains the programming model, functionality, and provides annotated examples.
• API Reference – Offers a comprehensive reference of the library API.

Installation

You can start using oneDNN by installing it through the binary distribution available in:

• Anaconda
• Intel oneAPI

Note that library dependencies must be resolved during application build time. Refer to the System Requirements for further information.

System Requirements

oneDNN supports various architectures, including:

• Intel 64 or AMD64
• Arm 64-bit Architecture (AArch64)
• OpenPOWER & IBM Power ISA
• IBMz zArchitecture (s390x)
• RISC-V 64-bit (RV64)

However, note that support for Power ISA, IBMz, and RISC-V is experimental with limited validation.

Applications Enabled with oneDNN

oneDNN can be integrated with several popular applications, including:

• Apache MXNet
• Apache SINGA
• DeepLearning4J
• Flashlight
• Korali
• MATLAB Deep Learning Toolbox
• ONNX Runtime
• OpenVINO toolkit
• PaddlePaddle
• PyTorch
• Tensorflow

Support

If you need assistance or want to report issues, utilize the GitHub issues page. Alternatively, you can reach out via the UXL Foundation Slack channel #onednn.

Troubleshooting

Encountering issues during installation or usage? Here are some common troubleshooting tips:

• Missing Dependencies: Ensure that all dependencies listed under System Requirements have been installed.
• Compilation Errors: Make sure your C++ compiler version is compatible with the oneDNN library.
• Architecture Compatibility: Verify that your system architecture is supported, especially if you are using experimental features.
• For environment-specific issues: Refer to the respective Installation instructions for your operating system.

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Conclusion

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.