oneAPI Deep Neural Network Library (oneDNN)

oneAPI Deep Neural Network Library (oneDNN) is an open-source performance library for deep learning applications. The library includes basic building blocks for neural networks optimized for Intel Architecture Processors and Intel Processor Graphics. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs.

You can find library source code and code used by these samples at oneDNN GitHub repository.

See the Intel® oneAPI Deep Neural Network Library product page for details on getting oneDNN.

oneDNN Samples

Type	Name	Description
Component	getting_started	A C++ sample demonstrating basics of oneDNN programming model.
Component	tutorials	Hands-on Jupyter notebook tutorials among different topics.

Using Samples in Intel® DevCloud for oneAPI

You can use oneDNN samples in Intel® DevCloud for oneAPI the environment in the following ways:

• Log in to a DevCloud system via SSH and
  • use git clone to get a full copy of samples repository, or
  • use the oneapi-cli tool to download specific sample.
• Launch a JupyterLab server and run Jupyter Notebooks from your web browser.

Using Visual Studio Code* (Optional)

You can use Visual Studio Code (VS Code) extensions to set your environment, create launch configurations, and browse and download samples.

The basic steps to build and run a sample using VS Code include:

• Download a sample using the extension Code Sample Browser for Intel Software Developer Tools.
• Configure the oneAPI environment with the extension Environment Configurator for Intel Software Developer Tools.
• Open a Terminal in VS Code (Terminal>New Terminal).
• Run the sample in the VS Code terminal using the instructions below.
• (Linux only) Debug your GPU application with GDB for Intel® oneAPI toolkits using the Generate Launch Configurations extension.

To learn more about the extensions, see the Using Visual Studio Code with Intel® oneAPI Toolkits User Guide.

License

Code samples are licensed under the MIT license. See License.txt for details.

Third party program Licenses can be found here: third-party-programs.txt.