Get Started with Intel® oneAPI Deep Neural Network Library
The Intel® oneAPI Deep Neural Network Library (oneDNN) is a 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.
The oneDNN library provides a SYCL* extensions API for CPUs and GPUs.
Before You Begin
- See Get Started with the Intel® oneAPI DPC++/C++ Compiler.
- Refer to the oneDNN Release Notes and System Requirements to make sure you have the necessary system and software components.
- To build examples, you will also need CMake* 2.8.1.1 or later.
Examples
Use the following sample projects to become familiar with the Intel® oneAPI Deep Neural Network Library:
| Sample Name | Description |
| getting_started | This C++ API example demonstrates the basics of the oneDNN programming model. |
| sycl_interop_buffer and sycl_interop_usm | This C++ API example demonstrates programming for Intel® Processor Graphics with SYCL extensions API in oneDNN. |
Building Examples with the Intel® oneAPI DPC++/C++ Compiler
To setup the oneAPI environment, see the following examples.
Linux
source ${ONEAPI_ROOT}/setvars.sh
# Copy examples to a working directory
cp -R ${DNNLROOT}/examples .
cd examples
# Compile
icpx -fsycl getting_started.cpp -o getting_started -ldnnl
# Run
./getting_started cpu
./getting_started gpuWindows
%ONEAPI_ROOT%\setvars.bat
# Copy examples to a working directory
xcopy /E "%DNNLROOT%\examples" examples
cd examples
# Compile
icx /EHa -fsycl getting_started.cpp dnnl.lib
# Run
getting_started.exe cpu
getting_started.exe gpu
NOTE:
You may also compile and link with the pkg-config tool.
Notices and Disclaimers
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Your costs and results may vary.
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