This software was previously known as Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN) and Deep Neural Network Library (DNNL).
With the launch of oneAPI we changed the project name and repository location to be consistent with the rest of oneAPI libraries:
- Short library name changed to oneDNN.
- Repository moved from
intel/mkl-dnntooneapi-src/oneDNN. Existing links to the code and documentation will continue to work.There are no changes to the API, environment variables, or build options planned at this point.
oneAPI Deep Neural Network Library (oneDNN) is an open-source cross-platform performance library of basic building blocks for deep learning applications. The library is optimized for Intel Architecture Processors, Intel Processor Graphics and Xe architecture-based Graphics. Support for other architectures such as Arm* 64-bit Architecture (AArch64) and OpenPOWER* Power ISA (PPC64) is experimental. See the System Requirements section below.
oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. Deep learning practitioners should use one of the applications enabled with oneDNN:
- Apache* MXNet
- Apache* SINGA
- BigDL
- Caffe* Optimized for Intel Architecture
- Chainer*
- DeepLearning4J*
- Korali
- MATLAB* Deep Learning Toolbox
- Menoh*
- Microsoft* Cognitive Toolkit (CNTK)
- nGraph
- ONNX Runtime
- OpenVINO(TM) toolkit
- PaddlePaddle*
- PyTorch*
- Tensorflow*
- Developer guide explains programming model, supported functionality, and implementation details, and includes annotated examples.
- API reference provides a comprehensive reference of the library API.
Pre-built binaries for Linux*, Windows*, and macOS* are available for download in the releases section. Package names use the following convention:
| OS | Package name |
|---|---|
| Linux | dnnl_lnx_<version>_cpu_<cpu runtime>[_gpu_<gpu runtime>].tgz |
| Windows | dnnl_win_<version>_cpu_<cpu runtime>[_gpu_<gpu runtime>].zip |
| macOS | dnnl_mac_<version>_cpu_<cpu runtime>.tgz |
Several packages are available for each operating system to ensure interoperability with CPU or GPU runtime libraries used by the application.
| Configuration | Dependency |
|---|---|
cpu_iomp |
Intel OpenMP runtime |
cpu_gomp |
GNU* OpenMP runtime |
cpu_vcomp |
Microsoft Visual C OpenMP runtime |
cpu_tbb |
Threading Building Blocks (TBB) |
The packages do not include library dependencies and these need to be resolved in the application at build time. See the System Requirements section below and the Build Options section in the developer guide for more details on CPU and GPU runtimes.
If the configuration you need is not available, you can build the library from source.
oneDNN supports platforms based on the following architectures:
WARNING
Arm 64-bit Architecture (AArch64) and Power ISA (PPC64) support is experimental with limited testing validation.
The library is optimized for the following CPUs:
- Intel Atom processor with Intel SSE4.1 support
- 4th, 5th, 6th, 7th, and 8th generation Intel(R) Core(TM) processor
- Intel(R) Xeon(R) processor E3, E5, and E7 family (formerly Sandy Bridge, Ivy Bridge, Haswell, and Broadwell)
- Intel(R) Xeon Phi(TM) processor (formerly Knights Landing and Knights Mill)
- Intel Xeon Scalable processor (formerly Skylake, Cascade Lake, and Cooper Lake)
- future Intel Xeon Scalable processor (code name Sapphire Rapids)
On a CPU based on Intel 64 or on AMD64 architecture, oneDNN detects the instruction set architecture (ISA) at runtime and uses just-in-time (JIT) code generation to deploy the code optimized for the latest supported ISA. Future ISAs may have initial support in the library disabled by default and require the use of run-time controls to enable them. See CPU dispatcher control for more details.
WARNING
On macOS, applications that use oneDNN may need to request special entitlements if they use the hardened runtime. See the linking guide for more details.
The library is optimized for the following GPUs:
- Intel HD Graphics
- Intel UHD Graphics
- Intel Iris Plus Graphics
- Xe architecture-based Graphics (code named DG1 and Tiger Lake)
oneDNN supports systems meeting the following requirements:
- Operating system with Intel 64 / Arm 64 / Power architecture support
- C++ compiler with C++11 standard support
- CMake 2.8.11 or later
- Doxygen 1.8.5 or later to build the documentation
Configurations of CPU and GPU engines may introduce additional build time dependencies.
oneDNN CPU engine is used to execute primitives on Intel Architecture Processors, 64-bit Arm Architecture (AArch64) processors, 64-bit Power ISA (PPC64) processors, and compatible devices.
The CPU engine is built by default and cannot be disabled at build time. The engine can be configured to use the OpenMP or TBB threading runtime. The following additional requirements apply:
- OpenMP runtime requires C++ compiler with OpenMP 2.0 or later standard support
- TBB runtime requires Threading Building Blocks (TBB) 2017 or later.
Some implementations rely on OpenMP 4.0 SIMD extensions. For the best performance results on Intel Architecture Processors we recommend using the Intel C++ Compiler.
Intel Processor Graphics and Xe architecture-based Graphics are supported by the oneDNN GPU engine. The GPU engine is disabled in the default build configuration. The following additional requirements apply when GPU engine is enabled:
- OpenCL* runtime library (OpenCL version 1.2 or later)
- OpenCL driver (with kernel language support for OpenCL C 2.0 or later) with Intel subgroups extension support
When oneDNN is built from source, the library runtime dependencies and specific versions are defined by the build environment.
Common dependencies:
- GNU C Library (
libc.so) - GNU Standard C++ Library v3 (
libstd++.so) - Dynamic Linking Library (
libdl.so) - C Math Library (
libm.so) - POSIX Threads Library (
libpthread.so)
Runtime-specific dependencies:
| Runtime configuration | Compiler | Dependency |
|---|---|---|
DNNL_CPU_RUNTIME=OMP |
GCC | GNU OpenMP runtime (libgomp.so) |
DNNL_CPU_RUNTIME=OMP |
Intel C/C++ Compiler | Intel OpenMP runtime (libiomp5.so) |
DNNL_CPU_RUNTIME=OMP |
Clang | Intel OpenMP runtime (libiomp5.so) |
DNNL_CPU_RUNTIME=TBB |
any | TBB (libtbb.so) |
DNNL_GPU_RUNTIME=OCL |
any | OpenCL runtime (libOpenCL.so) |
Common dependencies:
- Microsoft Visual C++ Redistributable (
msvcrt.dll)
Runtime-specific dependencies:
| Runtime configuration | Compiler | Dependency |
|---|---|---|
DNNL_CPU_RUNTIME=OMP |
Microsoft Visual C++ Compiler | No additional requirements |
DNNL_CPU_RUNTIME=OMP |
Intel C/C++ Compiler | Intel OpenMP runtime (iomp5.dll) |
DNNL_CPU_RUNTIME=TBB |
any | TBB (tbb.dll) |
DNNL_GPU_RUNTIME=OCL |
any | OpenCL runtime (OpenCL.dll) |
Common dependencies:
- System C/C++ runtime (
libc++.dylib,libSystem.dylib)
Runtime-specific dependencies:
| Runtime configuration | Compiler | Dependency |
|---|---|---|
DNNL_CPU_RUNTIME=OMP |
Intel C/C++ Compiler | Intel OpenMP runtime (libiomp5.dylib) |
DNNL_CPU_RUNTIME=TBB |
any | TBB (libtbb.dylib) |
CPU engine was validated on RedHat* Enterprise Linux 7 with
- GNU Compiler Collection 4.8, 5.4, 6.1, 7.2, and 8.1
- Clang* 3.8.0
- Intel C/C++ Compiler 17.0, 18.0, and 19.0
on Windows Server* 2012 R2 with
- Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
- Intel C/C++ Compiler 17.0 and 19.0
on macOS 10.13 (High Sierra) with
- Apple LLVM version 9.2 (XCode 9.2)
- Intel C/C++ Compiler 18.0 and 19.0
GPU engine was validated on Ubuntu* 18.04 with
- GNU Compiler Collection 6.1 and 8.1
- Clang 3.8.1
- Intel C/C++ Compiler 19.0
- Intel SDK for OpenCL applications 2019 Update 3
- Intel Graphics Compute Runtime for OpenCL 19.37.14191
on Windows Server 2019 with
- Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
- Intel C/C++ Compiler 19.0
- Intel SDK for OpenCL applications 2019 Update 3
- Intel Graphics - Windows 10 DCH Drivers 26.20.100.6709
See README included into corresponding binary package.
Please submit your questions, feature requests, and bug reports on the GitHub issues page.
You may reach out to project maintainers privately at [email protected].
We welcome community contributions to oneDNN. If you have an idea on how to improve the library:
- For changes impacting the public API or library overall, such as adding new primitives or changes to the architecture, submit an RFC pull request.
- Ensure that the changes are consistent with the code contribution guidelines and coding style.
- Ensure that you can build the product and run all the examples with your patch.
- Submit a pull request.
For additional details, see contribution guidelines.
This project is intended to be a safe, welcoming space for collaboration, and contributors are expected to adhere to the Contributor Covenant code of conduct.
oneDNN is licensed under Apache License Version 2.0. Refer to the "LICENSE" file for the full license text and copyright notice.
This distribution includes third party software governed by separate license terms.
3-clause BSD license:
Apache License Version 2.0:
Boost Software License, Version 1.0:
MIT License:
SIL Open Font License (OFL):
This third party software, even if included with the distribution of the Intel software, may be governed by separate license terms, including without limitation, third party license terms, other Intel software license terms, and open source software license terms. These separate license terms govern your use of the third party programs as set forth in the "THIRD-PARTY-PROGRAMS" file.
Intel, the Intel logo, Intel Atom, Intel Core, Intel Xeon Phi, Iris, OpenVINO, the OpenVINO logo, Pentium, VTune, and Xeon are trademarks of Intel Corporation or its subsidiaries.
* Other names and brands may be claimed as the property of others.
Microsoft, Windows, and the Windows logo are trademarks, or registered trademarks of Microsoft Corporation in the United States and/or other countries.
OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos.
(C) Intel Corporation