mkl-dnn

KO | EN

gitlite — search

#python #java #python3 #arduino #golang #machine-learning #rust #html #flask #javascript #seismology #nodejs

★ 11 Open GitHub ↗

Fork of oneAPI Deep Neural Network Library for Intel® Open Image Denoise

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

oneAPI Deep Neural Network Library (oneDNN)
===========================================

> 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](https://www.oneapi.com/) 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-dnn` to `oneapi-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. oneDNN has experimental support
for the following architectures:
* Arm\* 64-bit Architecture (AArch64)
* NVIDIA\* GPU
* OpenPOWER\* Power ISA (PPC64)
* IBMz\* (s390x)

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](#applications-enabled-with-onednn).

# Table of Contents

- [Documentation](#documentation)
- [Installation](#installation)
- [System Requirements](#system-requirements)
- [Applications Enabled with oneDNN](#applications-enabled-with-onednn)
- [Support](#support)
- [Contributing](#contributing)
- [License](#license)
- [Security](#security)
- [Trademark Information](#trademark-information)

# Documentation

* [Developer guide](https://oneapi-src.github.io/oneDNN) explains programming
  model, supported functionality, and implementation details, and
  includes annotated examples.
* [API reference](https://oneapi-src.github.io/oneDNN/modules.html) provides
  a comprehensive reference of the library API.

# Installation

Binary distribution of this software is available as
[Intel oneAPI Deep Neural Network Library](https://software.intel.com/en-us/oneapi/onednn)
in [Intel oneAPI]( https://software.intel.com/en-us/oneapi).

Pre-built binaries for Linux\*, Windows\*, and macOS\* are available
for download in the
[releases section](https://github.com/oneapi-src/oneDNN/releases). Package
names use the following convention:

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)
| `cpu_dpcpp_gpu_dpcpp` | [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler), TBB, OpenCL runtime, oneAPI Level Zero runtime

The packages do not include library dependencies and these need to be resolved
in the application at build time. See the
[System Requirements](#system-requirements) section below and the
[Build Options](https://oneapi-src.github.io/oneDNN/dev_guide_build_options.html)
section in the [developer guide](https://oneapi-src.github.io/oneDNN) for more
details on CPU and GPU runtimes.

If the configuration you need is not available, you can
[build the library from source](https://oneapi-src.github.io/oneDNN/dev_guide_build.html).

# System Requirements

oneDNN supports platforms based on the following architectures:
- [Intel 64 or AMD64](https://en.wikipedia.org/wiki/X86-64),
- [Arm 64-bit Architecture (AArch64)](https://developer.arm.com/architectures/cpu-architecture/a-profile).
- [OpenPOWER](https://openpowerfoundation.org/) / [IBM Power ISA](https://en.wikipedia.org/wiki/Power_ISA).
- [IBMz z/Architecture (s390x)](https://en.wikipedia.org/wiki/Z/Architecture).

> **WARNING**
>
> Arm 64-bit Architecture (AArch64), Power ISA (PPC64) and IBMz (s390x) 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](https://oneapi-src.github.io/oneDNN/dev_guide_cpu_dispatcher_control.html)
for more details.

On a CPU based on Arm AArch64 architecture, oneDNN can be built with Arm Compute Library
integration. Compute Library is an open-source library for machine learning applications
and provides AArch64 optimized implementations of core functions. This functionality currently
requires that Compute Library is downloaded and built separately, see
[Build from Source](https://oneapi-src.github.io/oneDNN/dev_guide_build.html). oneDNN is only
compatible with Compute Library versions 21.02 or later.

> **WARNING**
>
> On macOS, applications that use oneDNN may need to request special
> entitlements if they use the hardened runtime. See the
> [linking guide](https://oneapi-src.github.io/oneDNN/dev_guide_link.html)
> 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)

## Requirements for Building from Source

oneDNN supports systems meeting the following requirements:
* Operating system with Intel 64 / Arm 64 / Power / IBMz architecture support
* C++ compiler with C++11 standard support
* [CMake](https://cmake.org/download/) 2.8.11 or later
* [Doxygen](http://www.doxygen.nl/download.html#srcbin) 1.8.5 or later
  to build the documentation
* [Arm Compute Library](https://github.com/arm-software/ComputeLibrary)
  for builds using Compute Library on AArch64.

Configurations of CPU and GPU engines may introduce additional build time
dependencies.

### CPU Engine

oneDNN CPU engine is used to execute primitives on Intel Architecture
Processors, 64-bit Arm Architecture (AArch64) processors,
64-bit Power ISA (PPC64) processors, IBMz (s390x), 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, TBB or DPCPP 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)](https://www.threadingbuildingblocks.org/)
2017 or later.
* DPCPP runtime requires
  * [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler)
  Beta
  * [Threading Building Blocks (TBB)](https://www.threadingbuildingblocks.org/)

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.

### GPU Engine

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 requires
    * 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
* DPCPP runtime requires
    * [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler) Beta
    * OpenCL runtime library (OpenCL version 1.2 or later)
    * [oneAPI Level Zero](https://github.com/oneapi-src/level-zero)
* DPCPP runtime with NVIDIA GPU support requires
    * [oneAPI DPC++ Compiler](https://github.com/intel/llvm)
    * OpenCL runtime library (OpenCL version 1.2 or later)
    * NVIDIA CUDA\* driver
    * cuBLAS 10.1 or later
    * cuDNN 7.6 or later

> **WARNING**
>
> NVIDIA GPU support is experimental. General information, build instructions
> and implementation limitations is available in
> [NVIDIA backend readme](https://github.com/oneapi-src/oneDNN/blob/master/src/gpu/nvidia/README.md).

### Runtime Dependencies

When oneDNN is built from source, the library runtime dependencies
and specific versions are defined by the build environment.

#### Linux

Common dependencies:
* GNU C Library (`libc.so`)
* GNU Standard C++ Library v3 (`libstdc++.so`)
* Dynamic Linking Library (`libdl.so`)
* C Math Library (`libm.so`)
* POSIX Threads Library (`libpthread.so`)

Runtime-specific dependencies:

#### Windows

Common dependencies:
* Microsoft Visual C++ Redistributable (`msvcrt.dll`)

Runtime-specific dependencies:

#### macOS

Common dependencies:
* System C/C++ runtime (`libc++.dylib`, `libSystem.dylib`)

Runtime-specific dependencies:

### Validated Configurations

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](https://software.intel.com/content/www/us/en/develop/tools/parallel-studio-xe.html)
  17.0, 18.0, and 19.0
* [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler) Beta

on Windows Server\* 2012 R2 with
* Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
* [Intel C/C++ Compiler](https://software.intel.com/content/www/us/en/develop/tools/parallel-studio-xe.html)
  17.0 and 19.0
* [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler) Beta

on macOS 10.13 (High Sierra) with
* Apple LLVM version 9.2 (XCode 9.2)
* [Intel C/C++ Compiler](https://software.intel.com/content/www/us/en/develop/tools/parallel-studio-xe.html)
  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](https://software.intel.com/content/www/us/en/develop/tools/parallel-studio-xe.html)
  19.0
* [Intel SDK for OpenCL applications](https://software.intel.com/content/www/us/en/develop/tools/opencl-sdk.html)
  2019 Update 3
* [Intel Graphics Compute Runtime for OpenCL](https://github.com/intel/compute-runtime/releases)
  19.37.14191
* [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler)
  Beta

on Windows Server 2019 with
* Microsoft Visual C++ 14.0 (Visual Studio 2015 Update 3)
* [Intel C/C++ Compiler](https://software.intel.com/content/www/us/en/develop/tools/parallel-studio-xe.html)
  19.0
* [Intel SDK for OpenCL applications](https://software.intel.com/content/www/us/en/develop/tools/opencl-sdk.html) 2019 Update 3
* [Intel Graphics - Windows 10 DCH Drivers](https://downloadcenter.intel.com/download/28783/Intel-Graphics-Windows-10-DCH-Drivers) 26.20.100.6709
* [Intel oneAPI DPC++ Compiler](https://software.intel.com/en-us/oneapi/dpc-compiler) Beta

## Requirements for Pre-built Binaries

See the README included in the corresponding binary package.

# Applications Enabled with oneDNN

* [Apache\* MXNet](https://mxnet.apache.org)
* [Apache\* SINGA](https://singa.apache.org)
* [BigDL](https://github.com/intel-analytics/BigDL)
* [Caffe\* Optimized for Intel Architecture](https://github.com/intel/caffe)
* [Chainer\*](https://chainer.org)
* [DeepLearning4J\*](https://deeplearning4j.org)
* [Flashlight\*](https://github.com/facebookresearch/flashlight)
* [Korali](https://github.com/cselab/korali)
* [MATLAB\* Deep Learning Toolbox](https://www.mathworks.com/help/deeplearning/)
* [Menoh\*](https://github.com/pfnet-research/menoh)
* [Microsoft\* Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit)
* [nGraph](https://ngraph.ai)
* [ONNX Runtime](https://github.com/microsoft/onnxruntime)
* [OpenVINO(TM) toolkit](https://01.org/openvinotoolkit)
* [PaddlePaddle\*](http://www.paddlepaddle.org)
* [PyTorch\*](https://pytorch.org/)
* [Tensorflow\*](https://www.tensorflow.org)

# Support

Please submit your questions, feature requests, and bug reports on the
[GitHub issues](https://github.com/oneapi-src/oneDNN/issues) page.

You may reach out to project maintainers privately
at dnnl.maintainers@intel.com.

> **WARNING**
>
> This is pre-production software and functionality may change without prior
> notice.

# Contributing

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](https://github.com/oneapi-src/oneDNN/tree/rfcs).
* Ensure that the changes are consistent with the
  [code contribution guidelines](CONTRIBUTING.md#code_contribution_guidelines)
  and [coding style](CONTRIBUTING.md#coding_style).
* Ensure that you can build the product and run all the examples with your
  patch.
* Submit a [pull request](https://github.com/oneapi-src/oneDNN/pulls).

For additional details, see [contribution guidelines](CONTRIBUTING.md).

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.md) code of conduct.

# License

oneDNN is licensed under [Apache License Version 2.0](LICENSE). Refer to the
"[LICENSE](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:
* [Xbyak](https://github.com/herumi/xbyak)
* [gtest](https://github.com/google/googletest)
* [Instrumentation and Tracing Technology API (ITT API)](https://github.com/intel/IntelSEAPI/tree/master/ittnotify)
* [CMake](https://github.com/Kitware/CMake)

Apache License Version 2.0:
* [Font Roboto](https://fonts.google.com/specimen/Roboto)
* [MathJax](https://github.com/mathjax/MathJax)
* [Xbyak_aarch64](https://github.com/fujitsu/xbyak_aarch64)

Boost Software License, Version 1.0:
* [Boost C++ Libraries](https://www.boost.org/)

MIT License:
* [Intel Graphics Compute Runtime for oneAPI Level Zero and OpenCL Driver](https://github.com/intel/compute-runtime)
* [Intel Graphics Compiler](https://github.com/intel/intel-graphics-compiler)

SIL Open Font License (OFL):
* [Font Awesome](https://github.com/FortAwesome/Font-Awesome)
* [Font Lato](https://fonts.google.com/specimen/Lato)

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](THIRD-PARTY-PROGRAMS)" file.

# Security

See Intel's [Security Center](https://www.intel.com/content/www/us/en/security-center/default.html)
for information on how to report a potential security issue or vulnerability.

See also: [Security Policy](SECURITY.md)

# Trademark Information

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.

mkl-dnn

Explore Similar Repositories

Was this content helpful?

Recent Feedback

Download README