intel平臺優化

TensorFlow *是深度學習領域中主要使用的機器學習框架，要求高效利用計算資源。 為了充分利用英特爾架構和提高性能，TensorFlow *庫已經使用英特爾MKL-DNN原語進行了優化，該原語是深度學習應用的流行性能庫。

已進行優化的平臺

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有三種安裝方式。

1. 使用pip??

?pip install -i https://pypi.anaconda.org/intel/simple tensorflow

2. anaconda 安裝

video

3.? 自己編譯

前兩種方式可能不支持最新的指令集。

首先安裝?dnf?Bazel?

安裝 Bazel

pushd /var/tmpURL=https://github.com/bazelbuild/bazel/releases/latest
LASTURL=$(curl $URL -s -L -I -o /dev/null -w '%{url_effective}')
BZ_VERSION=${LASTURL##*/}
wget https://github.com/bazelbuild/bazel/releases/download/$BZ_VERSION/bazel-$BZ_VERSION-installer-linux-x86_64.shchmod +x bazel-*
./bazel-*
export PATH=/usr/local/bin:$PATHpopd

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centos 7.4 can not install `dnf`from epel

WARNING: EPEL 7 DNF is very old and has issues to include security flaws. This appears to be the reason it was removed. That said here is the work around to get it working on Centos 7.

cat  >  /etc/yum.repos.d/dnf-stack-el7.repo << EOF
[dnf-stack-el7]
name=Copr repo for dnf-stack-el7 owned by @rpm-software-management
baseurl=https://copr-be.cloud.fedoraproject.org/results/@rpm-software-management/dnf-stack-el7/epel-7-\$basearch/
skip_if_unavailable=True
gpgcheck=1
gpgkey=https://copr-be.cloud.fedoraproject.org/results/@rpm-software-management/dnf-stack-el7/pubkey.gpg
enabled=1
enabled_metadata=1
EOFyum install dnf

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?centos 7會出現這個bug:

dnf copr plugin not present in dnf-plugins-core

因為EPEL 7 DNF 已經被移除了centos 7 install dn，還需要：

wget http://springdale.math.ias.edu/data/puias/unsupported/7/x86_64/dnf-plugins-core-0.1.5-3.sdl7.noarch.rpm

dnf install copr-cli

sudo dnf update

dnf copr enable vbatts/bazel

centos?可以直接安裝bazel下：

wget https://copr.fedorainfracloud.org/coprs/vbatts/bazel/repo/epel-7/vbatts-bazel-epel-7.repo -P /etc/yum.repos.d/
yum install dnf-plugins-core-0.1.5-3.sdl7.noarch.rpm 
yum install bazel 

?install tf:

git clone https://github.com/tensorflow/tensorflow tensorflow
cd tensorflow

?Compiling TensorFlow with Intel C Compiler

CC=icc bazel build --verbose_failures --config=mkl --copt=-msse4.2 --copt="-DEIGEN_USE_VML" -c opt //tensorflow/tools/pip_package:build_pip_package

bazel build --config=mkl -c opt --copt=-mavx --copt=-mavx2 --copt=-mfma --copt=-mavx512f --copt=-mavx512dq --copt=-mavx512cd --copt=-mavx512bw --copt=-mavx512vl --copt="-DEIGEN_USE_VML" //tensorflow/tools/pip_package:build_pip_package

?Build and Install TensorFlow* on Intel? Architecture

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build tensorflow container：

more @ github

ref?build-dev-container.sh ?@github tensorflow docker

# source?tf-docker.evn

# cat?tf-docker.evn

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# The script set the following environment variables for tf docker:
export TF_DOCKER_BUILD_TYPE=mkl
# export TF_DOCKER_BUILD_TYPE=CPU
# CPU or GPU imageexport TF_DOCKER_BUILD_IS_DEVEL=YES
# Is this developer imageexport TF_DOCKER_BUILD_DEVEL_BRANCH=r1.99
# export TF_DOCKER_BUILD_DEVEL_BRANCH=master
#  (Required if TF_DOCKER_BUILD_IS_DEVEL is YES)
#  Specifies the branch to checkout for devel docker images# export TF_DOCKER_BUILD_CENTRAL_PIP
#   (Optional)
#   If set to a non-empty string, will use it as the URL from which the
#   pip wheel file will be downloaded (instead of building the pip locally).# export TF_DOCKER_BUILD_CENTRAL_PIP_IS_LOCAL
#   (Optional)
#   If set to a non-empty string, we will treat TF_DOCKER_BUILD_CENTRAL_PIP
#   as a path rather than a url.export TF_DOCKER_BUILD_IMAGE_NAME=native-mkl-tf
#  (Optional)
#  If set to any non-empty value, will use it as the image of the
#  newly-built image. If not set, the tag prefix tensorflow/tensorflow
#  will be used.# export TF_DOCKER_BUILD_VERSION:
#   (Optinal)
#   If set to any non-empty value, will use the version (e.g., 0.8.0) as the
#   tag prefix of the image. Additional strings, e.g., "-devel-gpu", will be
#   appended to the tag. If not set, the default tag prefix "latest" will be
#   used.# export TF_DOCKER_BUILD_PORT
#   (Optional)
#   If set to any non-empty and valid port number, will use that port number
#   during basic checks on the newly-built docker image.# export TF_DOCKER_BUILD_PUSH_CMD
#   (Optional)
#   If set to a valid binary/script path, will call the script with the final
#   tagged image name with an argument, to push the image to a central repo
#   such as gcr.io or Docker Hub.# export TF_DOCKER_BUILD_PUSH_WITH_CREDENTIALS
#   (Optional)
#   Do not set this along with TF_DOCKER_BUILD_PUSH_CMD. We will push with the
#   direct commands as opposed to a script.# export TF_DOCKER_USERNAME
#   (Optional)
#   Dockerhub username for pushing a package.# export TF_DOCKER_EMAIL
#   (Optional)
#   Dockerhub email for pushing a package.# export TF_DOCKER_PASSWORD
#   (Optional)
#   Dockerhub password for pushing a package.# export TF_DOCKER_BUILD_PYTHON_VERSION
#   (Optional)
#   Specifies the desired Python version. Defaults to PYTHON2.# export TF_DOCKER_BUILD_OPTIONS
#   (Optional)
#   Specifies the desired build options. Defaults to OPT.

View Code

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參考：

MPI教程

tensorflow MPI

build tensorflow

build

install 中文版

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pip install mock

REF：

學習課程

more info

conda install for TensorFlow and Intel Distribution for Python upgrade from 2017 to 2018

DNF (Dandified Yum)

?Intel? Computer Vision(CV) SDK?

Intel's Deep Learning Inference Engine Developer Guide

inference-engine-devguide-introduction

Configuring Model Optimizer for TensorFlow* Prerequisites

Configuring Caffe*

Converting Your TensorFlow* Model

Configuring Model Optimizer for TensorFlow* Prerequisites

What is Intel? DAAL?

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應用相關的論文

Pedestrian Detection Using TensorFlow* on Intel? Architecture

tensorflow 監測交通燈

CIFAR-10 分類-tensorflow

構建安裝TensorFlow* Serving on Intel? Architecture

Train and Use a TensorFlow* Model on Intel? Architecture

Using the Model Optimizer to Convert TensorFlow* Models

視頻?Performance Optimization of Deep Learning Frameworks Caffe* and TensorFlow* for the Intel? Xeon Phi? Product Family

單節點完整使用教程

轉載于:https://www.cnblogs.com/shaohef/p/8968283.html

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