Welcome to Jaehyek's Blog!

• HTML CSS list

  2016-12-03

  Jaehyek

  html

  html  css

  다음 사이트를 참조하자
  CSS와 HTML 연동 http://igotit.tistory.com/entry/css-cascading-style-sheets-%EC%9A%94%EC%A0%90%EC%A0%95%EB%A6%AC
  CSS: 선택자(Selector) 이해 http://www.nextree.co.kr/p8468/
  실용적인 HTML/CSS태그 정리 http://blog.naver.com/PostView.nhn?blogId=shimjy1&logNo=220383323521&redirect=Dlog&widgetTypeCall=true

  CSS 속성을 정리
  CSS 속성 요약 정리 http://durst.tistory.com/94
  CSS 속성 모음 1 http://ggachi.ncity.net/TIP/5619
  CSS 속성 모음 display inline과 block http://ggachi.ncity.net/TIP/5617 CSS 속성 모음 float http://ggachi.ncity.net/TIP/5617 CSS 속성 모음 margin http://ggachi.ncity.net/TIP/5611
  CSS 속성 모음 position http://ggachi.ncity.net/TIP/5608
  CSS 속성 모음 position http://ggachi.ncity.net/TIP/5603
  CSS(Cascading Style Sheets)속성정리 http://kangel77.egloos.com/v/6214214
  CSS 속성 float의 모든 것 http://techbug.tistory.com/181
  CSS 시각적 가이드 http://cssreference.io/

  Read All

  ---

• Algorithm (1)

  2016-11-01

  Jaehyek

  Algorithm

  Algorithm

  피보나치( Fibonacci ) 수열

  다음과 같은 수열을 피보나치 수열이다.

  0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597

  파이썬으로 한다면

  def fibonacci ( n ) : 
      a, b = 0 , 1 
      while a < n : 
          print (a, end=" ")
          a, b = b, a+b
          
  fibonacci (2000)
  
  

  Read All

  ---

• Install Faster R-CNN on TitanX

  2016-10-29

  Jaehyek

  Deep-Learning

  Deep-Learning  Faster-R-CNN  TitanX

  To install the faster R-CNN on TitanX
  refer to the URL https://github.com/smallcorgi/Faster-RCNN_TF

  and do the following

  pip install matplotlib.pyplot
  pip install scipy
  apt-get install python-tk 
  In tensorflow0.12, I just modified @tf.RegisterShape("RoiPool") to @ops.RegisterShape("RoiPool"), and it worked for me. Maybe you could try it.
  pip install pyyaml
  

  To run demo.

  First, download VGGnet_fast_rcnn_iter_70000.ckpt, and then

  ~/FNRCC/tools $) python /demo.py --model ./VGGnet_fast_rcnn_iter_70000.ckpt
  

  Read All

  ---

• Install SSD on Jetson TX1

  2016-10-28

  Jaehyek

  Deep-Learning

  Deep-Learning  SSD  TX1

  refer to the URL https://myurasov.github.io/2016/11/27/ssd-tx1.html

  Before install the above link, and install the following

  sudo apt-get install libatlas-base-dev 
  sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
  sudo apt-get install libopenblas-dev
  

  Modify the Makefile.config

  OPENCV_VERSION := 3
  CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
               -gencode arch=compute_20,code=sm_21 \
               -gencode arch=compute_30,code=sm_30 \
               -gencode arch=compute_35,code=sm_35 \
               -gencode arch=compute_50,code=sm_50 \
               -gencode arch=compute_52,code=sm_52 \
               -gencode arch=compute_52,code=sm_53 \ ( 53 추가 )
               -gencode arch=compute_61,code=sm_61
  
  PYTHON_LIB := /usr/lib/x86_64-linux-gnu ( intel CPU Desktop )
  PYTHON_LIB := /usr/lib/aarch64-linux-gnu ( Arm CPU Desktop )
  
  INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
  LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib
               
  

  Modify the Makefile

  and add lib to Makefile like this: in Makefile LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial boost_regex

  mount smbfs

  sudo mount -t cifs //172.21.26.47/titanx-home/TX1/share  /home/ubuntu/opencv/build2 -o username=ysc,password=lge123
  

  install opencv2 ( Arm CPU Desktop )

  sudo apt-get install build-essential cmake git pkg-config
  sudo apt-get install libjpeg8-dev libtiff4-dev libjasper-dev libpng12-dev
  sudo apt-get install libgtk2.0-dev
  sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
  sudo apt-get install libatlas-base-dev gfortran
  sudo apt-get install python2.7-dev
  
  wget https://bootstrap.pypa.io/get-pip.py --no-check-certificate
  sudo python get-pip.py 
  pip install numpy
  

  $ cd ~ 
  $ git clone https://github.com/daveselinger/opencv
  $ cd opencv 
  $ git checkout 3.1.0-with-cuda8
  

  $ cd ~ 
  $ git clone https://github.com/Itseez/opencv_contrib.git 
  $ cd opencv_contrib 
  $ git checkout 3.1.0
  

  만일 “Deep-Learning-install-OpenCV.md”을 이용해서 opencv을 compile시킬 때, 다음과 같은 error가 나온다.

  /home/epinux/dev/opencv_contrib/modules/tracking/include/opencv2/tracking/onlineMIL.hpp:57:23: error: expected unqualified-id before ‘>’ token
   #define  sign(s)  ((s > 0 ) ? 1 : ((s<0) ? -1 : 0))
  

  이를 수정하기 위해서는 다음을 참조한다. https://github.com/opencv/opencv_contrib/issues/618
  즉
  

  opencv_contrib/modules/tracking/include/opencv2/tracking/onlineMIL.hpp:
  #define  sign(s)  ((s > 0 ) ? 1 : ((s<0) ? -1 : 0))  ==> #define  sgn(s)  ((s > 0 ) ? 1 : ((s<0) ? -1 : 0))
  

  그리고 다음을 수정한다.
  

  opencv_contrib/modules/tracking/src/onlineMIL.cpp : 
  310: from a sign() call to a sgn() call
  339: from a sign() call to a sgn() call
  

  if you got a error like as below during compiling caffe-ssd on intel CPU Desktop

  /usr/include/boost/property_tree/detail/json_parser_read.hpp:257:264: error: ‘type name’ declared as function returning an array
  make: *** [.build_release/cuda/src/caffe/layers/detection_output_layer.o] Error 1
  

  To solve the above , update the gcc, g++ compiler version to above 5. refer to https://github.com/BVLC/caffe/issues/4957

  $ sudo add-apt-repository ppa:ubuntu-toolchain-r/test
  $ sudo apt-get update
  $ sudo apt-get install gcc-5 g++-5
  

  and then delete and create the gcc, g++ link

  $ sudo rm /usr/bin/gcc
  $ sudo rm /usr/bin/g++
  $ sudo cd /usr/bin
  $ sudo ln -s gcc-5 gcc
  $ sudo ln -s g++-5 g++
  

  if ld can’t find hdf5_serial_hl and hdf5_serial, use the old one in Makefile

  LIBRARIES += glog gflags protobuf boost_system boost_filesystem boost_regex m hdf5_hl hdf5 boost_regex
  # LIBRARIES += glog gflags protobuf boost_system boost_filesystem m hdf5_serial_hl hdf5_serial boost_regex
  

  Read All

  ---

• install OpenCV on Ubnutu 14.04

  2016-10-27

  Jaehyek

  Deep-Learning

  Deep-Learning  OpenCV

  refer to the URL https://github.com/DrewNF/Build-Deep-Learning-Env-with-Tensorflow-Python-OpenCV

  Preparing Linux Machine

  $ sudo apt-get update 
  $ sudo apt-get upgrade
  

  Now we need to install our developer tools:

  $ sudo apt-get install build-essential cmake git pkg-config
  

  OpenCV needs to be able to load various image file formats from disk, including JPEG, PNG, TIFF, etc. In order to load these image formats from disk, we’ll need our image I/O packages:

  $ sudo apt-get install libjpeg8-dev libtiff4-dev libjasper-dev libpng12-dev
  

  how do we display the actual image to our screen? The answer is the GTK development library, which the highgui module of OpenCV depends on to guild Graphical User Interfaces (GUIs):

  $ sudo apt-get install libgtk2.0-dev
  

  what about processing video streams and accessing individual frames? We’ve got that covered here:

  $ sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev
  

  Install libraries that are used to optimize various routines inside of OpenCV:

  $ sudo apt-get install libatlas-base-dev gfortran
  $ sudo apt-get install libhdf5-dev
  

  if a Python package manager is needed ? install pip :

  $ wget https://bootstrap.pypa.io/get-pip.py 
  $ sudo python get-pip.py
  

  Install a OpenCV

  $ cd ~ 
  $ git clone https://github.com/daveselinger/opencv
  $ cd opencv 
  $ git checkout 3.1.0-with-cuda8
  

  You can replace the 3.1.0 version with whatever the current release is. Be sure to check OpenCV.org for information on the latest release.

  We also need the opencv_contrib repo as well. Without this repository, we won’t have access to standard keypoint detectors and local invariant descriptors (such as SIFT, SURF, etc.) that were available in the OpenCV 2.4.X version. We’ll also be missing out on some of the newer OpenCV 3.0 features like text detection in natural images:

  $ cd ~ 
  $ git clone https://github.com/Itseez/opencv_contrib.git 
  $ cd opencv_contrib 
  $ git checkout 3.1.0
  

  make sure that you checkout the same version for opencv_contrib that you did for opencv above, otherwise you could run into compilation errors

  Time to setup the build:

  $ cd ~/opencv 
  $ mkdir build 
  $ cd build 
  $ cmake -D CMAKE_BUILD_TYPE=RELEASE \ -D CMAKE_INSTALL_PREFIX=/usr/local \ -D INSTALL_C_EXAMPLES=ON \ -D INSTALL_PYTHON_EXAMPLES=ON \ -D OPENCV_EXTRA_MODULES_PATH=~/opencv_contrib/modules \ -D BUILD_EXAMPLES=ON ..
  
  

  Here some very important options, read carefully :

  CMAKE_BUILD_TYPE : This option indicates that we are building a release binary of OpenCV.
  CMAKE_INSTALL_PREFIX : The base directory where OpenCV will be installed.
  PYTHON2_PACKAGES_PATH : The explicit path to where our site-packages  directory lives in our cv  virtual environment.
  PYTHON2_LIBRARY : Path to our Hombrew installation of Python.
  PYTHON2_INCLUDE_DIR : The path to our Python header files for compilation.
  INSTALL_C_EXAMPLES : Indicate that we want to install the C/C++ examples after compilation.
  INSTALL_PYTHON_EXAMPLES : Indicate that we want to install the Python examples after complication.
  BUILD_EXAMPLES : A flag that determines whether or not the included OpenCV examples will be compiled or not.
  OPENCV_EXTRA_MODULES_PATH : This option is extremely important — here we supply the path to the opencv_contrib repo 
                  that we pulled down earlier, indicating that OpenCV should compile the extra modules as well.
  

  The compiling options changes depending on the version of python and most depending on how we are installing all the libraries and the dependencies.
  In order to build OpenCV 3.1.0, you need to set -D INSTALL_C_EXAMPLES=OFF (rather than ON) in the cmake command. There is a bug in the OpenCV v3.1.0 CMake build script that can cause errors if you leave this switch on. Once you set this switch to off, CMake should run without a problem.

  Now we can finally compile OpenCV:

  $ make -j4
  

  Where you can replace the 4 with the number of available cores on your processor to speedup the compilation. OpenCV Linking: If we have installed OpenCV globally on your PC and you want to link it to the virtualenv, we need to export the two following paths:

  ** export PYTHONPATH="${PYTHONPATH}:/my/other/path"** (Example of path: /opt/amd64/opencv-3.1.0/lib/python2.7/dist-packages)
  ** export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/my/other/path"** (Example of path: /opt/amd64/opencv-3.1.0/lib)
  
  export PYTHONPATH="${PYTHONPATH}:/usr/local/lib/python2.7/dist-packages  :: .bashrc
  

  Assuming that OpenCV compiled without error, you can now install it on your Ubuntu system:

  $ sudo make install 
  $ sudo ldconfig
  

  If you’ve reached this step without an error, OpenCV should now be installed in ** /usr/local/lib/python2.7/site-packages**. However, our cv virtual environment is located in our home directory — thus to use OpenCV within our cv environment, we first need to sym-link OpenCV into the site-packages directory of the cv virtual environment:

  $ cd ~/.virtualenvs/cv/lib/python2.7/site-packages/ 
  $ ln -s /usr/local/lib/python2.7/site-packages/cv2.so cv2.so
  

  Due to install error, we should copy cv2.so

  $ sudo cp ~/opencv/build/lib/cv2.so  /usr/local/lib/python2.7/dist-packages/
  

  Your output should be:

  >>> import cv2
  >>> cv2.__version__ '3.0.0'
  

  Download cuDNN 6.5

  $ cd ~
  $ wget https://s3-eu-west-1.amazonaws.com/christopherbourez/public/cudnn-6.5-linux-x64-v2.tgz
  

  Add to your .bashrc

  export GLPATH=/usr/lib
  export __GL_PERFMON_MODE=1
  export TF_NEED_CUDA=1
  export LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/local/lib:$LD_LIBRARY_PATH
  export PATH=/usr/local/cuda/bin:$PATH
  export PYTHONPATH=/usr/local/lib/python2.7/dist-packages:$PYTHONPATH
  export CUDA_HOME=/usr/local/cuda
  

  Read All

  ---

• install Titan X on Ubuntu 14.04

  2016-10-26

  Jaehyek

  Deep-Learning

  Deep-Learning  summary  hunkim  github

  refer to the URL http://www.nvidia.com/object/gpu-accelerated-applications-tensorflow-installation.html

  만일 위의 URL이 잘 되지 않는다면 다음 URL을 활용한다. https://github.com/uher/InstallGpuEnableTensorflow

  Step 1. Install NVIDIA CUDA

  • download cuda_8.0.44_linux.run from https://developer.nvidia.com/cuda-downloads
  • download NVIDIA-Linux-x86_64-375.39.run from http://www.nvidia.com/download/driverResults.aspx/114708/en-us
  • run the following instructions. refer to http://askubuntu.com/questions/848221/installing-cuda-8-0-on-ubuntu-16-04-with-nvidia-geforce-845m

  1. sudo apt-get purge nvidia-*
  2. sudo service lightdm stop
  3. sudo ./NVIDIA-Linux-x86_64-367.57.run --no-opengl-files
     다음 step 4에서 설치할 driver을 미리 설치한다. 즉 다음 step에서는 driver을 설치하지 않아야 한다.
  4. sudo ./cuda_8.0.44_linux.run (you should not install its own nvidia-driver 367.48, as you have 367.57 already installed)
  5. in /usr/local/cuda-8.0/include/host_config.h, comment this line out:
     #error – unsupported GNU version! gcc versions later than 5 are not supported!
     
  6. insert the following in .bashrc
     export PATH=/usr/local/cuda-8.0/bin:.:$PATH
     export LD_LIBRARY_PATH=/usr/local/cuda-8.0/lib64:$LD_LIBRARY_PATH
     export GLPATH=/usr/lib
     
  7. reboot

  Step 2. Install NVIDIA cuDNN

  • download cudnn-8.0-linux-x64-v5.1.tgz from https://developer.nvidia.com/rdp/cudnn-download
    $ sudo tar -xvf cudnn-8.0-linux-x64-v5.1.tgz -C /usr/local
    

  Additionally

  $ sudo apt-get install git
  $ sudo apt-get install vim
  

  Step 3. Install and upgrade PIP

  $ sudo apt-get install python-pip python-dev
  $ pip install –upgrade pip
  $ sudo pip install numpy
  

  Step 4. Install Bazel

  To build TensorFlow from source, the Bazel build system must first be installed as follows
  

  $ sudo apt-get install software-properties-common swig
  $ sudo add-apt-repository ppa:webupd8team/java
  $ sudo apt-get update
  $ sudo apt-get install oracle-java8-installer
  $ echo “deb http://storage.googleapis.com/bazel-apt stable jdk1.8” | sudo tee /etc/apt/sources.list.d/bazel.list
  $ curl https://storage.googleapis.com/bazel-apt/doc/apt-key.pub.gpg | sudo apt-key add -
  $ sudo apt-get update
  

  이후 다음을 하는 것 보다
  

  $ sudo apt-get install bazel
  

  아래를 하는 것이 더 최신의 것 임.

  $ wget https://github.com/bazelbuild/bazel/archive/0.3.2.tar.gz
  $ tar -xvzf 0.3.2.tar.gz
  $ cd bazel-0.3.2/
  $ sudo ./compile.sh
  $ sudo cp output/bazel /usr/local/bin
  $ cd ..
  

  Step 5. Install TensorFlow

  To obtain the best performance with TensorFlow we recommend building it from source.
  First, clone the TensorFlow source code repository:
  

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

  다음을 하는 것 보다
  

  $ git reset –hard 70de76e
  

  아래를 하는 것이 더 안정적인 것 임.

  $ git reset –hard 287db3a
  

  그리고 configure을 하기 전에 다음을 실행한다.
  1.computecpp을 설치한다. 다음을 참조하고 https://www.codeplay.com/products/computesuite/computecpp
  tar을 풀어서 /usr/local 밑에 둔다.
  

  2.zlib_archive의 new version을 설치한다. 다음과 같이 수정한다. https://github.com/tensorflow/tensorflow/issues/6594

  edit tensorflow/workspace.bzl
  replace zlib-1.2.8 with zlib-1.2.11
  remove the sha256 line
  

  혹은

  diff –git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl
  index 06e16cd..7c7b44c 100644
  — a/tensorflow/workspace.bzl
  +++ b/tensorflow/workspace.bzl
  @@ -228,9 +228,8 @@ def tf_workspace(path_prefix = “”, tf_repo_name = “”):
  
  native.new_http_archive(
  name = “zlib_archive”,
  

  • url = “http://zlib.net/zlib-1.2.8.tar.gz”,
    
  • sha256 = “36658cb768a54c1d4dec43c3116c27ed893e88b02ecfcb44f2166f9c0b7f2a0d”,
    
  • strip_prefix = “zlib-1.2.8”,
    
  • url = “http://zlib.net/zlib-1.2.11.tar.gz”,
    
  • strip_prefix = “zlib-1.2.11”,
    build_file = str(Label(“//:zlib.BUILD”)),
    )
    

  configure script 실행에서 “Do you wish to build TensorFlow with GPU support?” 을 질문하지 않을 수 있으므로
  export TF_NEED_CUDA = 1 하고 ( .bashrc 에 반영할 것. )
  

  Then run the configure script as follows:

  $ cd tensorflow
  $ ./configure
  Please specify the location of python. [Default is /usr/bin/python]: [enter]
  Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] n
  No Google Cloud Platform support will be enabled for TensorFlow
  Do you wish to build TensorFlow with GPU support? [y/N] y
  GPU support will be enabled for TensorFlow
  Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]: [enter]
  Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 8.0
  Please specify the location where CUDA 8.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: [enter]
  Please specify the Cudnn version you want to use. [Leave empty to use system default]: 5
  Please specify the location where cuDNN 5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: [enter]
  Please specify a list of comma-separated Cuda compute capabilities you want to build with.
  You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
  Please note that each additional compute capability significantly increases your build time and binary size.
  [Default is: “3.5,5.2”]: 5.2,6.1 [see https://developer.nvidia.com/cuda-gpus]
  Setting up Cuda include
  Setting up Cuda lib64
  Setting up Cuda bin
  Setting up Cuda nvvm
  Setting up CUPTI include
  Setting up CUPTI lib64
  Configuration finished
  

  Then call bazel to build the TensorFlow pip package:
  만일 아래의 command가 안될 때는
  bazel clean
  을 실행하고 다시 실행할 것.
  

  bazel build -c opt –config=cuda //tensorflow/tools/pip_package:build_pip_package
  bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg
  

  And finally install the TensorFlow pip package

  sudo pip install –upgrade /tmp/tensorflow_pkg/tensorflow-0.12*
  

  Step 6. UPGRADE PROTOBUF

  텐서플로우 pip 패키지는 protobuf pip 패키지 버전 3.0.0b2를 필요로 합니다. PyPI에서 다운받을 수 있는(pip install protobuf를 사용해서) Protobuf의 pip 패키지는 파이썬 만으로 개발된 라이브러리로 C++ 구현보다 직렬화/역직렬화시 10~50배 느립니다. Protobuf는 빠른 프로토콜 파싱을 위한 C++ 바이너리 확장을 지원합니다. 이 확장은 표준 파이썬 PIP 패키지에는 포함되어 있지 않습니다. 우리는 이 바이너리 확장을 포함한 protobuf pip 패키지를 자체적으로 만들었습니다. 다음 명령을 사용해 자체적으로 만든 protobuf pip 패키지를 설치할 수 있습니다:

  Upgrade to the latest version of the protobuf package:

  Python 2.7:

  $ sudo pip install –upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0b2.post2-cp27-none-linux_x86_64.whl

  Python 3.4:
  

  $ sudo pip3 install –upgrade https://storage.googleapis.com/tensorflow/linux/cpu/protobuf-3.0.0b2.post2-cp34-none-linux_x86_64.whl

  cd ..

  pip install tensorflow 명령은 파이썬으로된 기본 pip 패키지를 설치하므로 위 패키지를 설치하려면 반드시 텐서플로우를 설치하고 난 후에 합니다. 위 pip 패키지는 이미 설치된 protobuf 패키지를 덮어 씁니다. 바이너리 pip 패키지는 64M 넘는 메세지에 대한 지원을 이미 하고 있어 아래와 같은 에러가 이미 해결 되었습니다:

  [libprotobuf ERROR google/protobuf/src/google/protobuf/io/coded_stream.cc:207] A
  protocol message was rejected because it was too big (more than 67108864 bytes).
  To increase the limit (or to disable these warnings), see
  CodedInputStream::SetTotalBytesLimit() in google/protobuf/io/coded_stream.h.
  

  Step 7. TEST YOUR INSTALLATION

  To test the installation, open an interactive Python shell and import the TensorFlow module:

  $ cd
  $ python
  …
  import tensorflow as tf
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcublas.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcudnn.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcufft.so locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcuda.so.1 locally
  I tensorflow/stream_executor/dso_loader.cc:105] successfully opened CUDA library libcurand.so locally
  

  With the TensorFlow module imported, the next step to test the installation is to create a TensorFlow Session, which will initialize the available computing devices and provide a means of executing computation graphs:

  sess = tf.Session()

  This command will print out some information on the detected hardware configuration. For example, the output on a system containing a Tesla M40 GPU is:

  sess = tf.Session()
  I tensorflow/core/common_runtime/gpu/gpu_init.cc:102] Found device 0 with properties:
  name: Tesla M40
  major: 5 minor: 2 memoryClockRate (GHz) 1.112
  pciBusID 0000:04:00.0
  Total memory: 11.25GiB
  Free memory: 11.09GiB
  …

  To manually control which devices are visible to TensorFlow, set the CUDA_VISIBLE_DEVICES environment variable when launching Python. For example, to force the use of only GPU 0:

  $ CUDA_VISIBLE_DEVICES=0 python

  You should now be able to run a Hello World application:

  hello_world = tf.constant(“Hello, TensorFlow!”)
  print sess.run(hello_world)
  Hello, TensorFlow!
  print sess.run(tf.constant(123)*tf.constant(456))
  56088
  

  TensorFlow의 구현 코드(TensorFlow implementation)를 통해 graph에 정의된 내용이 실행가능한 작업들(operation)로 변환되고 CPU나 GPU같이 이용가능한 연산 자원들에 뿌려집니다. 코드로 어느 CPU 혹은 GPU를 사용할 지 명시적으로 지정할 필요는 없습니다. 작업을 가능한 한 많이 처리하기 위해 TensorFlow는 (컴퓨터가 GPU를 가지고 있다면) 첫 번째 GPU를 이용하니까요.

  만약 컴퓨터에 복수의 GPU가 있어서 이를 사용하려면, op을 어느 하드웨어에 할당할 지 명시적으로 밝혀야 합니다. 작업에 사용할 CPU 혹은 GPU를 지정하려면 with…Device 구문을 사용하면 됩니다.

  with tf.Session() as sess:
    with tf.device("/gpu:1"):
      matrix1 = tf.constant([[3., 3.]])
      matrix2 = tf.constant([[2.],[2.]])
      product = tf.matmul(matrix1, matrix2)
      ...
  

  이용할 CPU 혹은 GPU는 문자열로 지정할 수 있습니다. 현재 지원되는 것은 아래와 같습니다.

  - "/cpu:0": 컴퓨터의 CPU.
  - "/gpu:0": 컴퓨터의 1번째 GPU.
  - "/gpu:1": 컴퓨터의 2번쨰 GPU.
  

  TensorFlow 1.0.0-RC0 Release

  아래의 글은 https://tensorflow.blog/2017/01/27/tensorflow-1-0-0-rc0-release/에서 퍼 온 글이다.

  파이썬 2.7

  Ubuntu/Linux 64-bit, CPU only, Python 2.7
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc0-cp27-none-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, GPU enabled, Python 2.7
  

  Requires CUDA toolkit 8.0 and CuDNN v5.
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp27-none-linux_x86_64.whl

  # Mac OS X, CPU only, Python 2.7:
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc0-py2-none-any.whl

  # Mac OS X, GPU enabled, Python 2.7:
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc0-py2-none-any.whl

  파이썬 3.x

  Ubuntu/Linux 64-bit, CPU only, Python 3.3
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc0-cp33-cp33m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, GPU enabled, Python 3.3
  

  Requires CUDA toolkit 8.0 and CuDNN v5.
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp33-cp33m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, CPU only, Python 3.4
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc0-cp34-cp34m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, GPU enabled, Python 3.4
  

  Requires CUDA toolkit 8.0 and CuDNN v5.
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp34-cp34m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, CPU only, Python 3.5
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc0-cp35-cp35m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, GPU enabled, Python 3.5
  

  Requires CUDA toolkit 8.0 and CuDNN v5.
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp35-cp35m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, CPU only, Python 3.6
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.0.0rc0-cp36-cp36m-linux_x86_64.whl

  # Ubuntu/Linux 64-bit, GPU enabled, Python 3.6
  

  Requires CUDA toolkit 8.0 and CuDNN v5.
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.0.0rc0-cp36-cp36m-linux_x86_64.whl

  # Mac OS X, CPU only, Python 3.4 or 3.5:
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.0.0rc0-py3-none-any.whl

  # Mac OS X, GPU enabled, Python 3.4 or 3.5:
  

  $ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/gpu/tensorflow_gpu-1.0.0rc0-py3-none-any.whl

  설치
  

  $ sudo pip install –upgrade $TF_BINARY_URL

  • 윈도우즈 CPU:

  C:> pip install –upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.0.0rc0-cp35-cp35m-win_amd64.whl

  • 윈도우즈 GPU:

  C:> pip install –upgrade https://storage.googleapis.com/tensorflow/windows/gpu/tensorflow_gpu-1.0.0rc0-cp35-cp35m-win_amd64.whl

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