install Titan X on Ubuntu 14.04

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

sudo apt-get purge nvidia-*

sudo service lightdm stop

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.

sudo ./cuda_8.0.44_linux.run (you should not install its own nvidia-driver 367.48, as you have 367.57 already installed)

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!

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

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

install Titan X on Ubuntu 14.04

Step 1. Install NVIDIA CUDA

Step 2. Install NVIDIA cuDNN

Step 3. Install and upgrade PIP

Step 4. Install Bazel

Step 5. Install TensorFlow

Step 6. UPGRADE PROTOBUF

Step 7. TEST YOUR INSTALLATION

TensorFlow 1.0.0-RC0 Release

파이썬 2.7

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

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

Requires CUDA toolkit 8.0 and CuDNN v5.

# Mac OS X, CPU only, Python 2.7:

# Mac OS X, GPU enabled, Python 2.7:

파이썬 3.x

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

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

Requires CUDA toolkit 8.0 and CuDNN v5.

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

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

Requires CUDA toolkit 8.0 and CuDNN v5.

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

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

Requires CUDA toolkit 8.0 and CuDNN v5.

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

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

Requires CUDA toolkit 8.0 and CuDNN v5.

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

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

설치

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