建议不要使用Centos6.5以及更低的系统安装Tensorflow GPU版本,可以尝试下CPU版的,具体情况可以参见:https://blog.csdn.net/edward_zcl/article/details/88585463
反正我是放弃Centos6.5系统下安装GPU版的Tensorflow了,Centos6.5感觉是个坎。。
Ubuntu用户请参见:https://blog.csdn.net/edward_zcl/article/details/88636903
以下主要讲解Centos7下面安装Tensorflow,转载自:https://blog.csdn.net/liuguangrong/article/details/78737419
CentOS7下安装Anaconda3和Tensorflow
Anaconda3下载
从Anaconda官网下载linux版本:https://www.anaconda.com/download/#linux
Anaconda3安装
将下载好的文件Anaconda3-5.0.1-Linux-x86_64.sh执行如下命令:
# bash Anaconda3-5.0.1-Linux-x86_64.sh
安装过程中修改Anaconda3的安装路径为/opt/modules/anaconda3:
Do you accept the license terms? [yes|no]
Please answer 'yes' or 'no':'
>>> yes
Anaconda3 will now be installed into this location:
/root/anaconda3
- Press ENTER to confirm the location
- Press CTRL-C to abort the installation
- Or specify a different location below
[/root/anaconda3] >>> /opt/modules/anaconda3
PREFIX=/opt/modules/anaconda3
等待安装完成提示信息,询问是否要将Anaconda3添加到PATH环境变量中,直接回车(选择no):
installation finished.
Do you wish the installer to prepend the Anaconda3 install location
to PATH in your /root/.bashrc ? [yes|no]
[no] >>>
You may wish to edit your .bashrc to prepend the Anaconda3 install location to PATH:
export PATH=/opt/modules/anaconda3/bin:$PATH
Thank you for installing Anaconda3!
手动将export PATH=/opt/modules/anaconda3/bin:$PATH添加到/etc/profile中, 最后source /etc/profile使环境变量生效:
# source /etc/profile
Tensorflow安装
建立Tensorflow运行环境
Tensorflow目前Python3版本最高支持到Python3.5,所以选择Python 3.5, 只需要执行conda create -n tensorflow python=3.5指令:
## Python 2.7
# conda create -n tensorflow python=2.7
## Python 3.4
# conda create -n tensorflow python=3.4
## Python 3.5
# conda create -n tensorflow python=3.5
在Anaconda3中创建Tensorflow虚拟环境:
Fetching package metadata ...........
Solving package specifications: .
Package plan for installation in environment /opt/modules/anaconda3/envs/tensorflow:
The following NEW packages will be INSTALLED:
ca-certificates: 2017.08.26-h1d4fec5_0
certifi: 2017.11.5-py35h9749603_0
libedit: 3.1-heed3624_0
libffi: 3.2.1-hd88cf55_4
libgcc-ng: 7.2.0-h7cc24e2_2
libstdcxx-ng: 7.2.0-h7a57d05_2
ncurses: 6.0-h9df7e31_2
openssl: 1.0.2m-h26d622b_1
pip: 9.0.1-py35h7e7da9d_4
python: 3.5.4-h417fded_24
readline: 7.0-ha6073c6_4
setuptools: 36.5.0-py35ha8c1747_0
sqlite: 3.20.1-hb898158_2
tk: 8.6.7-hc745277_3
wheel: 0.30.0-py35hd3883cf_1
xz: 5.2.3-h55aa19d_2
zlib: 1.2.11-ha838bed_2
Proceed ([y]/n)?
libffi-3.2.1-h 100% |##################################################################| Time: 0:00:00 137.60 kB/s
ncurses-6.0-h9 100% |##################################################################| Time: 0:00:01 622.10 kB/s
openssl-1.0.2m 100% |##################################################################| Time: 0:00:03 1.06 MB/s
tk-8.6.7-hc745 100% |##################################################################| Time: 0:00:02 1.13 MB/s
xz-5.2.3-h55aa 100% |##################################################################| Time: 0:00:00 1.28 MB/s
zlib-1.2.11-ha 100% |##################################################################| Time: 0:00:00 1.59 MB/s
readline-7.0-h 100% |##################################################################| Time: 0:00:00 1.27 MB/s
sqlite-3.20.1- 100% |##################################################################| Time: 0:00:01 1.41 MB/s
python-3.5.4-h 100% |##################################################################| Time: 0:00:07 3.87 MB/s
certifi-2017.1 100% |##################################################################| Time: 0:00:00 6.01 MB/s
setuptools-36. 100% |##################################################################| Time: 0:00:00 6.55 MB/s
wheel-0.30.0-p 100% |##################################################################| Time: 0:00:00 6.82 MB/s
pip-9.0.1-py35 100% |##################################################################| Time: 0:00:00 6.78 MB/s
#
# To activate this environment, use:
# > source activate tensorflow
#
# To deactivate an active environment, use:
# > source deactivate
#
为了简便也可以直接指定版本python=3.5, 且克隆anaconda所有的Python包:
conda create -n tensorflow python=3.5 anaconda
conda环境管理
列出所有的环境
# conda info --envs
创建一个指定Python版本且包含anaconda所有Python包的新环境
# conda create -n py36 python=3.6 anaconda
克隆一个环境
创建一个和root环境一样的副本:
conda create -n py36 --clone root
删除一个环境
# conda remove -n py36 --all
在conda环境下安装tensorflow(pip安装方式)
激活conda环境(tensorflow)
# source activate tensorflow
根据tensorflow的版本设置环境变量(以CPU版本为例)
Tensorflow的源码地址: https://github.com/tensorflow/tensorflow,如下三种环境Python2.7, Python3.4, Python3.5,选择一种(Python3.5)运行:
## Linux 64-bit, CPU only, Python 2.7
(tensorflow)$ export TF_BINARY_URL=https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON2,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-cp27-none-linux_x86_64.whl
## Linux 64-bit, CPU only, Python 3.4
(tensorflow)$ export TF_BINARY_URL=https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3,label=cpu-slave/lastSuccessfulBuild/artifact/pip_test/whl/tf_nightly-1.head-cp34-cp34m-linux_x86_64.whl
## Linux 64-bit, CPU only, Python 3.5
(tensorflow)$ export TF_BINARY_URL=https://ci.tensorflow.org/view/tf-nightly/job/tf-nightly-linux/TF_BUILD_IS_OPT=OPT,TF_BUILD_IS_PIP=PIP,TF_BUILD_PYTHON_VERSION=PYTHON3.
使用pip命令安装tensorflow
选择一种安装环境(Python 3):
## Python 2
(tensorflow)# pip install --ignore-installed --upgrade $TF_BINARY_URL
## Python 3
(tensorflow)# pip install --ignore-installed --upgrade $TF_BINARY_URL
使用conda命令安装tensorflow
Using conda参照如下网址:
A community maintained conda package is available from conda-forge.
https://github.com/conda-forge/tensorflow-feedstock
Only the CPU version of TensorFlow is available at the moment and can be installed in the conda environment for Python 2 or Python 3.
$ source activate tensorflow
(tensorflow)#
Linux/Mac OS X, Python 2.7/3.4/3.5, CPU only:
(tensorflow)# conda install -c conda-forge tensorflow
参考资料
【1】https://docs.anaconda.com/anaconda/faq#how-do-i-get-the-latest-anaconda-with-python-3-5
【2】http://blog.csdn.net/goodshot/article/details/62046214
【3】http://blog.csdn.net/nxcxl88/article/details/52704877?locationNum=13