一、248机器Python3源码安装
- ssh之后到根目录（例如
/home/shgx）[shgx为用户名]，下载指定版本的Python3源码：wget https://www.python.org/ftp/python/3.5.6/Python-3.5.6.tgz - 解压文件
tar -xvf Python-3.5.6.tgz - 在根目录下（
/home/shgx）创建如下路径（mkdir -p bin/Python3）,使用pwd检查Python3下的路径为/home/shgx/bin/Python3 - 打开刚刚解压的文件目录
cd /home/shgx/Python-3.5.6/（下面的步骤5.6.7都在此目录下执行） - 执行
./configure --prefix=/home/shgx/bin/Python3进行检测 - 执行
make进行编译 - 执行
make install进行安装 - 打开
cd /home/shgx/bin/Python3/bin执行python3以及pip3或者/pip3查看是否运行成功， - 成功之后在根目录下（
/home/shgx）创建软链接
ln -s /home/shgx/bin/Python3/bin/python3 python3 - 这样在你的根目录下就可以执行
python3了
二、248机器安装GPU-TenSorFlow
- 可以通过上面创建软连接的方式同样创建pip3的软连接，或者进入直接调用目录
/home/shgx/bin/Python3/bin/pip3进行安装 - 安装GPU版本的TensorFlow
/home/shgx/bin/Python3/bin/pip3 install tensorflow-gpu==1.2 - 安装Keras
/home/shgx/bin/Python3/bin/pip3 install keras==2.1.5 - 以此类推...可以安装其他库
三、 211机器源码安装TensorFlow
- 确认安装了Python3(我的安装路径为
/home/shgx/Python3.5)以及numpy、wheel - 查看CUDA版本,并记录版本为
7.1.2
cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
#define CUDNN_MAJOR 7
#define CUDNN_MINOR 1
#define CUDNN_PATCHLEVEL 2
--
#define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
#include "driver_types.h"
3.在本地目录下创建临时文件夹如：mkdir -p tmp/tensorflow_pkg
- 下载tensorflow源码
git clone https://github.com/tensorflow/tensorflow - 以上是准备工作，下面开始安装，首先进入tensorflow文件夹
cd tensorflow，切换分支git checkout r1.6 - 执行
./configure进行检测,configure成功后，依然还在tensorflow的根目录下输入以下命令（Yes or No可根据你的需要进行选择，其中需要填写CUDA版本）
[shgx@deep-worker1 tensorflow]$ ./configure
You have bazel 0.11.1- (@non-git) installed.
Please specify the location of python. [Default is /usr/bin/python]: /home/shgx/Python3.5/bin/python3
Found possible Python library paths:
/home/shgx/Python3.5/lib/python3.5/site-packages
Please input the desired Python library path to use. Default is [/home/shgx/Python3.5/lib/python3.5/site-packages]
Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]:
jemalloc as malloc support will be enabled for TensorFlow.
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 Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.
Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]:
No Apache Kafka Platform support will be enabled for TensorFlow.
Do you wish to build TensorFlow with XLA JIT support? [y/N]:
No XLA JIT support will be enabled for TensorFlow.
Do you wish to build TensorFlow with GDR support? [y/N]:
No GDR support will be enabled for TensorFlow.
Do you wish to build TensorFlow with VERBS support? [y/N]:
No VERBS support will be enabled for TensorFlow.
Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]:
No OpenCL SYCL support will be enabled for TensorFlow.
Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.
Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]: 9.1
Please specify the location where CUDA 9.1 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]:
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:7.1.2
Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]:
Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Do you wish to build TensorFlow with TensorRT support? [y/N]:
No TensorRT support will be enabled for TensorFlow.
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: 6.1,6.1,6.1,6.1]
Do you want to use clang as CUDA compiler? [y/N]:
nvcc will be used as CUDA compiler.
Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
Do you wish to build TensorFlow with MPI support? [y/N]:
No MPI support will be enabled for TensorFlow.
Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:
Not configuring the WORKSPACE for Android builds.
Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
--config=mkl # Build with MKL support.
--config=monolithic # Config for mostly static monolithic build.
--config=tensorrt # Build with TensorRT support.
Configuration finished
- configure成功后，依然还在tensorflow的根目录下输入以下命令(大约20分钟)
bazel build --config=opt --config=cuda tensorflow/tools/pip_package:build_pip_package
- 之后执行：
bazel-bin/tensorflow/tools/pip_package/build_pip_package /home/shgx/tmp/tensorflow_pkg
- 最后
cd /tmp/tensorflow_pkg切换到/tmp/tensorflow_pkg目录下，使用pip3进行安装pip3 install tensorflow-1.6.0-cp35-cp35m-linux_x86_64.whl（具体名字和你的python版本有关）
四、GPU使用
- 使用
nvidia-smi查看GPU使用情况 - 选择一块GPU进行实验
CUDA_VISIBLE_DEVICES=0 python3 xxx.py
