tensorflow和caffe安装（cuda9.0+cudnn7)

tensorflow安装（cuda9.0+cudnn7)

1)升级pip

pip install -U numpy
或者
pip install --upgrade pip
或者
easy_install -U pip

2)安装protobuf

apt-get install autoconf automake libtool
git clone --recursive https://github.com/google/protobuf.git ./autogen.sh
./configure
make
make install
ldconfig
cd protobuf/python    ##进入解压protobuf后中的python文件夹，里面会有buile文件夹与setup.py文件等
python setup.py build
python setup.py install
cd /usr/local/lib/python2.7/site-packages   #进入tensorflow想关联的python的目录下修改
chmod -R 755 .*
protoc –version #查看安装成功

3) 安装tensorflow

下载whl地址：https://mega.nz/#!U7R3QbzK!fmCi-qr5W3zfkBBpsZAPz3wGU4iXkAJNhwxTnTerM48
pip install tensorflow-1.3.0rc1-cp27-cp27mu-linux_x86_64.whl
python
import tensorflow as tf #查看是否安装成功

caffe/pycaffe安装（cuda9.0+cudnn7)

1)https://github.com/BVLC/caffe下载caffe

cp Makefile.config.example Makefile.config

修改为

## Refer to http://caffe.berkeleyvision.org/installation.html # Contributions simplifying and improving our build system are welcome!

# cuDNN acceleration switch (uncomment to build with cuDNN).
USE_CUDNN := 1

# CPU-only switch (uncomment to build without GPU support).
# CPU_ONLY := 1

# uncomment to disable IO dependencies and corresponding data layers
# USE_OPENCV := 0
# USE_LEVELDB := 0
# USE_LMDB := 0

# uncomment to allow MDB_NOLOCK when reading LMDB files (only if necessary)
#   You should not set this flag if you will be reading LMDBs with any
#   possibility of simultaneous read and write
# ALLOW_LMDB_NOLOCK := 1

# Uncomment if you're using OpenCV 3
OPENCV_VERSION := 3

# To customize your choice of compiler, uncomment and set the following.
# N.B. the default for Linux is g++ and the default for OSX is clang++
# CUSTOM_CXX := g++

# CUDA directory contains bin/ and lib/ directories that we need.
CUDA_DIR := /usr/local/cuda
# On Ubuntu 14.04, if cuda tools are installed via
# "sudo apt-get install nvidia-cuda-toolkit" then use this instead:
# CUDA_DIR := /usr

# CUDA architecture setting: going with all of them.
# For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility.
# For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.
# For CUDA >= 9.0, comment the *_20 and *_21 lines for compatibility.
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_60,code=sm_60 \
-gencode arch=compute_61,code=sm_61 \
-gencode arch=compute_61,code=compute_61

# BLAS choice:
# atlas for ATLAS (default)
# mkl for MKL
# open for OpenBlas
BLAS := open
# Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
# Leave commented to accept the defaults for your choice of BLAS
# (which should work)!
# BLAS_INCLUDE := /path/to/your/blas
# BLAS_LIB := /path/to/your/blas

# Homebrew puts openblas in a directory that is not on the standard search path
# BLAS_INCLUDE := $(shell brew --prefix openblas)/include
# BLAS_LIB := $(shell brew --prefix openblas)/lib

# This is required only if you will compile the matlab interface.
# MATLAB directory should contain the mex binary in /bin.
# MATLAB_DIR := /usr/local
# MATLAB_DIR := /Applications/MATLAB_R2012b.app

# NOTE: this is required only if you will compile the python interface.
# We need to be able to find Python.h and numpy/arrayobject.h.
PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/local/lib/python2.7/dist-packages/numpy/core/include
# Anaconda Python distribution is quite popular. Include path:
# Verify anaconda location, sometimes it's in root.
# ANACONDA_HOME := $(HOME)/anaconda
# PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
# $(ANACONDA_HOME)/include/python2.7 \
# $(ANACONDA_HOME)/lib/python2.7/site-packages/numpy/core/include

# Uncomment to use Python 3 (default is Python 2)
# PYTHON_LIBRARIES := boost_python3 python3.5m
# PYTHON_INCLUDE := /usr/include/python3.5m \
#                 /usr/lib/python3.5/dist-packages/numpy/core/include

# We need to be able to find libpythonX.X.so or .dylib.
PYTHON_LIB := /usr/lib
# PYTHON_LIB := $(ANACONDA_HOME)/lib

# Homebrew installs numpy in a non standard path (keg only)
# PYTHON_INCLUDE += $(dir $(shell python -c 'import numpy.core; print(numpy.core.__file__)'))/include
# PYTHON_LIB += $(shell brew --prefix numpy)/lib

# Uncomment to support layers written in Python (will link against Python libs)
WITH_PYTHON_LAYER := 1

# Whatever else you find you need goes here.
INCLUDE_DIRS := /usr/include/hdf5/serial
LIBRARY_DIRS := /usr/lib/x86_64-linux-gnu/hdf5/serial

# If Homebrew is installed at a non standard location (for example your home directory) and you
4000
use it for general dependencies
# INCLUDE_DIRS += $(shell brew --prefix)/include
# LIBRARY_DIRS += $(shell brew --prefix)/lib

# NCCL acceleration switch (uncomment to build with NCCL)
# https://github.com/NVIDIA/nccl (last tested version: v1.2.3-1+cuda8.0)
# USE_NCCL := 1

# Uncomment to use `pkg-config` to specify OpenCV library paths.
# (Usually not necessary -- OpenCV libraries are normally installed in one of the above $LIBRARY_DIRS.)
USE_PKG_CONFIG := 1

# N.B. both build and distribute dirs are cleared on `make clean`
BUILD_DIR := build
DISTRIBUTE_DIR := distribute

# Uncomment for debugging. Does not work on OSX due to https://github.com/BVLC/caffe/issues/171 # DEBUG := 1

# The ID of the GPU that 'make runtest' will use to run unit tests.
TEST_GPUID := 0

# enable pretty build (comment to see full commands)
Q ?= @

2)环境变量

export CPLUS_INCLUDE_PATH=/usr/include/python2.7

3）安装protobuf

下载https://github.com/google/protobuf/releases/download/v2.6.1/protobuf-2.6.1.tar.gz

#remove
sudo apt-get remove libprotobuf-dev protobuf-compiler
sudo apt-get remove libprotobuf-lite8 libprotoc8
sudo apt-get remove python-protobuf
sudo pip uninstall protobuf
#如果安装了anaconda
conda uninstall protobuf
tar -zxvf protobuf-2.6.1.tar.gz
sudo apt-get install build-essential
cd protobuf-2.6.1/
./configure
make
make check
sudo make install

在/etc/ld.so.conf.d/目录下创建文件bprotobuf.conf文件,文件内容如下

/usr/local/lib

命令行执行

ldconfig

这时,再输入protoc –version就可以正常看到版本号了

pip install protobuf==2.6.1
apt-get install python-numpy

4)安装caffe/pycaffe

apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
apt-get install --no-install-recommends libboost-all-dev
apt-get install libopenblas-dev liblapack-dev libatlas-base-dev
apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
make all –j8
make test –j8
make runtest –j8
make pycaffe  -j8

配置环境变量，以便python调用

gedit ~/.bashrc

将export PYTHONPATH=/home/caffe/python:$PYTHONPATH添加到文件中

source ~/.bashrc

测试

python
import caffe