caffe 自己安装记录，cpu版

参考官方文档 依赖库安装 Ubuntu14.02版本 https://github.com/BVLC/caffe/blob/master/docs/install_apt.md
编译教程： https://github.com/BVLC/caffe/blob/master/docs/installation.md

---
title: Installation: Ubuntu
---

Ubuntu
Installation

General dependencies

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev

1 直接使用提供的 命令即可

sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install --no-install-recommends libboost-all-dev

注意： opencv， boost 最好安装高版本的库，高版本的库速度可能会快一些。zm$ apt-cache search opencv 能够查看的版本，方便安装指定版本的opencv。2------- 暂时只打算编译cpu版本，所以只cuda 安装这步直接跳过。CUDA: Install via the NVIDIA package instead of

apt-get

to be certain of the library and driver versions.
Install the library and latest driver separately; the driver bundled with the library is usually out-of-date. This can be skipped for CPU-only installation.3------------------直接只用提供的命令行 sudo apt-get install libatlas-base-devBLAS: install ATLAS by

sudo apt-get install libatlas-base-dev

or install OpenBLAS or MKL for better CPU
performance.4--------------------------------直接使用给定的命令行，

sudo
apt-get install

python-dev

Python (optional): if you use the default Python you will need to

sudo apt-get install

the

python-dev

package
to have the Python headers for building the pycaffe interface.注意： 如果需要编译pycaffe，还需要安装python-numpy库，这步不安装，编译pycaffe时会提示错误。安装命令为：

sudo
apt-get install

python-numpy

到这里 库的安装就完成了。下边到了编译过程。Remaining dependencies, 14.04Everything is packaged in 14.04.

sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

Remaining dependencies, 12.04These dependencies need manual installation in 12.04.

# glog
wget https://google-glog.googlecode.com/files/glog-0.3.3.tar.gz tar zxvf glog-0.3.3.tar.gz
cd glog-0.3.3
./configure
make && make install
# gflags
wget https://github.com/schuhschuh/gflags/archive/master.zip unzip master.zip
cd gflags-master
mkdir build && cd build
export CXXFLAGS="-fPIC" && cmake .. && make VERBOSE=1
make && make install
# lmdb
git clone https://github.com/LMDB/lmdb cd lmdb/libraries/liblmdb
make && make install

Note that glog does not compile with the most recent gflags version (2.1), so before that is resolved you will need to build with glog first.Continue with compilation.------------------------------------------------------------------------------------------------------------------------------------------------------开始编译--------------------------------------------------------------------------------------------------------------------------------------------------------

Installation

Prior to installing, have a glance through this guide and take note of the details for your platform. We install and run Caffe on Ubuntu 14.04 and 12.04, OS X 10.10 / 10.9 / 10.8, and AWS. The official Makefile and

Makefile.config

build
are complemented by an automatic CMake build from the community.Prerequisites
Compilation
Hardware
Platforms: Ubuntu guide, OS
X guide, and RHEL / CentOS / Fedora guideWhen updating Caffe, it's best to

make clean

before re-compiling.

Prerequisites

Caffe has several dependencies:CUDA is required for GPU mode.library version 7.0 and the latest driver version are recommended, but 6.* is fine too
5.5, and 5.0 are compatible but considered legacyBLAS via ATLAS, MKL, or OpenBLAS.
Boost >= 1.55

protobuf

,

glog

,

gflags

,

hdf5

Optional dependencies:OpenCV >= 2.4 including 3.0
IO libraries:

lmdb

,

leveldb

(note:
leveldb requires

snappy

)
cuDNN for GPU acceleration (v3)Pycaffe and Matcaffe interfaces have their own natural needs.For Python Caffe:

Python 2.7

or

Python
3.3+

,

numpy (>= 1.7)

, boost-provided

boost.python

For MATLAB Caffe: MATLAB with the

mex

compiler.cuDNN Caffe: for fastest operation Caffe is accelerated by drop-in integration of NVIDIA cuDNN.
To speed up your Caffe models, install cuDNN then uncomment the

USE_CUDNN := 1

flag in

Makefile.config

when
installing Caffe. Acceleration is automatic. The current version is cuDNN v3; older versions are supported in older Caffe.CPU-only Caffe: for cold-brewed CPU-only Caffe uncomment the

CPU_ONLY := 1

flag in

Makefile.config

to
configure and build Caffe without CUDA. This is helpful for cloud or cluster deployment.

CUDA
and BLAS

Caffe requires the CUDA

nvcc

compiler to compile its GPU code and CUDA driver for GPU operation. To install CUDA, go to the NVIDIA
CUDA website and follow installation instructions there. Install the library and the latest standalone driver separately; the driver bundled with the library is usually out-of-date. Warning! The 331.* CUDA driver series has a critical
performance issue: do not use it.For best performance, Caffe can be accelerated by NVIDIA cuDNN. Register for free at the cuDNN site, install it,
then continue with these installation instructions. To compile with cuDNN set the

USE_CUDNN := 1

flag set in your

Makefile.config

.Caffe requires BLAS as the backend of its matrix and vector computations. There are several implementations of this library. The choice is yours:ATLAS: free, open source, and so the default for Caffe.
Intel MKL: commercial and optimized for Intel CPUs, with a free trial and student licenses.Install MKL.
Set

BLAS := mkl

in

Makefile.config

OpenBLAS: free and open source; this optimized and parallel BLAS could require more effort to install, although
it might offer a speedup.Install OpenBLAS
Set

BLAS := open

in

Makefile.config

Python
and/or MATLAB Caffe (optional)

Python

The main requirements are

numpy

and

boost.python

(provided
by boost).

pandas

is useful too and needed for some examples.You can install the dependencies with

for req in $(cat requirements.txt); do pip install $req; done

but we suggest first installing the Anaconda Python distribution, which provides most of the necessary
packages, as well as the

hdf5

library dependency.To import the

caffe

Python module after completing the installation, add the module directory to your

$PYTHONPATH

by

export
PYTHONPATH=/path/to/caffe/python:$PYTHONPATH

or the like. You should not import the module in the

caffe/python/caffe

directory!Caffe's Python interface works with Python 2.7. Python 3.3+ should work out of the box without protobuf support. For protobuf support please install protobuf 3.0 alpha (https://developers.google.com/protocol-buffers/).
Earlier Pythons are your own adventure.

MATLAB

Install MATLAB, and make sure that its

mex

is in your

$PATH

.Caffe's MATLAB interface works with versions 2015a, 2014a/b, 2013a/b, and 2012b.

Windows

There is an unofficial Windows port of Caffe at niuzhiheng/caffe:windows. Thanks @niuzhiheng!

Compilation

Caffe can be compiled with either Make or CMake. Make is officially supported while CMake is supported by the community.
5---------------编译，----------------------------------------------------------------------------------

cp Makefile.config.example Makefile.config

这里只编译cpu版本，

vim    Makefile.config

For CPU-only Caffe, uncomment

CPU_ONLY := 1

in

Makefile.config

.最后 make -j 使用所有的核 编译，这样编译的速度最快-------------------------------------------------------------------------------------------[/code]

5---------------编译 Pycaffe----------------------------------------------------------------------------------

cp Makefile.config.example Makefile.config

pycaffe 需要 numpy 库的支持，所以需要安装Python-numpy 库

sudo apt-get install

python-numpy

最后 make pycaffe 即可

5---------------测试：跑mnist 例子---------------------------------------------------------------------------------

sh data/mnist/get_mnist.sh

sh example/mnist/creat_mnist.sh

打开 example/mnist/lenet_solver.prototxt 文件，把 GPU 改为 CPU ，最后 sh example/mnist/train_lenet.shmnist例子跑通，安装完毕。

Compilation
with Make

Configure the build by copying and modifying the example

Makefile.config

for your setup. The defaults should work, but uncomment
the relevant lines if using Anaconda Python.

cp Makefile.config.example Makefile.config
# Adjust Makefile.config (for example, if using Anaconda Python, or if cuDNN is desired)
make all
make test
make runtest

For CPU & GPU accelerated Caffe, no changes are needed.
For cuDNN acceleration using NVIDIA's proprietary cuDNN software, uncomment the

USE_CUDNN := 1

switch in

Makefile.config

.
cuDNN is sometimes but not always faster than Caffe's GPU acceleration.
For CPU-only Caffe, uncomment

CPU_ONLY := 1

in

Makefile.config

.To compile the Python and MATLAB wrappers do

make pycaffe

and

make
matcaffe

respectively. Be sure to set your MATLAB and Python paths in

Makefile.config

first!Distribution: run

make distribute

to create a

distribute

directory
with all the Caffe headers, compiled libraries, binaries, etc. needed for distribution to other machines.Speed: for a faster build, compile in parallel by doing

make all -j8

where 8 is the number of parallel threads
for compilation (a good choice for the number of threads is the number of cores in your machine).Now that you have installed Caffe, check out the MNIST tutorial and the reference
ImageNet model tutorial.

Compilation
with CMake

In lieu of manually editing

Makefile.config

to configure the build, Caffe offers an unofficial CMake build thanks to @Nerei, @akosiorek,
and other members of the community. It requires CMake version >= 2.8.7. The basic steps are as follows:

mkdir build
cd build
cmake ..
make all
make install
make runtest

See PR #1667 for options and details.

Hardware

Laboratory Tested Hardware: Berkeley Vision runs Caffe with K40s, K20s, and Titans including models at ImageNet/ILSVRC scale. We also run on GTX series cards (980s and 770s) and GPU-equipped MacBook Pros. We have not encountered any trouble
in-house with devices with CUDA capability >= 3.0. All reported hardware issues thus-far have been due to GPU configuration, overheating, and the like.CUDA compute capability: devices with compute capability <= 2.0 may have to reduce CUDA thread numbers and batch sizes due to hardware constraints. Your mileage may vary.Once installed, check your times against our reference performance numbers to
make sure everything is configured properly.Ask hardware questions on the caffe-users group.