ubuntu16.04 caffe cuda9.1 segnet nvidia gpu安装注意的点

旧城等待， 2022-05-28 12:30 146阅读 0赞

GPU驱动：R390

cuda：9.1

gcc：5.4.0

anaconda：2

GPU运算能力：2.1

CPU：8G

系统：ubuntu 16.04 x86\_64

安装一般依赖项：

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

还有官网提到的：

*  [BLAS][] via ATLAS, MKL, or OpenBLAS.
 *  [Boost][] >= 1.55
 *  `protobuf`, `glog`, `gflags`, `hdf5`

以及anaconda，这些没什么特别注意的，我选择的是anaconda2,因为我要用segnet，不过我也配了3,版本可以说是文件夹，库都在那放着，你要谁，你就在caffe的config中指定谁，他就会去找，所以理论上来讲，你无比自由想用谁用谁，你可以一个项目配一个caffe。我就是这么干的。甚至不同的数据库还称一个caffe。一个不爽想从gpu换cpu那就直接make clean，然后你的caffe大楼就会井然有序的拆除。暂时还没出现什么问题。等我看看caffe 官方doc

**安装驱动**

1.很重要的是：如果你用ubuntu那你的显卡驱动以及cuda都必须！**用apt安装**！，要不cuda可能会崩溃，原文：

‘for CUDA version. Note, the cuda version may break if your NVIDIA driver and CUDA toolkit are not installed by APT.’

所以推荐：http://www.cnblogs.com/xujianqing/p/6142963.html这篇文中的显卡驱动安装方式，但是！！！版本号要从375换到390，**因为cuda9要求驱动390**

**安装cuda**

官网要求的gcc版本是5.3.1，然而自带的是5.4,不过我依旧安装成功了。如果有问题再看吧。而且在http://caffe.berkeleyvision.org/install\_apt.html caffe官网的ubuntu安装页，写了16.04要用版本8的cuda，然而网上有不少9的教程，于是我来试试小白鼠好了。而且我的gpu运算能力只有2.1。可以看出我这是在作死的边缘试探。

![1093219-20180319154152089-8204313.png][]

**![1093219-20180319152221902-56814922.png][]**

https://developer.nvidia.com/cuda-gpus包括可以安装的CUDA的Gpu型号

**1.首先你要看https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html\#package-manager-metas，里面有个kernel版本安装要求，这个版本必须匹配你电脑的版本，而且在cuda之前安装，因为如果你跳过这个步骤，那么cuda driver就会自己找个版本。**

**具体命令**

sudo apt-get install linux-headers-$(uname -r)

2.安装cuda现在官网提供的最新版本9.1，要求R390驱动，如果你想要这个版本的cuda，直接复制粘贴网上的命令行，我也不知道会出现什么。

‘Before installing the CUDA Toolkit on Linux, please ensure that you have the latest NVIDIA driver R390 installed. The latest NVIDIA R390 driver is available at: www.nvidia.com/drivers’

![1093219-20180307202148142-1729545761.png][]

并且推荐使用官网提供的命令行，以及终端建议的命令行。

![1093219-20180307202338360-1991658904.png][]![1093219-20180307202348379-1246808423.png][]

3.安装library package工具包

参见https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html\#package-manager-metas

3.7节，我就不翻译了命令行是：

cat /var/lib/apt/lists/*cuda*Packages | grep "Package:"

sudo apt-get install cuda

sudo apt-get install cuda-drivers  
    测试安装cuda

cd /usr/local/cuda-9.1/samples/1_Utilities/deviceQuery
    
    make
    
    sudo ./deviceQuery

这个注意自己的路径，就是你的安装路径是否是这个，以及你的版本号，这都是经常需要注意的。我这个意思就是我安装的是9.1
    如果安装成功就会出现你的GPU配置：

![1093219-20180319153011710-1448698932.png][]

安装caffe 比较重要的是配置文件，也就是makefile.config. 你不要复制粘贴，你要读，很容易读懂的，#之后的内容不用看，是注释掉的。你必须要改的是CPU_only，cuda路径，还有anaconda路径，不是让你满电脑找安在那里了。 通常情况下安装路径和我一样，但是文件夹里面可能会有细节的不同，这个不同来源于版本。也就是说你的版本和我一样你就可以复制粘贴了。

## 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
    
    # 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-9.1
    # 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 lines for compatibility.
    CUDA_ARCH := -gencode arch=compute_30,code=sm_30 \
            -gencode arch=compute_35,code=sm_35 \
            -gencode arch=compute_50,code=sm_50 \
            -gencode arch=compute_50,code=compute_50
    
    # BLAS choice:
    # atlas for ATLAS (default)
    # mkl for MKL
    # open for OpenBlas
    BLAS := atlas
    # 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/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)/anaconda2
     PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
             $(ANACONDA_HOME)/include/python2.7 \
             $(ANACONDA_HOME)/lib/python2.7/site-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 := $(PYTHON_INCLUDE) /usr/local/include
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
    
    # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
    # INCLUDE_DIRS += $(shell brew --prefix)/include
    # LIBRARY_DIRS += $(shell brew --prefix)/lib
    
    # 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
    
    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 ?= @

然后在make all的时候 ，出现了：
    nvcc fatal   : Unsupported gpu architecture 'compute_20'
    ，看http://blog.csdn.net/kemgine/article/details/78781377，删除了两行cuda配置，我放上来的config已经删掉了，请注意这一点。

于是我换到了8.0

然后就没有错误了，然后我如愿以偿的得到了内存不足这一个错误。。。

下面给出8.0的成功的config文件仅供参考

## 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
    
    # 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-8.0
    # 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 lines for compatibility.
    CUDA_ARCH := -gencode arch=compute_20,code=sm_20 \
                    -gencode arch=compute_30,code=sm_30 \
            -gencode arch=compute_35,code=sm_35 \
            -gencode arch=compute_50,code=sm_50 \
            -gencode arch=compute_50,code=compute_50
    
    # BLAS choice:
    # atlas for ATLAS (default)
    # mkl for MKL
    # open for OpenBlas
    BLAS := atlas
    # 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/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)/anaconda2
     PYTHON_INCLUDE := $(ANACONDA_HOME)/include \
             $(ANACONDA_HOME)/include/python2.7 \
             $(ANACONDA_HOME)/lib/python2.7/site-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 := $(PYTHON_INCLUDE) /usr/local/include
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
    
    # If Homebrew is installed at a non standard location (for example your home directory) and you use it for general dependencies
    # INCLUDE_DIRS += $(shell brew --prefix)/include
    # LIBRARY_DIRS += $(shell brew --prefix)/lib
    
    # 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
    
    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 ?= @

[BLAS]: http://en.wikipedia.org/wiki/Basic_Linear_Algebra_Subprograms
[Boost]: http://www.boost.org/
[1093219-20180319154152089-8204313.png]: /images/20220528/2ef2bd344b0541ee8ab76628ce5925d4.png
[1093219-20180319152221902-56814922.png]: /images/20220528/cec8996ec3d242918d6c1f879e45b7a6.png
[1093219-20180307202148142-1729545761.png]: /images/20220528/86e823d7dc5748519f9a9ea7613cabf1.png
[1093219-20180307202338360-1991658904.png]: https://images2018.cnblogs.com/blog/1093219/201803/1093219-20180307202338360-1991658904.png
[1093219-20180307202348379-1246808423.png]: /images/20220528/48d1e60e53c44d728de37c1985a82220.png
[1093219-20180319153011710-1448698932.png]: /images/20220528/8a0b1bf49a0641c388de9e72f9598141.png