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linux上caffe安装(docker 超简易操作版本)

linux上caffe安装(docker 超简易操作版本)

作者: 夕一啊 | 来源:发表于2018-10-10 12:58 被阅读0次

    由于caffe实在是太难安装了,需要依赖的库太多,安装了一天都没安装好(每安一个库都能 遇到一堆问题),最后选择在docker上安装,轻轻松松解决问题。

    1.下载docker镜像

    https://hub.docker.com/search/?isAutomated=0&isOfficial=0&page=1&pullCount=0&q=caffe&starCount=0
    这里有各个版本的caffe docker,随意选择一个(我选择的第一个)
    $ docker pull bvlc/caffe:gpu

    然后查看docker images里面会有下好的这个docker,复制image id (这里是47dee10d8ba0)

    $ nvidia-docker run -it -v 挂载目录:挂载目录 --name your_name 47dee10d8ba0 /bin/bash
    然后就进入docker了

    2.测试mnist例子

    进入/opt/caffe


    docker带的caffe目录,不需要再编译

    可以试验下这里的mnist例子。

    下载mnist数据集到这个目录下
    $ ./data/mnist/get_mnist.sh

    转换格式,在examples/mnist生成了两个目录:mnist_test_lmdb和mnist_train_lmdb
    $ ./examples/mnist/create_mnist.sh

    在 /opt/caffe文件夹下运行
    $ ./build/tools/caffe train --solver=examples/mnist/lenet_solver.prototxt

    训练结果
    成功!

    3. tips

    1.docker是个好东西,安装依赖包遇到一个又一个问题太要命了,不过安装过程让我对linux多些了解
    2.安装好带caffe的docker,可以直接在pythom后import cafffe,但是在我从github上clone的caffe库里运行mnist的例子不行,还是得编译caffe,因为import各种包的时候位置不对,所以必须得进入docker自带的caffe文件夹里。

    参考:https://www.cnblogs.com/wmlj/p/8681216.html # [运行caffe自带的mnist实例教程]

    4. 后记(编译caffe)

    以上步骤,可以使用标准的caffe了,但是caffe这个东西,和tensorflow不一样,它的层还得用c++写在源代码里,重新编译一次caffe才能用。
    我需要用的一个代码,自带一个caffe文件夹,下面是他重写过的caffe,要想运行它的代码,就不能用标准caffe而是得编译他的caffe。
    在caffe文件夹下,修改Makefile.config文件,

    ## 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
    # This code is taken from https://github.com/sh1r0/caffe-android-lib
    # USE_HDF5 := 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 := atlas
    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 := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial/
    LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib  /usr/lib/x86_64-linux-gnu/hdf5/serial/
    
    # 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
    
    # 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 ?= @
    CUSTOM_CXX := g++ -std=c++11
    

    然后
    $ make clean
    $ make all -j8 #使用8个进程
    $ make pycaffe
    这样就编译好了,因为之前的docker已经是把其他依赖库都装好了的,所以只需要重新编译下就行了。
    如果还是报错“ImportError: No module named _caffe”,说明python的路径问题,sys.path.insert(0,"/caffe/python")即可

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