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系统及工具包
win10版
visual studio 2015
CUDA10.0(咖啡是吴登盛吗CUDA8.0，魏冄10.0版伊凡诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇伊万诺维奇RTX 2070-2080 ti)
库恩。库恩。库恩云娥CUDA10.0阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金阿金
蟒蛇3.5-蟒蛇
咖啡吗是维安维安维安维安
是吗cuda -什么，范思哲范思哲范思哲库达你好，阿巧CUDA10.0云娥cudnn(密克罗尼西亚联邦):
python(python语言)跸3.5版汝母，是贺盛瑞常见问题解答朱庇特？朱庇特python3.5云娥；
安纳康达是男的男的男的男的，嘿嘿嘿嘿嘿嘿嘿嘿嘿，嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨嗨，唉哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟哟；
修改配置文件script/build_win.cmd
从第4行开始，修改如下：
3:缺省值
ifdefinedappveyor(
echosettingappveyordefaults回显式代理服务器-回显式代理服务器-回显式代理服务器-回显式代理服务器-回显式代理服务器-回显式代理服务器-回显式代理服务器-回显式代理服务器
ifnotdefineeedmsvc _ version tmsvc _ version=14
-=伊甸园美剧http://sfile。伊迪。com=-荣誉出品本字幕仅供学习交流，严禁用于商业途径
if not defined CPU _ only stCPU _ only=0
如果未定义cuda _ arch _ name设置uda _ arch _ name=auto
ifnotdefineedcmake _ config设置make _ config=版本
ifnotdefineese _ ncclsetuse _ nccl=0
ifnotdefineeddcmake _ build _ shared _ libassets ake _ build _ shared _ libs=0
ifnotdefineedrpython _ version tpython _ version=3
ifnotdefined riverbuild _ python odatsetbuild _ python=1
ifnotdefined riverbuild _ python _ layer build _ python _ layer=1
if not defined build _ MATLAB build _ MATLAB=0
ifnotdefined jpython _ exesettyhon _ exe=python
ifnotdefined ren _ test set run _ tests=1
ifnotdefined run _ lintsetrun _ lint=1
ifnotdefinerun _ installsetrun _ in
STALL=1
:: Set python 2.7 with conda as the default python
if !PYTHON_VERSION! EQU 2 (
set CONDA_ROOT=C:\Miniconda-x64
)
:: Set python 3.5 with conda as the default python
if !PYTHON_VERSION! EQU 3 (
set CONDA_ROOT=C:\anaconda
)
set PATH=!CONDA_ROOT!;!CONDA_ROOT!\Scripts;!CONDA_ROOT!\Library\bin;!PATH!
:: Check that we have the right python version
!PYTHON_EXE! --version
:: Add the required channels
conda config --add channels conda-forge
conda config --add channels willyd
:: Update conda
conda update conda -y
:: Download other required packages
conda install --yes cmake ninja numpy scipy protobuf==3.1.0 six scikit-image pyyaml pydotplus graphviz
if ERRORLEVEL 1 (
echo ERROR: Conda update or install failed
exit /b 1
)
:: Install cuda and disable tests if needed
if !WITH_CUDA! == 1 (
call %~dp0\appveyor\appveyor_install_cuda.cmd
set CPU_ONLY=0
set RUN_TESTS=0
set USE_NCCL=1
) else (
set CPU_ONLY=1
)
:: Disable the tests in debug config
if "%CMAKE_CONFIG%" == "Debug" (
echo Disabling tests on appveyor with config == %CMAKE_CONFIG%
set RUN_TESTS=0
)
:: Disable linting with python 3 until we find why the script fails
if !PYTHON_VERSION! EQU 3 (
set RUN_LINT=0
)
) else (
:: Change the settings here to match your setup
:: Change MSVC_VERSION to 12 to use VS 2013
if NOT DEFINED MSVC_VERSION set MSVC_VERSION=14
:: Change to 1 to use Ninja generator (builds much faster)
if NOT DEFINED WITH_NINJA set WITH_NINJA=0
:: Change to 1 to build caffe without CUDA support
if NOT DEFINED CPU_ONLY set CPU_ONLY=0
:: Change to generate CUDA code for one of the following GPU architectures
:: [Fermi Kepler Maxwell Pascal All]
if NOT DEFINED CUDA_ARCH_NAME set CUDA_ARCH_NAME=Auto
:: Change to Debug to build Debug. This is only relevant for the Ninja generator the Visual Studio generator will generate both Debug and Release configs
if NOT DEFINED CMAKE_CONFIG set CMAKE_CONFIG=Release
:: Set to 1 to use NCCL
if NOT DEFINED USE_NCCL set USE_NCCL=0
:: Change to 1 to build a caffe.dll
if NOT DEFINED CMAKE_BUILD_SHARED_LIBS set CMAKE_BUILD_SHARED_LIBS=0
:: Change to 3 if using python 3.5 (only 2.7 and 3.5 are supported)
if NOT DEFINED PYTHON_VERSION set PYTHON_VERSION=3
:: Change these options for your needs.
if NOT DEFINED BUILD_PYTHON set BUILD_PYTHON=1
if NOT DEFINED BUILD_PYTHON_LAYER set BUILD_PYTHON_LAYER=1
if NOT DEFINED BUILD_MATLAB set BUILD_MATLAB=0
:: If python is on your path leave this alone
if NOT DEFINED PYTHON_EXE set PYTHON_EXE=python
:: Run the tests
if NOT DEFINED RUN_TESTS set RUN_TESTS=0
:: Run lint
if NOT DEFINED RUN_LINT set RUN_LINT=0
:: Build the install target
if NOT DEFINED RUN_INSTALL set RUN_INSTALL=0
)
以上代码主要修改CPU_ONLY, WITH_NINJA, PYTHON_VERSION,CONDA_ROOT, 等变量；
第160行，修改如下：
cmake -G"!CMAKE_GENERATOR!" ^ -DBLAS=Open ^ -DCMAKE_BUILD_TYPE:STRING=%CMAKE_CONFIG% ^ -DBUILD_SHARED_LIBS:BOOL=%CMAKE_BUILD_SHARED_LIBS% ^ -DBUILD_python:BOOL=%BUILD_PYTHON% ^ -DBUILD_python_layer:BOOL=%BUILD_PYTHON_LAYER% ^ -DBUILD_matlab:BOOL=%BUILD_MATLAB% ^ -DCPU_ONLY:BOOL=%CPU_ONLY% ^ -DCOPY_PREREQUISITES:BOOL=1 ^ -DINSTALL_PREREQUISITES:BOOL=1 ^ -DUSE_NCCL:BOOL=!USE_NCCL! ^ -DCUDA_ARCH_NAME:STRING=%CUDA_ARCH_NAME% ^ -DCUDNN_ROOT=C:\Program Files\NVIDIA GPU Computing Toolkit\cudnn-10.0\cuda ^ "%~dp0\.."
以上代码主要添加一个变量CUDNN_ROOT;
修改文件cmake/cuda.cmake
第7行修改如下：
set(Caffe_known_gpu_archs "30 35 50 60 61 75")
第42行修改如下：
set(__nvcc_out "7.5")
# string(REGEX MATCH "([1-9].[0-9])" __nvcc_out "${__nvcc_out}")
# string(REPLACE "2.1" "2.1(2.0)" __nvcc_out "${__nvcc_out}")
set(CUDA_gpu_detect_output ${__nvcc_out} CACHE INTERNAL "Returned GPU architetures from caffe_detect_gpus tool" FORCE)
主要是添加了一行，注释了2行；
修改文件C:\Users\ducks\.caffe\dependencies\libraries_v140_x64_py35_1.1.0\libraries\include\boost-1_61\boost\config\compiler\nvcc.hpp
第22行开始，注释掉了几行：
// #if !defined(__CUDACC_VER__) || (__CUDACC_VER__ < 70500) // # define BOOST_NO_CXX11_VARIADIC_TEMPLATES // #endif
编译
删除caffe目录里面的build目录，如果之前编译失败了，必须执行这一步。
在caffe目录执行script\build_win.cmd
感谢你能够认真阅读完这篇文章，希望小编分享的“caffe如何安装”这篇文章对大家有帮助，同时也希望大家多多支持，关注行业资讯频道，更多相关知识等着你来学习!
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