环境:win10, vs2015
首先呢准备cuda, cuDNN, caffe-windows源码,cmake, python等工具
事先声明下,请详细阅读gitbub的caffe-windows的Readme,别瞎搞。嗯。。。
安装他要求的cuda8.0和cuDNN v5谢谢。你就会少踩坑。
下载地址:
caffe_windows源码:https://github.com/BVLC/caffe/tree/windows
cuda8.0:https://developer.nvidia.com/cuda-80-ga2-download-archive
cuDNN v5:https://developer.nvidia.com/rdp/cudnn-archive 里面的 Download cuDNN v5 (May 27, 2016), for CUDA 8.0
cmake: https://cmake.org/download/ 找到.msi后缀的 cmake-3.15.2-win64-x64.msi
python: 3.5版本的或者2.7 我是装3.5的
caffe的依赖包:libraries_v140_x64_py35_1.1.0
自己去下放在C:\用户\.....\.caffe\dependencies
然后你就开始装。。。这是一个漫长的过程。
多补充一句:
安装cuda的时候默认路径是 什么Data/tmp下,你修改成NVIDIA GPU Computing Toolkit也比较好认。
检测匹配的时候会提示你跟显卡不匹配(一堆英文),无视他就行。
安装完cuda后 运行cmd,nvcc -V看下是否版本是8.0
caffe-windows\scripts 文件夹下有build_win.cmd 用编辑器打开,改一些东西。
MSVC_VERSION=14 (vs2015)
WITH_NINJA=0 (我们是用cmake)
PYTHON_VERSION=3
RUN_INSTALL=1
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=1
)
添加一个-DCUDNN_ROOT=你cuDNN的路径
:: Configure using cmake and using the caffe-builder dependencies
:: Add -DCUDNN_ROOT=C:/Projects/caffe/cudnn-8.0-windows10-x64-v5.1/cuda ^
:: below to use cuDNN
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=D:\build_lib\cuda ^
"%~dp0\.."
将build_win.cmd在cmd里打开,运行。
生成的vs工程就是当前cmd的路径。
分别生成ALL_BUILD的Debug和Release版本。
caffe-windows\data\mnist运行get_mnist.sh
wget报错:下载wget包,将路径添加到环境变量Path
在caffe-windows\examples\mnist找到create_mnist.sh不能直接运行。
前面生成的convert_mnist_data.exe不是bin后缀
将4个数据复制到\build\examples\mnist\Release文件夹内
打开cmd切换成该路径:运行
convert_mnist_data.exe train-images-idx3-ubyte train-labels-idx1-ubyte mnist_train_lmdb --backend=lmdb
convert_mnist_data.exe t10k-images-idx3-ubyte t10k-labels-idx1-ubyte mnist_test_lmdb --backend=lmdb
修改caffe-windows\examples\mnist 里的lenet_train_test.prototxt
将2个source的路径改成据对路径
和lenet_solver.prototxt的2个source的路径改成据对路径
添加caffe.exe到环境变量。
执行
caffe.exe train --solver=caffe-windows\examples\mnist\lenet_solver.prototxt
大功告成。。。