![]() # MATLAB directory should contain the mex binary in /bin. # 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. # Custom (MKL/ATLAS/OpenBLAS) include and lib directories. # BLAS choice: # atlas for ATLAS (default) # mkl for MKL # open for OpenBlas gencode arch =compute_61,code =compute_61 # For CUDA < 8.0, comment the *_60 and *_61 lines for compatibility.ĬUDA_ARCH : = -gencode arch =compute_30,code =sm_30 \ # For CUDA < 6.0, comment the *_50 through *_61 lines for compatibility. ![]() # 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. 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. OPENCV_VERSION : = 3 # To customize your choice of compiler, uncomment and set the following. # CPU_ONLY := 1 # uncomment to disable IO dependencies and corresponding data layers # USE_OPENCV := 0 # USE_LEVELDB := 0 # USE_LMDB := 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 USE_CUDNN : = 1 # CPU-only switch (uncomment to build without GPU support). ![]() # Refer to # Contributions simplifying and improving our build system are welcome! # cuDNN acceleration switch (uncomment to build with cuDNN). root/miniconda3/envs/p圓7/lib/python3.7/site-packages/numpy/core/include/numpy (p圓7 ) # find /root/miniconda3/envs/p圓7/lib/ -name numpy 查找numpy路径 find /root/miniconda3/envs/p圓7/lib/ -name numpy (p圓7 ) # python -c "import distutils.sysconfig as sysconfig print(sysconfig.get_config_var('LIBDIR'))" root/miniconda3/envs/p圓7/include/python3.7m (p圓7 ) # python -c "from distutils.sysconfig import get_python_inc print(get_python_inc())" Python -c "import distutils.sysconfig as sysconfig print(sysconfig.get_config_var('LIBDIR'))" 查找对应路径 python -c "from distutils.sysconfig import get_python_inc print(get_python_inc())" ![]() Git clone caffe仓库 git clone -b 1.0 -depth 1 įor req in $( cat python/requirements.txt ) do pip install $req done cp nfig 添加nvidia-machine-learning软件源 curl -fsSL | apt-key add - & \ echo "deb /" > /etc/apt//cuda.listĪpt install libnccl2 = 2.6.4-1+cuda10.0 libnccl-dev = 2.6.4-1+cuda10.0Īpt-get install -y -no-install-recommends libboost-all-dev 安装依赖包 apt-get -y install libboost-dev libprotobuf-dev libgflags-dev libgoogle-glog-dev libhdf5-dev libopencv-dev protobuf-c-compiler protobuf-compiler libopenblas-dev libhdf5-dev libleveldb-dev liblmdb-dev libboost-system-dev libboost-filesystem-dev libsnappy-dev libboost-thread-dev libatlas-base-dev libboost-python-dev 修改conda源 bash /public/script/switch_conda_source.sh 安装curand apt install cuda-curand-dev-10-0 选用CUDA10.0镜像 添加nvidia-cuda和修改apt源 curl -fsSL | apt-key add - & \ echo "deb /" > /etc/apt//cuda.list & \ bash /public/script/switch_apt_source.sh
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