![]() Now it’s easy to try the R-CNN example from Ross Girshick : cd ~/technologiesĮxport CFLAGS = -I/usr/local/lib/python2.7/site-packages/numpy/core/include/ ![]() You can then have fun with DIGITS as we did on Ubuntu : download the MNIST dataset python tools/download_data/main.py mnist ~/data/mnist and train a first classifier on your GPU. Download DIGITS export CUDA_HOME =/usr/local/cuda #python for req in $( cat python/requirements.txt ) do pip install $req doneĮxport PYTHONPATH =~/technologies/caffe/python/: $PYTHONPATH cd. Build export DYLD_LIBRARY_PATH =/usr/local/cuda/lib If your iMac is not CUDA capable, comment USE_CUDNN := 1, CUDA_DIR := /usr/local/cuda and CUDA_ARCH=. Q ?= the Python lib directory /usr/local/Cellar/python/2.7.10_2/Frameworks/amework/Versions/2.7/lib. LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include PYTHON_LIB = $( shell brew -prefix numpy )/lib PYTHON_INCLUDE = $( dir $( shell python -c 'import re print(re._file_ ) ' ))/include PYTHON_LIB := /usr/local/Cellar/python/2.7.10_2/Frameworks/amework/Versions/2.7/lib /usr/lib usr/lib/python2.7/dist-packages/numpy/core/include PYTHON_INCLUDE := /usr/include/python2.7 \ Create the configuration file nfig USE_CUDNN := 1ĬUDA_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_50,code =compute_50
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