- #Caffe anaconda distribution install#
- #Caffe anaconda distribution software#
- #Caffe anaconda distribution code#
#Caffe anaconda distribution code#
# For CUDA < 6.0, comment the *_50 lines for compatibility.CUDA_ARCH :=-gencode arch = compute_50,code=sm_50\-gencode arch= com pute _50,code=compute_50\-gencode arch = compute_50,code=sm_53 \-gencode arch = compute _53,code= compute_53\-gen code arch =compute_50,code=sm_60\ -gen code arch = compute _ 60 ,code = compute _60 # BLAS choice: # atlas for ATLAS (default)# mkl for MKL # open for OpenBlasBLAS := atlas # Custom (MKL/ATLAS/OpenBLAS) include and lib directories.
#Caffe anaconda distribution install#
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. #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 # OPENCV_VERSION := 3 #To customize your choice of compiler, uncomment and set the following. #USE_CUDNN := 1 #CPU-only switch (uncomment to build without GPU support).
berkeley /installation.html #Contributions simplifying and improving our build system are welcome! #cuDNN acceleration switch (uncomment to build with cuDNN). Shin for help! Thanks Ross Girshick for sharing the code. The demo performs detection using a VGG16 network trained for detection on PASCAL VOC 2007. data /scripts /fetch_ faster_ rcnn_ models.sh. Pip install scikit-image google protobuf easydict opencv-python pyyamlcd $FRCN_ ROOT /. nfs/01/cwr0463/.Cp-rdistribute /python /caffe /home /zongwei /miniconda3 /envs /py-faster-rcnn /lib / python 2.7 /site-packages cp distribute /lib /lib*~ /miniconda3 /envs /py-faster-rcnn /lib To do so, we make a set of directories from our home directory of the format (where my username is cwr0463): We can add these to a common module file so that they’re loaded all the time at user login (since we’ll not be using this cluster for anything else). Looking at the list, we see can already see some packages that we’ll need to load up to meet the Caffe dependencies. Firstly, we need to see what things are already installed: We’ll leverage that package, and create our own module file, for our dependencies.
#Caffe anaconda distribution software#
Its quite handy as a way to manage different versions of the same software and to keep the associated BASH variables sane. Ruby supports the Module package for dynamic load and unloading of resources. Since the cluster has only recently been setup, there was some leg work required on our end to get Caffe fully up and running, without root access, which we’ll document here. In this case, since our digital histology deep learning work requires a large number of GPUs to analyze thousands of patients, we were granted access to the OSC Ruby cluster, which has 20 NVIDIA Tesla K40 GPUs. One of the perks of working at Case Western Reserve is that we often qualify for access to cutting edge resource and special projects.
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