Unet xxx


20-Nov-2019 04:41

Not even dockerhub nvidia/cuda has an image for Ubuntu 18.04 and cuda 9.0.Maybe you should upgrade to at least cuda 9.2 or better 10 with corresponding cu DNN 7, but this will probably break your tensorflow installation. If you have permissions to do so, you can also run the caffe_unet server in nvidia-docker (this is how I usually install it).I cannot test it right now, because I cannot freshly clone caffe (Internal Server error on github), but will try later.

Unet xxx-83

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Our build uses the following sequence of commands: apt-get update apt-get install -y sudo wget git build-essential cmake libboost-system-dev libboost-thread-dev libboost-filesystem-dev libprotobuf-dev protobuf-compiler libhdf5-serial-dev libatlas-base-dev libgoogle-glog-dev python3-dev python3-numpy libboost-python-dev git clone https://github.com/BVLC/cd caffe git checkout 99bd99795dcdf0b1d3086a8d67ab1782a8a08383 wget https://lmb.informatik.uni-freiburg.de/lmbsoft/unet/caffe_unet_99bd99_20190109.patch git apply caffe_unet_99bd99_20190109.patch mkdir x86_64 cd x86_64 cmake -DCMAKE_BUILD_TYPE=Release -DCMAKE_CXX_FLAGS_RELEASE="-O2 -DNDEBUG" -DCMAKE_C_FLAGS_RELEASE="-O2 -DNDEBUG" -DCPU_ONLY=OFF -DUSE_OPENMP=ON -DCMAKE_INSTALL_PREFIX=/home/daveb/unet-seg -DUSE_OPENCV=OFF -DUSE_LEVELDB=OFF -DUSE_LMDB=OFF -DUSE_NCCL=OFF -DBUILD_python=ON -DBUILD_python_layer=ON -Dpython_version=3 -DCUDA_ARCH_NAME=Manual -DCUDA_ARCH_BIN="30 35 50 60 61 62" -DCUDA_ARCH_PTX="30" -DUSE_CUDNN=ON ..It should behave identical and hopefully produce a little more output.