Setup CUDA and driver
Follow instructions closely, using package manager method
http://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html
Installing EMAN2 on Ubuntu 16.04 X86_64
Install anaconda2 from their website.
Open a bash shell (at the moment activation and deactivation of conda environments does not work on cshell).
Install EMAN2 using conda in its own environment. At this time I used np113* version. The steps below are adapted and modified from the install guide.
mkdir src
cd src
git clone https://github.com/cryoem/eman2.git #download EMAN2 source
# install dependencies
conda remove cmake bzip2 expat jsoncpp ncurses #just to make sure cmake is not broken, will be reinstalled with cmake
conda create -n eman113 cmake=3.8 -c conda-forge
conda install -n eman113 eman-deps=”*”=”np113*” -c cryoem -c defaults -c conda-forge
source activate eman113
#compile and install EMAN2
cd build
cmake ../ # on linux, also add -DENABLE_OPTIMIZE_MACHINE=ON
make -j
Test to make sure compile went fine
make test
make test-verbose
Enabling GPU in Theano
Install these optional packages for better performance of neural net particle picker. These are used by Theano. (If the steps below fail, consult the Theano page)
source activate eman113
conda install mkl-service pygpu=0.6.5 nose sphinx pydot-ng
pip install pycuda
# currently the latest dev version of scikit-cuda is needed
pip install git+https://github.com/lebedov/scikit-cuda.git#egg=scikit-cuda
define LD_LIBRARY_PATH
vim ~/.bashrc
and add this line to the file
export LD_LIBRARY_PATH=”/usr/local/cuda/lib64″
create .theanorc file in the home directory
vim ~/.theanorc
and add these lines
[global]
device = gpu
floatX = float32
[cuda]root=/usr/local/cuda/
#setting DEVICE to gpu instead of cuda, forces the use of pygpu instead of gpuarray,
Full functionality of theano (and successful testing of libgpuarray (pygpu) requires the installation of NCCL (network installer) and CUDNN. Download Deb packages from nvidia developer website and install:
sudo dpkg -i /data/reza/nvidia-machine-learning-repo-ubuntu1604_1.0.0-1_amd64 #this is the network installer
sudo dpkg -i libcudnn5_5.1.10-1+cuda8.0_amd64.deb #stable theano version uses cudnn5.1
sudo dpkg -i libcudnn5-dev_5.1.10-1+cuda8.0_amd64.deb #stable theano version uses cudnn 5.1
sudo apt-get update
sudo apt-get install libnccl-dev
Test the environment
#simple check to see everything is there
python -c “import theano”
Using cuDNN version 5100 on context None
Mapped name None to device cuda: Quadro M6000 (0000:82:00.0)
#full test of pygpu, you can “pip install mpi4py” to have the full test set done, but it is not required
DEVICE=”cuda” python -c “import pygpu; pygpu.test()”
Ran 7301 tests in 159.932s
Use EMAN2
Note that you will need to run this once in each shell before being able to run EMAN2 commands:
source activate eman113
this will switch to the conda environment where all EMAN2 dependencies are configured.
Cold Room Times 