Environment settings

The enviroment of rid-kit software is a bit complex, it uses dflow to manage the workflow in kubernetes enviroment refered to as management environment. The actual rid-kit code runs on local or remote (slurm, cloud) enviroment refered to as computation environment. The settings of the management enviroment has already been described in Tutorial, only settings of computation enviroment is given as follows.

Configuration of computation environment

Note:

If you want to set environments by hand, please follow settings of this section.

You can also set computation environment of rid through docker, just run “docker pull pkufjhdocker/rid-kit:latest” to get the docker image we built. Or build the image through the dockerfile in rid-kit/install directory.

Install tensorflow’s C++ interface and other necessary packages

We recommend using conda to manage the python enviroment. Use the command

conda create -n rid-drop python=3.9 libtensorflow_cc=2.6.2=*cuda110* tensorflow=2.6.2=*cuda110* nccl mdtraj numpy scikit-learn cmake -c conda-forge

to get the compiled libtensorflow_cc and other necessary packages. After installation, activate the enviroment and set library path

conda activate rid-drop
export LIBRARY_PATH=${CONDA_PREFIX}/lib:$LIBRARY_PATH
export LD_LIBRARY_PATH=${CONDA_PREFIX}/lib:$LD_LIBRARY_PATH

Install plumed2.8.0

You need to copy compiled DeePFE.cpp to the plumed directory. This file locates at rid-kit/install/DeePFE.cpp. Note that here we use c++ 14 standard to compile, the gcc version need to be newer than 6.1.0. In slurm enviroment, if the software is managed by Enviroment Module, one can load gcc such as module load gcc/7.2.0 or other available versions.

set CC and CXX enviroment variables

export CC=/yourpath/to/gcc-7.2.0
export CXX=/yourpath/to/g++-7.2.0

tar -xvzf plumed-2.8.0.tgz
cp DeePFE.cpp plumed-2.8.0/src/bias
cd plumed-2.8.0
./configure --prefix=$CONDA_PREFIX \
                   CXXFLAGS="-std=gnu++14 -I $CONDA_PREFIX/include/" \
                   LDFLAGS=" -L$CONDA_PREFIX/lib -ltensorflow_cc -ltensorflow_framework -Wl,-rpath=$CONDA_PREFIX/lib/" \
make -j 6
make install

Set the enviroment variables

export PLUMED_KERNEL=${CONDA_PREFIX}/lib/libplumedKernel.@SOEXT@

Install gromacs 2021.4

Note that in order to compile gromacs with GPU support, you need to have CUDA toolkit installed. In slurm enviroment, if the software is managed by Enviroment Module, one can load CUDA toolkits such as module load cuda/11.1. Also note that in most slurm environment, there is no network connection at computing node, but if you want to compile gromacs without fftw installed, you will have to compile it on login-in node where network connection is on.

tar -xzvf gromacs-2021.4.tar.gz
cd gromacs-2021.4
plumed patch -p  # make sure you use the correct plumed version
mkdir build
cd build
cmake .. -DGMX_BUILD_OWN_FFTW=ON \
                -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX \
                -DGMX_GPU=CUDA  \
                -DGMX_SIMD=avx_512
make -j 4
make install

Set the enviroment variables

source ${CONDA_PREFIX}/bin/GMXRC

Install Lammps-stable_23Jun2022_update1

tar -xzvf stable_23Jun2022_update1.tar.gz
cd lammps-stable_23Jun2022_update1/src

make packages you will use

make yes-extra-fix
make yes-extra-dump
make yes-kspace

make plumed

make lib-plumed args="-p $CONDA_PREFIX -m runtime"
make yes-plumed
make serial -j4 #build serial version of lammps
ln -s /home/dongdong/software/lammps-stable_23Jun2022_update1/src/lmp_serial /home/dongdong/software/anaconda3/envs/rid_lmp/bin/lmp_serial