1.1. Easy install
There various easy methods to install DeePMD-kit. Choose one that you prefer. If you want to build by yourself, jump to the next two sections.
After your easy installation, DeePMD-kit (
dp) and LAMMPS (
lmp) will be available to execute. You can try
dp -h and
lmp -h to see the help.
mpirun is also available considering you may want to train models or run LAMMPS in parallel.
1.1.1. Install off-line packages
Both CPU and GPU version offline packages are available in the Releases page.
Some packages are splited into two files due to size limit of GitHub. One may merge them into one after downloading:
cat deepmd-kit-2.0.0-cuda11.3_gpu-Linux-x86_64.sh.0 deepmd-kit-2.0.0-cuda11.3_gpu-Linux-x86_64.sh.1 > deepmd-kit-2.0.0-cuda11.3_gpu-Linux-x86_64.sh
1.1.2. Install with conda
One may create an environment that contains the CPU version of DeePMD-kit and LAMMPS:
conda create -n deepmd deepmd-kit=*=*cpu libdeepmd=*=*cpu lammps-dp -c https://conda.deepmodeling.org
Or one may want to create a GPU environment containing CUDA Toolkit:
conda create -n deepmd deepmd-kit=*=*gpu libdeepmd=*=*gpu lammps-dp cudatoolkit=11.3 horovod -c https://conda.deepmodeling.org
One could change the CUDA Toolkit version from
One may speficy the DeePMD-kit version such as
conda create -n deepmd deepmd-kit=2.0.0=*cpu libdeepmd=2.0.0=*cpu lammps-dp=2.0.0 horovod -c https://conda.deepmodeling.org
One may enable the environment using
conda activate deepmd
1.1.3. Install with docker
A docker for installing the DeePMD-kit is available here.
To pull the CPU version:
docker pull ghcr.io/deepmodeling/deepmd-kit:2.0.0_cpu
To pull the GPU version:
docker pull ghcr.io/deepmodeling/deepmd-kit:2.0.0_cuda10.1_gpu