1.2. Installation

There are three easy methods to install DeePMD-kit:

  • Install off-line packages

  • Install with conda

  • Install with docker

Users can choose a suitable method depending on the machine environment. The following is a detailed description of these three methods.

1.2.1. Install off-line packages

Users can use the offline packages to install the DeePMD-kit if their machine cannot be connected to the internet. Both CPU and GPU version offline packages are available on the Releases page.

Some GPU version off-line packages are splited into two files due to size limit of GitHub. Users can merge them into one with the cat command and then install the DeePMD-kit software with the bash command.

$ 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
$ bash deepmd-kit-2.0.0-cuda11.3_gpu-Linux-x86_64.sh

During the installation, users need to specify the installation path of the DeePMD-kit. It is assumed that the user chose to install DeePMD-kit at “/root/deepmd-kit”. Then, users need to configure the environment variable.

$ export PATH="/root/deepmd-kit/bin/:$PATH"

Users should remember to configure the environment variable after opening a new terminal. Users can also add the above line into the bashrc file, which is not explained here.

1.2.2. Install with conda

Users can use conda to install the DeePMD-kit if their machine can be connected to the internet. Before installing DeePMD-kit, users need to install Anaconda or Miniconda and activate the conda environment.

Both the CPU and GPU versions of DeePMD-kit can be installed via conda. Users can create an environment that contains the CPU version of DeePMD-kit and LAMMPS.

(base)$ conda create -n deepmd deepmd-kit=*=*cpu libdeepmd=*=*cpu lammps-dp -c https://conda.deepmodeling.org

or create an environment that contains the GPU version of DeePMD-kit and LAMMPS.

(base)$ conda create -n deepmd deepmd-kit=*=*gpu libdeepmd=*=*gpu lammps-dp cudatoolkit=11.3 horovod -c https://conda.deepmodeling.org

The environment also contains the CUDA Toolkit. Users could change the CUDA Toolkit version from 10.1 or 11.3. The latest version of DeePMD-kit will be installed by the above command. Users may want to specify the DeePMD-kit version such as 2.0.0 using

(base)$ 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

Before using the DeePMD-kit, users need to ensure that the environment is active. Users can enable the deepmd environment using

(base)$ conda activate deepmd

When the environment is activated, (base) will be converted to (deepmd) on the left in the terminal. For example,

(deepmd)$

1.2.3. Install with docker

Users can also use docker to install the DeePMD-kit if their machine can be connected to the internet.

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

To pull the ROCm version:

$ docker pull deepmodeling/dpmdkit-rocm:dp2.0.3-rocm4.5.2-tf2.6-lmp29Sep2021

1.2.4. Verify the installation

If the installation is successful, DeePMD-kit (dp) and LAMMPS (lmp) will be available to execute.mpirun is also available considering users may want to train models or run LAMMPS in parallel. To verify the installation, users can execute

$ dp -h

the terminal will show the help information like

usage: dp [-h] [--version]{config,transfer,train,freeze,test,compress,doc-traininput,model-devi,convert-from}

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
...

Note that users can also install the DeePMD-kit software from the source code, but this process is relatively complex. A detailed description is presented in ‘Install from source code’ Section of DeePMD-kit’s documentation.