Easy installation methods

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 run LAMMPS in parallel.

Install off-line packages

Both CPU and GPU version offline packages are avaiable in the Releases page.

Install with conda

DeePMD-kit is avaiable with conda. Install Anaconda or Miniconda first.

To install the CPU version:

conda install deepmd-kit=*=*cpu lammps-dp=*=*cpu -c deepmodeling

To install the GPU version containing CUDA 10.1:

conda install deepmd-kit=*=*gpu lammps-dp=*=*gpu -c deepmodeling

Install with docker

A docker for installing the DeePMD-kit is available here.

To pull the CPU version:

docker pull

To pull the GPU version:

docker pull

Install from source code

Please follow our github webpage to download the latest released version and development version.

Or get the DeePMD-kit source code by git clone

cd /some/workspace
git clone --recursive deepmd-kit

The --recursive option clones all submodules needed by DeePMD-kit.

For convenience, you may want to record the location of source to a variable, saying deepmd_source_dir by

cd deepmd-kit

Install the python interface

Install the Tensorflow’s python interface

First, check the python version on your machine

python --version

We follow the virtual environment approach to install the tensorflow’s Python interface. The full instruction can be found on the tensorflow’s official website. Now we assume that the Python interface will be installed to virtual environment directory $tensorflow_venv

virtualenv -p python3 $tensorflow_venv
source $tensorflow_venv/bin/activate
pip install --upgrade pip
pip install --upgrade tensorflow==2.3.0

It is notice that everytime a new shell is started and one wants to use DeePMD-kit, the virtual environment should be activated by

source $tensorflow_venv/bin/activate

if one wants to skip out of the virtual environment, he/she can do


If one has multiple python interpreters named like python3.x, it can be specified by, for example

virtualenv -p python3.7 $tensorflow_venv

If one does not need the GPU support of deepmd-kit and is concerned about package size, the CPU-only version of tensorflow should be installed by

pip install --upgrade tensorflow-cpu==2.3.0	

To verify the installation, run

python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

One should remember to activate the virtual environment every time he/she uses deepmd-kit.

Install the DeePMD-kit’s python interface


cd $deepmd_source_dir
pip install .

To test the installation, one should firstly jump out of the source directory

cd /some/other/workspace

then execute

dp -h

It will print the help information like

usage: dp [-h] {train,freeze,test} ...

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

optional arguments:
  -h, --help           show this help message and exit

Valid subcommands:
    train              train a model
    freeze             freeze the model
    test               test the model

Install the C++ interface

If one does not need to use DeePMD-kit with Lammps or I-Pi, then the python interface installed in the previous section does everything and he/she can safely skip this section.

Install the Tensorflow’s C++ interface

Check the compiler version on your machine

gcc --version

The C++ interface of DeePMD-kit was tested with compiler gcc >= 4.8. It is noticed that the I-Pi support is only compiled with gcc >= 4.9.

First the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be in consistent with the python interface. You may follow the instruction to install the corresponding C++ interface.

Install the DeePMD-kit’s C++ interface

Now goto the source code directory of DeePMD-kit and make a build place.

cd $deepmd_source_dir/source
mkdir build 
cd build

I assume you want to install DeePMD-kit into path $deepmd_root, then execute cmake

cmake -DTENSORFLOW_ROOT=$tensorflow_root -DCMAKE_INSTALL_PREFIX=$deepmd_root ..

where the variable tensorflow_root stores the location where the tensorflow’s C++ interface is installed. The DeePMD-kit will automatically detect if a CUDA tool-kit is available on your machine and build the GPU support accordingly. If you want to force the cmake to find CUDA tool-kit, you can speicify the key USE_CUDA_TOOLKIT,

cmake -DUSE_CUDA_TOOLKIT=true -DTENSORFLOW_ROOT=$tensorflow_root -DCMAKE_INSTALL_PREFIX=$deepmd_root ..

and you may further asked to provide CUDA_TOOLKIT_ROOT_DIR. If the cmake has executed successfully, then

make install

If everything works fine, you will have the following executable and libraries installed in $deepmd_root/bin and $deepmd_root/lib

$ ls $deepmd_root/bin
$ ls $deepmd_root/lib

Install LAMMPS’s DeePMD-kit module

DeePMD-kit provide module for running MD simulation with LAMMPS. Now make the DeePMD-kit module for LAMMPS.

cd $deepmd_source_dir/source/build
make lammps

DeePMD-kit will generate a module called USER-DEEPMD in the build directory. Now download the LAMMPS code (29Oct2020 or later), and uncompress it:

cd /some/workspace
tar xf stable_29Oct2020.tar.gz

The source code of LAMMPS is stored in directory lammps-stable_29Oct2020. Now go into the LAMMPS code and copy the DeePMD-kit module like this

cd lammps-stable_29Oct2020/src/
cp -r $deepmd_source_dir/source/build/USER-DEEPMD .

Now build LAMMPS

make yes-kspace
make yes-user-deepmd
make mpi -j4

The option -j4 means using 4 processes in parallel. You may want to use a different number according to your hardware.

If everything works fine, you will end up with an executable lmp_mpi.

./lmp_mpi -h

The DeePMD-kit module can be removed from LAMMPS source code by

make no-user-deepmd

Install i-PI

The i-PI works in a client-server model. The i-PI provides the server for integrating the replica positions of atoms, while the DeePMD-kit provides a client named dp_ipi that computes the interactions (including energy, force and virial). The server and client communicates via the Unix domain socket or the Internet socket. A full instruction of i-PI can be found here. The source code and a complete installation instructions of i-PI can be found here. To use i-PI with already existing drivers, install and update using Pip:

pip install -U i-PI

Test with Pytest:

pip install pytest
pytest --pyargs ipi.tests