1. Easy install

There are 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.


Note: The off-line packages and conda packages require the GNU C Library 2.17 or above. The GPU version requires compatible NVIDIA driver to be installed in advance. It is possible to force conda to override detection when installation, but these requirements are still necessary during runtime.

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-2.1.1-cuda11.6_gpu-Linux-x86_64.sh.0 deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh.1 > deepmd-kit-2.1.1-cuda11.6_gpu-Linux-x86_64.sh

One may enable the environment using

conda activate /path/to/deepmd-kit

1.2. Install with conda

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

1.2.1. Official channel

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 -c https://conda.deepmodeling.com -c defaults

Or one may want to create a GPU environment containing CUDA Toolkit:

conda create -n deepmd deepmd-kit=*=*gpu libdeepmd=*=*gpu lammps cudatoolkit=11.6 horovod -c https://conda.deepmodeling.com -c defaults

One could change the CUDA Toolkit version from 10.2 or 11.6.

One may specify the DeePMD-kit version such as 2.1.1 using

conda create -n deepmd deepmd-kit=2.1.1=*cpu libdeepmd=2.1.1=*cpu lammps horovod -c https://conda.deepmodeling.com -c defaults

One may enable the environment using

conda activate deepmd

1.2.2. conda-forge channel

DeePMD-kit is also available on the conda-forge channel:

conda create -n deepmd deepmd-kit lammps horovod -c conda-forge

The supported platform includes Linux x86-64, macOS x86-64, and macOS arm64. Read conda-forge FAQ to learn how to install CUDA-enabled packages.

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:2.1.1_cpu

To pull the GPU version:

docker pull ghcr.io/deepmodeling/deepmd-kit:2.1.1_cuda11.6_gpu

To pull the ROCm version:

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

1.4. Install Python interface with pip

If you have no existing TensorFlow installed, you can use pip to install the pre-built package of the Python interface with CUDA 12 supported:

pip install deepmd-kit[gpu,cu12]

cu12 is required only when CUDA Toolkit and cuDNN were not installed.

To install the package built against CUDA 11.8, use

pip install deepmd-kit-cu11[gpu,cu11]

Or install the CPU version without CUDA supported:

pip install deepmd-kit[cpu]

The LAMMPS module and the i-Pi driver are only provided on Linux and macOS. To install LAMMPS and/or i-Pi, add lmp and/or ipi to extras:

pip install deepmd-kit[gpu,cu12,lmp,ipi]

MPICH is required for parallel running. (The macOS arm64 package doesn’t support MPI yet.)

It is suggested to install the package into an isolated environment. The supported platform includes Linux x86-64 and aarch64 with GNU C Library 2.28 or above, macOS x86-64 and arm64, and Windows x86-64. A specific version of TensorFlow which is compatible with DeePMD-kit will be also installed.


If your platform is not supported, or want to build against the installed TensorFlow, or want to enable ROCM support, please build from source.