Quick reference

• The official DeePMD-kit docker image.

• Maintained by: openEuler CloudNative SIG.

• Where to get help: openEuler CloudNative SIG, openEuler.

DeePMD-kit | openEuler

DeePMD-kit is an open-source package for deep learning based molecular simulation. It provides a framework for deep potential molecular dynamics (DPMD) and deep potential range correction (DPRc). Learn more at https://github.com/deepmodeling/deepmd-kit.

Supported tags and respective Dockerfile links

The tag of each DeePMD-kit docker image is consist of the version of DeePMD-kit and the version of basic image. The details are as follows

Tags	Currently	Architectures
3.1.3-oe2403sp3	DeePMD-kit 3.1.3 on openEuler 24.03-LTS-SP3	amd64, arm64

Usage

In this usage, users can select the corresponding {Tag} based on their requirements.

Pull the image (example):

docker pull openeuler/deepmd-kit:3.1.3-oe2403sp4
docker pull openeuler/deepmd-kit:3.1.3-oe2403sp3

Run DeePMD-kit container:

docker run -it --rm openeuler/deepmd-kit:3.1.3-oe2403sp2 dp --version

Train a model (example):

docker run -it --rm -v /path/to/data:/data openeuler/deepmd-kit:3.1.3-oe2403sp2 dp train /data/input.json

Using with LAMMPS

DeePMD-kit can be used with LAMMPS for molecular dynamics simulations. Example workflow:

1. Train a deep potential model
2. Freeze the model
3. Use the model in LAMMPS with the pair_style deepmd command

Question and answering

If you have any questions or want to use special features, please submit an issue or a pull request on openeuler-docker-images.