# 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} Note: The off-line packages and conda packages require the [GNU C Library](https://www.gnu.org/software/libc/) 2.17 or above. The GPU version requires [compatible NVIDIA driver](https://docs.nvidia.com/deploy/cuda-compatibility/index.html#minor-version-compatibility) to be installed in advance. It is possible to force conda to [override detection](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-virtual.html#overriding-detected-packages) when installation, but these requirements are still necessary during runtime. You can refer to [DeepModeling conda FAQ](https://docs.deepmodeling.com/faq/conda.html) for more information. ::: :::{note} Python 3.9 or above is required for Python interface. ::: - [Install off-line packages](#install-off-line-packages) - [Install with conda](#install-with-conda) - [Install with docker](#install-with-docker) - [Install Python interface with pip](#install-python-interface-with-pip) ## Install off-line packages Both CPU and GPU version offline packages are available on [the Releases page](https://github.com/deepmodeling/deepmd-kit/releases). Some packages are split into two files due to the size limit of GitHub. One may merge them into one after downloading: ```bash cat deepmd-kit-2.2.9-cuda118-Linux-x86_64.sh.0 deepmd-kit-2.2.9-cuda118-Linux-x86_64.sh.1 > deepmd-kit-2.2.9-cuda118-Linux-x86_64.sh ``` One may enable the environment using ```bash conda activate /path/to/deepmd-kit ``` ## Install with conda DeePMD-kit is available with [conda](https://github.com/conda/conda). Install [Anaconda](https://www.anaconda.com/distribution/#download-section), [Miniconda](https://docs.conda.io/en/latest/miniconda.html), or [miniforge](https://conda-forge.org/download/) first. You can refer to [DeepModeling conda FAQ](https://docs.deepmodeling.com/faq/conda.html) for how to setup a conda environment. ### conda-forge channel DeePMD-kit is available on the [conda-forge](https://conda-forge.org/) channel: ```bash conda create -n deepmd deepmd-kit lammps horovod -c conda-forge ``` The supported platforms include Linux x86-64, macOS x86-64, and macOS arm64. Read [conda-forge FAQ](https://conda-forge.org/docs/user/tipsandtricks.html#installing-cuda-enabled-packages-like-tensorflow-and-pytorch) to learn how to install CUDA-enabled packages. ### Official channel (deprecated) ::::{danger} :::{deprecated} 3.0.0 The official channel has been deprecated since 3.0.0, due to the challenging work of building dependencies for [multiple backends](../backend.md). Old packages will still be available at https://conda.deepmodeling.com. Maintainers will build packages in the conda-forge organization together with other conda-forge members. ::: :::: ## Install with docker A docker for installing the DeePMD-kit is available [here](https://github.com/deepmodeling/deepmd-kit/pkgs/container/deepmd-kit). To pull the CPU version: ```bash docker pull ghcr.io/deepmodeling/deepmd-kit:2.2.8_cpu ``` To pull the GPU version: ```bash docker pull ghcr.io/deepmodeling/deepmd-kit:2.2.8_cuda12.0_gpu ``` ## Install Python interface with pip [Create a new environment](https://docs.deepmodeling.com/faq/conda.html#how-to-create-a-new-conda-pip-environment), and then execute the following command: :::::::{tab-set} ::::::{tab-item} TensorFlow {{ tensorflow_icon }} :::::{tab-set} ::::{tab-item} CUDA 12 ```bash pip install deepmd-kit[gpu,cu12] ``` `cu12` is required only when CUDA Toolkit and cuDNN were not installed. :::: ::::{tab-item} CUDA 11 ```bash pip install deepmd-kit-cu11[gpu,cu11] ``` :::: ::::{tab-item} CPU ```bash pip install deepmd-kit[cpu] ``` :::: ::::: :::::: ::::::{tab-item} PyTorch {{ pytorch_icon }} :::::{tab-set} ::::{tab-item} CUDA 12 ```bash pip install deepmd-kit[torch] ``` :::: ::::{tab-item} CUDA 11.8 ```bash pip install torch --index-url https://download.pytorch.org/whl/cu118 pip install deepmd-kit-cu11 ``` :::: ::::{tab-item} CPU ```bash pip install torch --index-url https://download.pytorch.org/whl/cpu pip install deepmd-kit ``` :::: ::::: :::::: ::::::{tab-item} JAX {{ jax_icon }} :::::{tab-set} ::::{tab-item} CUDA 12 ```bash pip install deepmd-kit[jax] jax[cuda12] ``` :::: ::::{tab-item} CPU ```bash pip install deepmd-kit[jax] ``` :::: ::::: To generate a SavedModel and use [the LAMMPS module](../third-party/lammps-command.md) and [the i-PI driver](../third-party/ipi.md), you need to install the TensorFlow. Switch to the TensorFlow {{ tensorflow_icon }} tab for more information. :::::: ::::::{tab-item} Paddle {{ paddle_icon }} :::::{tab-set} ::::{tab-item} CUDA 12.6 ```bash pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ pip install deepmd-kit ``` :::: ::::{tab-item} CUDA 11.8 ```bash pip install paddlepaddle-gpu==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ pip install deepmd-kit ``` :::: ::::{tab-item} CPU ```bash pip install paddlepaddle==3.0.0 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ pip install deepmd-kit ``` :::: ::::: :::::: ::::::: 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. :::{Warning} If your platform is not supported, or you want to build against the installed backends, or you want to enable ROCM support, please [build from source](install-from-source.md). ::: [The LAMMPS module](../third-party/lammps-command.md) and [the i-PI driver](../third-party/ipi.md) are provided on Linux and macOS for the TensorFlow, PyTorch, and JAX backend. It requires both TensorFlow and PyTorch. To install LAMMPS and/or i-PI, add `lmp` and/or `ipi` to extras: ```bash pip install deepmd-kit[gpu,cu12,lmp,ipi] ``` MPICH is required for parallel running.