Installation

Download

The source code is hosted on github.

Prerequisites

To compile (and run) GPUMD one requires an Nvidia GPU card with compute capability no less than 3.5 and CUDA toolkit 9.0 or newer. On Linux systems, one also needs a C++ compiler supporting at least the C++11 standard. On Windows systems, one also needs the cl.exe compiler from Microsoft Visual Studio and a 64-bit version of make.exe.

Compilation

In the src directory run make, which generates two executables, nep and gpumd. Please check the comments in the beginning of the makefile for some compiling options.

Examples

You can find several examples for how to use both the gpumd and nep executables in the examples directory of the GPUMD repository.

NetCDF Setup Instructions

To use NetCDF (see dump_netcdf keyword) with GPUMD, a few extra steps must be taken before building GPUMD. First, you must download and install the correct version of NetCDF. Currently, GPUMD is coded to work with netCDF-C 4.6.3 and it is recommended that this version is used (not newer versions).

The setup instructions are below:

  • Download netCDF-C 4.6.3

  • Configure and build NetCDF. It is best to follow the instructions included with the software but, for the configuration, please use the following flags seen in our example line

    ./configure --prefix=<path> --disable-netcdf-4 --disable-dap

    Here, the --prefix determines the output directory of the build. Then make and install NetCDF:

    make -j && make install

  • Enable the NetCDF functionality. To do this, one must enable the USE_NETCDF flag. In the makefile, this will look as follows:

    CFLAGS = -std=c++14 -O3 $(CUDA_ARCH) -DUSE_NETCDF

    In addition to that line the makefile must also be updated to the following:

    INC = -I<path>/include -I./
LDFLAGS = -L<path>/lib
LIBS = -lcublas -lcusolver -l:libnetcdf.a

    where <path> should be replaced with the installation path for NetCDF (defined in --prefix of the ./configure command).

  • Follow the remaining GPUMD installation instructions

Following these steps will enable the dump_netcdf keyword.

PLUMED Setup Instructions

To use PLUMED (see plumed keyword) with GPUMD, a few extra steps must be taken before building GPUMD. First, you must download and install PLUMED.

The setup instructions are below:

  • Download the latest version of PLUMED, e.g. the plumed-src-2.8.2.tgz tarball.

  • Configure and build PLUMED. It is best to follow the instructions, but for a quick installation, you may use the following setup:

    ./configure --prefix=<path> --disable-mpi --enable-openmp --enable-modules=all

    Here, the --prefix determines the output directory of the build. Then make and install PLUMED:

    make -j6 && make install

    Then update your environment variables (e.g., add the following lines to your bashrc file):

    export PLUMED_KERNEL=<path>/lib/libplumedKernel.so
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<path>/lib
export PATH=$PATH:<path>/bin

    where <path> should be replaced with the installation path for PLUMED (defined in --prefix of the ./configure command). Finally, reopen your shell to apply changes.

  • Enable the PLUMED functionality. To do this, one must enable the USE_PLUMED flag. In the makefile, this will look as follows:

    CFLAGS = -std=c++14 -O3 $(CUDA_ARCH) -DUSE_PLUMED

    In addition to that line the makefile must also be updated to the following:

    INC = -I<path>/include -I./
LDFLAGS = -L<path>/lib -lplumed -lplumedKernel

    where <path> should be replaced with the installation path for PLUMED (defined in --prefix of the ./configure command).

  • Follow the remaining GPUMD installation instructions

Following these steps will enable the plumed keyword.

GPUMD supports DP potential project

0 Program Introduction

This is the beginning of GPUMD’s support for other machine learning potential functions.

0.1 Necessary instructions

  • This is a test version.

  • Only potential function files ending with .pb in deepmd are supported, that is, the potential function files of the tensorflow version generated using dp --tf freeze.

0.2 Installation Dependencies

  • You must ensure that the new version of DP is installed and can run normally. This program contains DP-related dependencies.

  • The installation environment requirements of GPUMD itself must be met.

1 Installation details

Use the instance in AutoDL for testing. If one need testing use AutoDL, please contact Ke Xu (twtdq@qq.com).

And we have created an image in AutoDL that can run GPUMD-DP directly, which can be shared with the account that provides the user ID. Then, you will not require the following process and can be used directly.

2 GPUMD-DP installation (Offline version)

2.0 DP installation (Offline version)

Use the latest version of DP installation steps:

>> $ # Copy data and unzip files.
>> $ cd /root/autodl-tmp/
>> $ wget https://mirror.nju.edu.cn/github-release/deepmodeling/deepmd-kit/v3.0.0/deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.0 -O deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.0
>> $ wget https://mirror.nju.edu.cn/github-release/deepmodeling/deepmd-kit/v3.0.0/deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.1 -O deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.1
>> $ cat deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.0 deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.1 > deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh
>> $ # rm deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.0 deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh.1 # Please use with caution "rm"
>> $ sh deepmd-kit-3.0.0-cuda126-Linux-x86_64.sh -p /root/autodl-tmp/deepmd-kit -u # Just keep pressing Enter/yes.
>> $ source /root/autodl-tmp/deepmd-kit/bin/activate /root/autodl-tmp/deepmd-kit
>> $ dp -h

After running according to the above steps, using dp -h can successfully display no errors.

2.1 GPUMD-DP installation

The GitHub link is Here.

>> $ wget https://codeload.github.com/Kick-H/GPUMD/zip/7af5267f4d8ba720830c154f11634a1942b66b08
>> $ cd ${GPUMD}/src-v0.1

Modify makefile as follows:

  • Line 19 is changed from CUDA_ARCH=-arch=sm_60 to CUDA_ARCH=-arch=sm_89 (for RTX 4090). Modify according to the corresponding graphics card model.

  • Line 25 is changed from INC = -I./ to INC = -I./ -I/root/miniconda3/deepmd-kit/source/build/path_to_install/include/deepmd

  • Line 27 is changed from LIBS = -lcublas -lcusolver to LIBS = -lcublas -lcusolver -L/root/miniconda3/deepmd-kit/source/build/path_to_install/lib -ldeepmd_cc

Then run the following installation command:

>> $ sudo echo "export LD_LIBRARY_PATH=/root/miniconda3/deepmd-kit/source/build/path_to_install/lib:$LD_LIBRARY_PATH" >> /root/.bashrc
>> $ source /root/.bashrc
>> $ make gpumd -j

2.2 Running Tests

>> $ cd /root/miniconda3/GPUMD-bu0/tests/dp
>> $ ../../src/gpumd

3 GPUMD-DP installation (Online version)

3.0 Introduction

This is to use the online method to install the GPUMD-DP version, you need to connect the machine to the Internet and use github and other websites.

3.1 Conda environment

Create a new conda environment with Python and activate it:

>> $ conda create -n tf-gpu2  python=3.9
>> $ conda install -c conda-forge cudatoolkit=11.8
>> $ pip install --upgrade tensorflow

3.3 download deep-kit and install

Download DP source code and compile the source files following DP docs. Here are the cmake commands:

>> $ git clone https://github.com/deepmodeling/deepmd-kit.git
>> $ cd deepmd-kit/source
>> $ mkdir build && cd build
>> $ cmake -DENABLE_TENSORFLOW=TRUE -DUSE_CUDA_TOOLKIT=TRUE -DCMAKE_INSTALL_PREFIX=`path_to_install` -DUSE_TF_PYTHON_LIBS=TRUE ../
>> $ make -j && make install

We just need the DP C++ interface, so we don’t source all DP environment. The libraries will be installed in path_to_install.

3.4 Configure the makefile of GPUMD

The GitHub link is Here.

>> $ wget https://codeload.github.com/Kick-H/GPUMD/zip/7af5267f4d8ba720830c154f11634a1942b66b08
>> $ cd ${GPUMD}/src
>> $ vi makefile

Configure the makefile of GPUMD. The DP code is included by macro definition USE_TENSORFLOW. So add it to CFLAGS:

CFLAGS = -std=c++14 -O3 $(CUDA_ARCH) -DUSE_TENSORFLOW

Then link the DP C++ libraries. Add the following two lines to update the include and link paths and compile GPUMD:

INC += -Ipath_to_install/include/deepmd

LDFLAGS += -Lpath_to_install/lib -ldeepmd_cc

Then, you can install it using the following command:

>> $ make gpumd -j

3.5 Run GPUMD

When running GPUMD, if an error occurs stating that the DP libraries could not be found, add the library path temporarily with:

LD_LIBRARY_PATH=path_to_install/lib:$LD_LIBRARY_PATH

Or add the environment permanently to the ~/.bashrc:

>> $ sudo echo "export LD_LIBRARY_PATH=/root/miniconda3/deepmd-kit/source/build/path_to_install/lib:$LD_LIBRARY_PATH" >> ~/.bashrc
>> $ source ~/.bashrc

3.6 Run Test

This DP interface requires two files: a setting file and a DP potential file. The first file is very simple and is used to inform GPUMD of the atom number and types. For example, the dp.txt is shown in here for use the potential dp.txt DP_POTENTIAL_FILE.pb command in the run.in file:

dp 2 O H

Notice

The type list in the setting file and the potential file must be the same.

Example

References