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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 https://github.com/deepmodeling/deepmd-kit.git deepmd-kit

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

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

cd deepmd-kit
deepmd_source_dir=`pwd`

Install the python interface

Install Tensorflow's python interface

First, check the python version on your machine

python --version

We follow the virtual environment approach to install TensorFlow's Python interface. The full instruction can be found on the official TensorFlow website. TensorFlow 1.8 or later is supported. Now we assume that the Python interface will be installed to the virtual environment directory $tensorflow_venv

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

It is important that every time 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

deactivate

If one has multiple python interpreters named something 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

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.

One can also build the TensorFlow Python interface from source for custom hardware optimization, such as CUDA, ROCM, or OneDNN support.

Install the DeePMD-kit's python interface

Check the compiler version on your machine

gcc --version

The compiler GCC 4.8 or later is supported in the DeePMD-kit. Note that TensorFlow may have specific requirements for the compiler version. It is recommended to use the same compiler version as TensorFlow, which can be printed by python -c "import tensorflow;print(tensorflow.version.COMPILER_VERSION)".

Execute

cd $deepmd_source_dir
pip install .

One may set the following environment variables before executing pip:

Environment variables Allowed value Default value Usage
DP_VARIANT cpu, cuda, rocm cpu Build CPU variant or GPU variant with CUDA or ROCM support.
CUDA_TOOLKIT_ROOT_DIR Path Detected automatically The path to the CUDA toolkit directory. CUDA 7.0 or later is supported. NVCC is required.
ROCM_ROOT Path Detected automatically The path to the ROCM toolkit directory.
TENSORFLOW_ROOT Path Detected automatically The path to TensorFlow Python library. By default the installer only finds TensorFlow under user site-package directory (site.getusersitepackages()) or system site-package directory (sysconfig.get_path("purelib")) due to limitation of PEP-517. If not found, the latest TensorFlow (or the environment variable TENSORFLOW_VERSION if given) from PyPI will be built against.
DP_ENABLE_NATIVE_OPTIMIZATION 0, 1 0 Enable compilation optimization for the native machine's CPU type. Do not enable it if generated code will run on different CPUs.

To test the installation, one should first 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,freeze,test}
    train              train a model
    freeze             freeze the model
    test               test the model

Install horovod and mpi4py

Horovod and mpi4py are used for parallel training. For better performance on GPU, please follow the tuning steps in Horovod on GPU.

# With GPU, prefer NCCL as a communicator.
HOROVOD_WITHOUT_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 HOROVOD_GPU_OPERATIONS=NCCL HOROVOD_NCCL_HOME=/path/to/nccl pip install horovod mpi4py

If your work in a CPU environment, please prepare runtime as below:

# By default, MPI is used as communicator.
HOROVOD_WITHOUT_GLOO=1 HOROVOD_WITH_TENSORFLOW=1 pip install horovod mpi4py

To ensure Horovod has been built with proper framework support enabled, one can invoke the horovodrun --check-build command, e.g.,

$ horovodrun --check-build

Horovod v0.22.1:

Available Frameworks:
    [X] TensorFlow
    [X] PyTorch
    [ ] MXNet

Available Controllers:
    [X] MPI
    [X] Gloo

Available Tensor Operations:
    [X] NCCL
    [ ] DDL
    [ ] CCL
    [X] MPI
    [X] Gloo

From version 2.0.1, Horovod and mpi4py with MPICH support are shipped with the installer.

If you don't install Horovod, DeePMD-kit will fall back to serial mode.

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 Tensorflow's C++ interface

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 >= 4.8. Note that TensorFlow may have specific requirements for the compiler version.

First, the C++ interface of Tensorflow should be installed. It is noted that the version of Tensorflow should be consistent with the python interface. You may follow the instruction or run the script $deepmd_source_dir/source/install/build_tf.py to install the corresponding C++ interface.

Install DeePMD-kit's C++ interface

Now go to the source code directory of DeePMD-kit and make a building 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 TensorFlow's C++ interface is installed.

One may add the following arguments to cmake:

CMake Aurgements Allowed value Default value Usage
-DTENSORFLOW_ROOT=<value> Path - The Path to TensorFlow's C++ interface.
-DCMAKE_INSTALL_PREFIX=<value> Path - The Path where DeePMD-kit will be installed.
-DUSE_CUDA_TOOLKIT=<value> TRUE or FALSE FALSE If TRUE, Build GPU support with CUDA toolkit.
-DCUDA_TOOLKIT_ROOT_DIR=<value> Path Detected automatically The path to the CUDA toolkit directory. CUDA 7.0 or later is supported. NVCC is required.
-DUSE_ROCM_TOOLKIT=<value> TRUE or FALSE FALSE If TRUE, Build GPU support with ROCM toolkit.
-DCMAKE_HIP_COMPILER_ROCM_ROOT=<value> Path Detected automatically The path to the ROCM toolkit directory.
-DLAMMPS_SOURCE_ROOT=<value> Path - Only neccessary for LAMMPS plugin mode. The path to the LAMMPS source code. LAMMPS 8Apr2021 or later is supported. If not assigned, the plugin mode will not be enabled.
-DUSE_TF_PYTHON_LIBS=<value> TRUE or FALSE FALSE If TRUE, Build C++ interface with TensorFlow's Python libraries(TensorFlow's Python Interface is required). And there's no need for building TensorFlow's C++ interface.
-DENABLE_NATIVE_OPTIMIZATION TRUE or FALSE FALSE Enable compilation optimization for the native machine's CPU type. Do not enable it if generated code will run on different CPUs.

If the CMake has been executed successfully, then run the following make commands to build the package:

make -j4
make install

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 have the following executable and libraries installed in $deepmd_root/bin and $deepmd_root/lib

$ ls $deepmd_root/bin
dp_ipi      dp_ipi_low
$ ls $deepmd_root/lib
libdeepmd_cc_low.so  libdeepmd_ipi_low.so  libdeepmd_lmp_low.so  libdeepmd_low.so          libdeepmd_op_cuda.so  libdeepmd_op.so
libdeepmd_cc.so      libdeepmd_ipi.so      libdeepmd_lmp.so      libdeepmd_op_cuda_low.so  libdeepmd_op_low.so   libdeepmd.so