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 https://github.com/deepmodeling/deepmd-kit.git deepmd-kitFor 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`First, check the Python version on your machine. Python 3.9 or above is required.
python --versionWe follow the virtual environment approach to install the backend's Python interface.
Now we assume that the Python interface will be installed in the virtual environment directory $deepmd_venv:
virtualenv -p python3 $deepmd_venv
source $deepmd_venv/bin/activate
pip install --upgrade pip::::{tab-set}
:::{tab-item} TensorFlow {{ tensorflow_icon }}
The full instruction to install TensorFlow can be found on the official TensorFlow website. TensorFlow 2.7 or later is supported.
pip install --upgrade tensorflowIf 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-cpuOne can also use conda to install TensorFlow from conda-forge.
To verify the installation, run
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"One can also build the TensorFlow Python interface from source for customized hardware optimization, such as CUDA, ROCM, or OneDNN support.
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:::{tab-item} PyTorch {{ pytorch_icon }}
To install PyTorch, run
pip install torchFollow PyTorch documentation to install PyTorch built against different CUDA versions or without CUDA.
One can also use conda to install PyTorch from conda-forge.
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:::{tab-item} JAX {{ jax_icon }}
To install JAX AI Stack, run
pip install jax-ai-stackOne can also install packages in JAX AI Stack manually. Follow JAX documentation to install JAX built against different CUDA versions or without CUDA.
One can also use conda to install JAX from conda-forge.
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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 $deepmd_venv/bin/activateif one wants to skip out of the virtual environment, he/she can do
deactivateIf one has multiple python interpreters named something like python3.x, it can be specified by, for example
virtualenv -p python3.9 $deepmd_venvOne should remember to activate the virtual environment every time he/she uses DeePMD-kit.
Check the compiler version on your machine
gcc --version
By default, DeePMD-kit uses C++ 14, so the compiler needs to support C++ 14 (GCC 5 or later). The backend package may use a higher C++ standard version, and thus require a higher compiler version (for example, GCC 7 for C++ 17).
::::{tab-set}
:::{tab-item} TensorFlow {{ tensorflow_icon }}
Note that TensorFlow may have specific requirements for the compiler version to support the C++ standard version and _GLIBCXX_USE_CXX11_ABI used by TensorFlow. 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)".
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:::{tab-item} PyTorch {{ pytorch_icon }}
You can set the environment variable export DP_ENABLE_PYTORCH=1 to enable customized C++ OPs in the PyTorch backend.
Note that PyTorch may have specific requirements for the compiler version to support the C++ standard version and _GLIBCXX_USE_CXX11_ABI used by PyTorch.
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Execute
cd $deepmd_source_dir
pip install .One may set the following environment variables before executing pip:
:::{envvar} DP_VARIANT
Choices: cpu, cuda, rocm; Default: cpu
Build CPU variant or GPU variant with CUDA or ROCM support. :::
:::{envvar} CUDAToolkit_ROOT
Type: Path; Default: Detected automatically
The path to the CUDA toolkit directory. CUDA 9.0 or later is supported. NVCC is required. :::
:::{envvar} ROCM_ROOT
Type: Path; Default: Detected automatically
The path to the ROCM toolkit directory. If ROCM_ROOT is not set, it will look for ROCM_PATH; if ROCM_PATH is also not set, it will be detected using hipconfig --rocmpath.
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:::{envvar} DP_ENABLE_TENSORFLOW
Choices: 0, 1; Default: 1
{{ tensorflow_icon }} Enable the TensorFlow backend. :::
:::{envvar} DP_ENABLE_PYTORCH
Choices: 0, 1; Default: 0
{{ pytorch_icon }} Enable customized C++ OPs for the PyTorch backend. PyTorch can still run without customized C++ OPs, but features will be limited. :::
:::{envvar} TENSORFLOW_ROOT
Type: Path; Default: Detected automatically
{{ tensorflow_icon }} The path to TensorFlow Python library. If not given, 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.
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:::{envvar} PYTORCH_ROOT
Type: Path; Default: Detected automatically
{{ pytorch_icon }} The path to PyTorch Python library. If not given, by default, the installer only finds PyTorch under the user site-package directory (site.getusersitepackages()) or the system site-package directory (sysconfig.get_path("purelib")) due to the limitation of PEP-517. If not found, the latest PyTorch (or the environment variable PYTORCH_VERSION if given) from PyPI will be built against.
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:::{envvar} DP_ENABLE_NATIVE_OPTIMIZATION
Choices: 0, 1; Default: 0
Enable compilation optimization for the native machine's CPU type. Do not enable it if generated code will run on different CPUs. :::
:::{envvar} CMAKE_ARGS
Type: string
Additional CMake arguments. :::
:::{envvar} FLAGS
<LANG>=CXX, CUDA or HIP
Type: string
Default compilation flags to be used when compiling <LANG> files. See CMake documentation for details.
:::
Other CMake environment variables may also be critical.
To test the installation, one should first jump out of the source directory
cd /some/other/workspace
then execute
dp -hIt will print the help information like
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 mpi4pyIf 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 mpi4pyTo 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] GlooSince 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.
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.
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:::{tab-item} TensorFlow {{ tensorflow_icon }} / JAX {{ jax_icon }}
The C++ interfaces of both TensorFlow and JAX backends are based on the TensorFlow C++ library.
Since TensorFlow 2.12, TensorFlow C++ library (libtensorflow_cc) is packaged inside the Python library. Thus, you can skip building TensorFlow C++ library manually. If that does not work for you, you can still build it manually.
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.
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:::{tab-item} PyTorch {{ pytorch_icon }}
If you have installed PyTorch using pip, you can use libtorch inside the PyTorch Python package. You can also download libtorch prebuilt library from the PyTorch website.
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:::{tab-item} JAX {{ jax_icon }}
The JAX backend only depends on the TensorFlow C API, which is included in both TensorFlow C++ library and TensorFlow C library. If you want to use the TensorFlow C++ library, just enable the TensorFlow backend (which depends on the TensorFlow C++ library) and nothing else needs to do. If you want to use the TensorFlow C library and disable the TensorFlow backend, download the TensorFlow C library from this page.
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Now go to the source code directory of DeePMD-kit and make a building place.
cd $deepmd_source_dir/source
mkdir build
cd buildThe installation requires CMake 3.16 or later for the CPU version, CMake 3.23 or later for the CUDA support, and CMake 3.21 or later for the ROCM support. One can install CMake via pip if it is not installed or the installed version does not satisfy the requirement:
pip install -U cmakeYou must enable at least one backend.
If you enable two or more backends, these backend libraries must be built in a compatible way, e.g. using the same _GLIBCXX_USE_CXX11_ABI flag.
We recommend using conda packages from conda-forge, which are usually compatible to each other.
::::{tab-set}
:::{tab-item} TensorFlow {{ tensorflow_icon }} / JAX {{ jax_icon }}
I assume you have activated the TensorFlow Python environment and want to install DeePMD-kit into path $deepmd_root, then execute CMake
cmake -DENABLE_TENSORFLOW=TRUE -DUSE_TF_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$deepmd_root ..If you specify -DUSE_TF_PYTHON_LIBS=FALSE, you need to give the location where TensorFlow's C++ interface is installed to -DTENSORFLOW_ROOT=${tensorflow_root}.
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:::{tab-item} PyTorch {{ pytorch_icon }}
I assume you have installed the PyTorch (either Python or C++ interface) to $torch_root, then execute CMake
cmake -DENABLE_PYTORCH=TRUE -DCMAKE_PREFIX_PATH=$torch_root -DCMAKE_INSTALL_PREFIX=$deepmd_root ..You can specify -DUSE_PT_PYTHON_LIBS=TRUE to use libtorch from the Python installation,
but you need to be careful that PyTorch PyPI packages are still built using _GLIBCXX_USE_CXX11_ABI=0, which may be not compatible with other libraries.
cmake -DENABLE_PYTORCH=TRUE -DUSE_PT_PYTHON_LIBS=TRUE -DCMAKE_INSTALL_PREFIX=$deepmd_root ..:::
:::{tab-item} JAX {{ jax_icon }}
If you want to use the TensorFlow C++ library, just enable the TensorFlow backend and nothing else needs to do.
If you want to use the TensorFlow C library and disable the TensorFlow backend, set {cmake:variable}ENABLE_JAX to ON and CMAKE_PREFIX_PATH to the root directory of the TensorFlow C library.
cmake -DENABLE_JAX=ON -D CMAKE_PREFIX_PATH=${tensorflow_c_root} ..:::
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One may add the following CMake variables to cmake using the -D <var>=<value> option:
:::{cmake:variable} ENABLE_TENSORFLOW
Type: BOOL (ON/OFF), Default: OFF
{{ tensorflow_icon }} {{ jax_icon }} Whether building the TensorFlow backend and the JAX backend.
Setting this option to ON will also set {cmake:variable}ENABLE_JAX to ON.
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:::{cmake:variable} ENABLE_PYTORCH
Type: BOOL (ON/OFF), Default: OFF
{{ pytorch_icon }} Whether building the PyTorch backend.
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:::{cmake:variable} ENABLE_JAX
Type: BOOL (ON/OFF), Default: OFF
{{ jax_icon }} Build the JAX backend.
If {cmake:variable}ENABLE_TENSORFLOW is ON, the TensorFlow C++ library is used to build the JAX backend;
If {cmake:variable}ENABLE_TENSORFLOW is OFF, the TensorFlow C library is used to build the JAX backend.
:::
:::{cmake:variable} TENSORFLOW_ROOT
Type: PATH
{{ tensorflow_icon }} {{ jax_icon }} The Path to TensorFlow's C++ interface.
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:::{cmake:variable} CMAKE_INSTALL_PREFIX
Type: PATH
The Path where DeePMD-kit will be installed. See also CMake documentation.
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:::{cmake:variable} USE_CUDA_TOOLKIT
Type: BOOL (ON/OFF), Default: OFF
If TRUE, Build GPU support with CUDA toolkit.
:::
:::{cmake:variable} CUDAToolkit_ROOT
Type: PATH, Default: Search automatically
The path to the CUDA toolkit directory. CUDA 9.0 or later is supported. NVCC is required. See also CMake documentation.
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:::{cmake:variable} USE_ROCM_TOOLKIT
Type: BOOL (ON/OFF), Default: OFF
If TRUE, Build GPU support with ROCM toolkit.
:::
:::{cmake:variable} CMAKE_HIP_COMPILER_ROCM_ROOT
Type: PATH, Default: Search automatically
The path to the ROCM toolkit directory. See also ROCm documentation.
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:::{cmake:variable} LAMMPS_SOURCE_ROOT
Type: PATH
Only necessary for using 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.
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:::{cmake:variable} USE_TF_PYTHON_LIBS
Type: BOOL (ON/OFF), Default: OFF
{{ tensorflow_icon }} If TRUE, Build C++ interface with TensorFlow's Python libraries (TensorFlow's Python Interface is required).
There's no need for building TensorFlow's C++ interface.
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:::{cmake:variable} USE_PT_PYTHON_LIBS
Type: BOOL (ON/OFF), Default: OFF
{{ pytorch_icon }} If TRUE, Build C++ interface with PyTorch's Python libraries (PyTorch's Python Interface is required).
There's no need for downloading PyTorch's C++ libraries.
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:::{cmake:variable} ENABLE_NATIVE_OPTIMIZATION
Type: BOOL (ON/OFF), Default: OFF
Enable compilation optimization for the native machine's CPU type. Do not enable it if generated code will run on different CPUs.
:::
:::{cmake:variable} CMAKE__FLAGS
(<LANG>=CXX, CUDA or HIP)
Type: STRING
Default compilation flags to be used when compiling <LANG> files.
See also CMake documentation.
:::
If the CMake has been executed successfully, then run the following make commands to build the package:
make -j4
make installOption -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 executable and libraries installed in $deepmd_root/bin and $deepmd_root/lib
$ ls $deepmd_root/bin
$ ls $deepmd_root/lib