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<span style="font-size:larger;">DeePMD-kit Manual</span>
========
[![GitHub release](https://img.shields.io/github/release/deepmodeling/deepmd-kit.svg?maxAge=86400)](https://github.com/deepmodeling/deepmd-kit/releases)
[![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2020.107206)
![Citations](https://citations.njzjz.win/10.1016/j.cpc.2018.03.016)
[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)](https://github.com/deepmodeling/deepmd-kit/releases)
[![conda install](https://img.shields.io/badge/downloads-9k%20total-green.svg?style=round-square&label=conda%20install)](https://anaconda.org/deepmodeling/deepmd-kit)
[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit)
[![docker pull](https://img.shields.io/docker/pulls/deepmodeling/deepmd-kit)](https://hub.docker.com/r/deepmodeling/deepmd-kit)
[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)](https://deepmd.readthedocs.io/)
# Table of contents
- [About DeePMD-kit](#about-deepmd-kit)
- [Highlights in v2.0](#highlights-in-deepmd-kit-v2.0)
- [Highlighted features](#highlighted-features)
- [License and credits](#license-and-credits)
- [Deep Potential in a nutshell](#deep-potential-in-a-nutshell)
- [Download and install](#download-and-install)
- [Use DeePMD-kit](#use-deepmd-kit)
- [Code structure](#code-structure)
- [Troubleshooting](#troubleshooting)
# About DeePMD-kit
DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems.
For more information, check the [documentation](https://deepmd.readthedocs.io/).
# Highlights in DeePMD-kit v2.0
* [Model compression](doc/freeze/compress.md). Accelerate the efficiency of model inference for 4-15 times.
* [New descriptors](doc/model/overall.md). Including [`se_e2_r`](doc/model/train-se-e2-r.md) and [`se_e3`](doc/model/train-se-e3.md).
* [Hybridization of descriptors](doc/model/train-hybrid.md). Hybrid descriptor constructed from concatenation of several descriptors.
* [Atom type embedding](doc/model/train-se-e2-a-tebd.md). Enable atom type embedding to decline training complexity and refine performance.
* Training and inference the dipole (vector) and polarizability (matrix).
* Split of training and validation dataset.
* Optimized training on GPUs.
## Highlighted features
* **interfaced with TensorFlow**, one of the most popular deep learning frameworks, making the training process highly automatic and efficient, in addition Tensorboard can be used to visualize training procedure.
* **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, i.e., LAMMPS and i-PI, respectively.
* **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc.
* **implements MPI and GPU supports**, makes it highly efficient for high performance parallel and distributed computing.
* **highly modularized**, easy to adapt to different descriptors for deep learning based potential energy models.
## License and credits
The project DeePMD-kit is licensed under [GNU LGPLv3.0](./LICENSE).
If you use this code in any future publications, please cite this using
``Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184.``
## Deep Potential in a nutshell
The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry preserving way. These local coordinates are then transformed, through a sub-network, to a so-called *atomic energy*. Summing up all the atomic energies gives the potential energy of the system.
The initial proof of concept is in the [Deep Potential][1] paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical *ab initio* molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics ([DeePMD][2]) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations.
Although being highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition ([DeepPot-SE][3]) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interests in the fields of physics, chemistry, biology, and materials science.
In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the [DeePCG paper][4] for more details.
# Download and install
Please follow our [GitHub](https://github.com/deepmodeling/deepmd-kit) webpage to download the [latest released version](https://github.com/deepmodeling/deepmd-kit/tree/master) and [development version](https://github.com/deepmodeling/deepmd-kit/tree/devel).
DeePMD-kit offers multiple installation methods. It is recommend using easily methods like [offline packages](doc/install/easy-install.md#offline-packages), [conda](doc/install/easy-install.md#with-conda) and [docker](doc/install/easy-install.md#with-docker).
One may manually install DeePMD-kit by following the instuctions on [installing the Python interface](doc/install/install-from-source.md#install-the-python-interface) and [installing the C++ interface](doc/install/install-from-source.md#install-the-c-interface). The C++ interface is necessary when using DeePMD-kit with LAMMPS, i-PI or GROMACS.
# Use DeePMD-kit
A quick-start on using DeePMD-kit can be found as follows:
- [Prepare data with dpdata](doc/data/dpdata.md)
- [Training a model](doc/train/training.md)
- [Freeze a model](doc/freeze/freeze.md)
- [Test a model](doc/test/test.md)
- [Run MD with LAMMPS](doc/third-party/lammps.md)
A full [document](doc/train/train-input-auto.rst) on options in the training input script is available.
# Advanced
- [Installation](doc/install/index.md)
- [Easy install](doc/install/easy-install.md)
- [Install from source code](doc/install/install-from-source.md)
- [Install LAMMPS](doc/install/install-lammps.md)
- [Install i-PI](doc/install/install-ipi.md)
- [Install GROMACS](doc/install/install-gromacs.md)
- [Building conda packages](doc/install/build-conda.md)
- [Data](doc/data/index.md)
- [System](doc/data/system.md)
- [Formats of a system](doc/data/data-conv.md)
- [Prepare data with dpdata](doc/data/dpdata.md)
- [Model](doc/model/index.md)
- [Overall](doc/model/overall.md)
- [Descriptor `"se_e2_a"`](doc/model/train-se-e2-a.md)
- [Descriptor `"se_e2_r"`](doc/model/train-se-e2-r.md)
- [Descriptor `"se_e3"`](doc/model/train-se-e3.md)
- [Descriptor `"se_atten"`](doc/model/train-se-atten.md)
- [Descriptor `"hybrid"`](doc/model/train-hybrid.md)
- [Descriptor `sel`](doc/model/sel.md)
- [Fit energy](doc/model/train-energy.md)
- [Fit `tensor` like `Dipole` and `Polarizability`](doc/model/train-fitting-tensor.md)
- [Train a Deep Potential model using `type embedding` approach](doc/model/train-se-e2-a-tebd.md)
- [Deep potential long-range](doc/model/dplr.md)
- [Deep Potential - Range Correction (DPRc)](doc/model/dprc.md)
- [Training](doc/train/index.md)
- [Training a model](doc/train/training.md)
- [Advanced options](doc/train/training-advanced.md)
- [Parallel training](doc/train/parallel-training.md)
- [TensorBoard Usage](doc/train/tensorboard.md)
- [Known limitations of using GPUs](doc/train/gpu-limitations.md)
- [Training Parameters](doc/train/train-input-auto.rst)
- [Freeze and Compress](doc/freeze/index.rst)
- [Freeze a model](doc/freeze/freeze.md)
- [Compress a model](doc/freeze/compress.md)
- [Test](doc/test/index.rst)
- [Test a model](doc/test/test.md)
- [Calculate Model Deviation](doc/test/model-deviation.md)
- [Inference](doc/inference/index.rst)
- [Python interface](doc/inference/python.md)
- [C++ interface](doc/inference/cxx.md)
- [Integrate with third-party packages](doc/third-party/index.rst)
- [Use deep potential with ASE](doc/third-party/ase.md)
- [Run MD with LAMMPS](doc/third-party/lammps.md)
- [LAMMPS commands](doc/third-party/lammps-command.md)
- [Run path-integral MD with i-PI](doc/third-party/ipi.md)
- [Run MD with GROMACS](doc/third-party/gromacs.md)
- [Interfaces out of DeePMD-kit](doc/third-party/out-of-deepmd-kit.md)
- [Use NVNMD](doc/nvnmd/index.md)
# Code structure
The code is organized as follows:
* `data/raw`: tools manipulating the raw data files.
* `examples`: examples.
* `deepmd`: DeePMD-kit python modules.
* `source/api_cc`: source code of DeePMD-kit C++ API.
* `source/ipi`: source code of i-PI client.
* `source/lib`: source code of DeePMD-kit library.
* `source/lmp`: source code of Lammps module.
* `source/gmx`: source code of Gromacs plugin.
* `source/op`: tensorflow op implementation. working with library.
# Troubleshooting
- [Model compatibility](doc/troubleshooting/model_compatability.md)
- [Installation](doc/troubleshooting/installation.md)
- [The temperature undulates violently during early stages of MD](doc/troubleshooting/md_energy_undulation.md)
- [MD: cannot run LAMMPS after installing a new version of DeePMD-kit](doc/troubleshooting/md_version_compatibility.md)
- [Do we need to set rcut < half boxsize?](doc/troubleshooting/howtoset_rcut.md)
- [How to set sel?](doc/troubleshooting/howtoset_sel.md)
- [How to control the number of nodes used by a job?](doc/troubleshooting/howtoset_num_nodes.md)
- [How to tune Fitting/embedding-net size?](doc/troubleshooting/howtoset_netsize.md)
# Contributing
See [DeePMD-kit Contributing Guide](CONTRIBUTING.md) to become a contributor! 🤓
[1]: https://arxiv.org/abs/1707.01478
[2]: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001
[3]: https://arxiv.org/abs/1805.09003
[4]: https://aip.scitation.org/doi/full/10.1063/1.5027645
# DeePMD-kit(Deep Potential Molecular Dynamics)
## 模型介绍
DeePMD-kit是深度势能分子动力学的开源项目。分子动力学是一套分子模拟方法，该方法主要依靠牛顿力学来模拟分子体系的运动。分子动力学在微观世界模拟中起着承载作用，在物理、化学、生物、材料科学以及力学等领域都有着非常重要的应用。
深势科技成功将DeePMD-kit进行了并行化，首次实现了具有AIMD精度的第一性原理分子动力学模拟的商业与原子规模的应用，为分子动力学提供了革命性的高性能解决方案。
## 模型结构
DeePMD-Kit,参考https://docs.deepmodeling.com/projects/deepmd/en/master/index.html
## DeePMD代码参考版本
版本：v2.1.5
原始代码位置：https://github.com/deepmodeling/deepmd-kit
## 数据集
示例中验证的数据集来自：
wget http://pai-blade.oss-accelerate.aliyuncs.com/bladedisc_notebook_binaries/data/disc_deepmd_example/data.tar.gz
tar -xvf data.tar.gz
实际业务测试请选择合适的数据集进行测试。
## 训练
### 环境配置
提供[光源](https://www.sourcefind.cn/#/service-details)拉取的训练的docker镜像：
* 训练镜像：
python依赖安装：
pip3 install -r requirement.txt
### 安装
export ROCM_ROOT=/opt/dtk-22.04.2
DP_VARIANT=rocm python3 -m pip install . -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
### 单卡测试
使用BladeDISC加速的测试命令（此处以单机单卡规模为例说明）,请根据实际使用的卡id号调整run.sh中指定的卡号：
bash run.sh
如果需要关闭BladeDISC加速，需要注释掉main.py文件中的如下内容：
disc.enable()
### 性能和准确率数据
| case | 优化前(关闭disc) | 优化后(打开disc) | 性能提升 |
| :---- | ----: | :----: | ---- |
water | 3.1s | 2.7s | 12.9%
GeTe_2_body描述符| 4.06s | 2.67s | 34.2%
| GeTe_hybrid描述符 | 18.5s | 17.8s | 3.7% |
准确率通过对比优化前与优化后的输出文件lcurve.out输出趋势一致进行确认；
## 参考
* https://github.com/deepmodeling/deepmd-kit
README_deepmd.md 0 → 100644
View file @ 56d2b104
<span style="font-size:larger;">DeePMD-kit Manual</span>
========
[![GitHub release](https://img.shields.io/github/release/deepmodeling/deepmd-kit.svg?maxAge=86400)](https://github.com/deepmodeling/deepmd-kit/releases)
[![doi:10.1016/j.cpc.2018.03.016](https://img.shields.io/badge/DOI-10.1016%2Fj.cpc.2018.03.016-blue)](https://doi.org/10.1016/j.cpc.2020.107206)
![Citations](https://citations.njzjz.win/10.1016/j.cpc.2018.03.016)
[![offline packages](https://img.shields.io/github/downloads/deepmodeling/deepmd-kit/total?label=offline%20packages)](https://github.com/deepmodeling/deepmd-kit/releases)
[![conda install](https://img.shields.io/badge/downloads-9k%20total-green.svg?style=round-square&label=conda%20install)](https://anaconda.org/deepmodeling/deepmd-kit)
[![pip install](https://img.shields.io/pypi/dm/deepmd-kit?label=pip%20install)](https://pypi.org/project/deepmd-kit)
[![docker pull](https://img.shields.io/docker/pulls/deepmodeling/deepmd-kit)](https://hub.docker.com/r/deepmodeling/deepmd-kit)
[![Documentation Status](https://readthedocs.org/projects/deepmd/badge/)](https://deepmd.readthedocs.io/)
# Table of contents
- [About DeePMD-kit](#about-deepmd-kit)
- [Highlights in v2.0](#highlights-in-deepmd-kit-v2.0)
- [Highlighted features](#highlighted-features)
- [License and credits](#license-and-credits)
- [Deep Potential in a nutshell](#deep-potential-in-a-nutshell)
- [Download and install](#download-and-install)
- [Use DeePMD-kit](#use-deepmd-kit)
- [Code structure](#code-structure)
- [Troubleshooting](#troubleshooting)
# About DeePMD-kit
DeePMD-kit is a package written in Python/C++, designed to minimize the effort required to build deep learning based model of interatomic potential energy and force field and to perform molecular dynamics (MD). This brings new hopes to addressing the accuracy-versus-efficiency dilemma in molecular simulations. Applications of DeePMD-kit span from finite molecules to extended systems and from metallic systems to chemically bonded systems.
For more information, check the [documentation](https://deepmd.readthedocs.io/).
# Highlights in DeePMD-kit v2.0
* [Model compression](doc/freeze/compress.md). Accelerate the efficiency of model inference for 4-15 times.
* [New descriptors](doc/model/overall.md). Including [`se_e2_r`](doc/model/train-se-e2-r.md) and [`se_e3`](doc/model/train-se-e3.md).
* [Hybridization of descriptors](doc/model/train-hybrid.md). Hybrid descriptor constructed from concatenation of several descriptors.
* [Atom type embedding](doc/model/train-se-e2-a-tebd.md). Enable atom type embedding to decline training complexity and refine performance.
* Training and inference the dipole (vector) and polarizability (matrix).
* Split of training and validation dataset.
* Optimized training on GPUs.
## Highlighted features
* **interfaced with TensorFlow**, one of the most popular deep learning frameworks, making the training process highly automatic and efficient, in addition Tensorboard can be used to visualize training procedure.
* **interfaced with high-performance classical MD and quantum (path-integral) MD packages**, i.e., LAMMPS and i-PI, respectively.
* **implements the Deep Potential series models**, which have been successfully applied to finite and extended systems including organic molecules, metals, semiconductors, and insulators, etc.
* **implements MPI and GPU supports**, makes it highly efficient for high performance parallel and distributed computing.
* **highly modularized**, easy to adapt to different descriptors for deep learning based potential energy models.
## License and credits
The project DeePMD-kit is licensed under [GNU LGPLv3.0](./LICENSE).
If you use this code in any future publications, please cite this using
``Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. "DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics." Computer Physics Communications 228 (2018): 178-184.``
## Deep Potential in a nutshell
The goal of Deep Potential is to employ deep learning techniques and realize an inter-atomic potential energy model that is general, accurate, computationally efficient and scalable. The key component is to respect the extensive and symmetry-invariant properties of a potential energy model by assigning a local reference frame and a local environment to each atom. Each environment contains a finite number of atoms, whose local coordinates are arranged in a symmetry preserving way. These local coordinates are then transformed, through a sub-network, to a so-called *atomic energy*. Summing up all the atomic energies gives the potential energy of the system.
The initial proof of concept is in the [Deep Potential][1] paper, which employed an approach that was devised to train the neural network model with the potential energy only. With typical *ab initio* molecular dynamics (AIMD) datasets this is insufficient to reproduce the trajectories. The Deep Potential Molecular Dynamics ([DeePMD][2]) model overcomes this limitation. In addition, the learning process in DeePMD improves significantly over the Deep Potential method thanks to the introduction of a flexible family of loss functions. The NN potential constructed in this way reproduces accurately the AIMD trajectories, both classical and quantum (path integral), in extended and finite systems, at a cost that scales linearly with system size and is always several orders of magnitude lower than that of equivalent AIMD simulations.
Although being highly efficient, the original Deep Potential model satisfies the extensive and symmetry-invariant properties of a potential energy model at the price of introducing discontinuities in the model. This has negligible influence on a trajectory from canonical sampling but might not be sufficient for calculations of dynamical and mechanical properties. These points motivated us to develop the Deep Potential-Smooth Edition ([DeepPot-SE][3]) model, which replaces the non-smooth local frame with a smooth and adaptive embedding network. DeepPot-SE shows great ability in modeling many kinds of systems that are of interests in the fields of physics, chemistry, biology, and materials science.
In addition to building up potential energy models, DeePMD-kit can also be used to build up coarse-grained models. In these models, the quantity that we want to parameterize is the free energy, or the coarse-grained potential, of the coarse-grained particles. See the [DeePCG paper][4] for more details.
# Download and install
Please follow our [GitHub](https://github.com/deepmodeling/deepmd-kit) webpage to download the [latest released version](https://github.com/deepmodeling/deepmd-kit/tree/master) and [development version](https://github.com/deepmodeling/deepmd-kit/tree/devel).
DeePMD-kit offers multiple installation methods. It is recommend using easily methods like [offline packages](doc/install/easy-install.md#offline-packages), [conda](doc/install/easy-install.md#with-conda) and [docker](doc/install/easy-install.md#with-docker).
One may manually install DeePMD-kit by following the instuctions on [installing the Python interface](doc/install/install-from-source.md#install-the-python-interface) and [installing the C++ interface](doc/install/install-from-source.md#install-the-c-interface). The C++ interface is necessary when using DeePMD-kit with LAMMPS, i-PI or GROMACS.
# Use DeePMD-kit
A quick-start on using DeePMD-kit can be found as follows:
- [Prepare data with dpdata](doc/data/dpdata.md)
- [Training a model](doc/train/training.md)
- [Freeze a model](doc/freeze/freeze.md)
- [Test a model](doc/test/test.md)
- [Run MD with LAMMPS](doc/third-party/lammps.md)
A full [document](doc/train/train-input-auto.rst) on options in the training input script is available.
# Advanced
- [Installation](doc/install/index.md)
- [Easy install](doc/install/easy-install.md)
- [Install from source code](doc/install/install-from-source.md)
- [Install LAMMPS](doc/install/install-lammps.md)
- [Install i-PI](doc/install/install-ipi.md)
- [Install GROMACS](doc/install/install-gromacs.md)
- [Building conda packages](doc/install/build-conda.md)
- [Data](doc/data/index.md)
- [System](doc/data/system.md)
- [Formats of a system](doc/data/data-conv.md)
- [Prepare data with dpdata](doc/data/dpdata.md)
- [Model](doc/model/index.md)
- [Overall](doc/model/overall.md)
- [Descriptor `"se_e2_a"`](doc/model/train-se-e2-a.md)
- [Descriptor `"se_e2_r"`](doc/model/train-se-e2-r.md)
- [Descriptor `"se_e3"`](doc/model/train-se-e3.md)
- [Descriptor `"se_atten"`](doc/model/train-se-atten.md)
- [Descriptor `"hybrid"`](doc/model/train-hybrid.md)
- [Descriptor `sel`](doc/model/sel.md)
- [Fit energy](doc/model/train-energy.md)
- [Fit `tensor` like `Dipole` and `Polarizability`](doc/model/train-fitting-tensor.md)
- [Train a Deep Potential model using `type embedding` approach](doc/model/train-se-e2-a-tebd.md)
- [Deep potential long-range](doc/model/dplr.md)
- [Deep Potential - Range Correction (DPRc)](doc/model/dprc.md)
- [Training](doc/train/index.md)
- [Training a model](doc/train/training.md)
- [Advanced options](doc/train/training-advanced.md)
- [Parallel training](doc/train/parallel-training.md)
- [TensorBoard Usage](doc/train/tensorboard.md)
- [Known limitations of using GPUs](doc/train/gpu-limitations.md)
- [Training Parameters](doc/train/train-input-auto.rst)
- [Freeze and Compress](doc/freeze/index.rst)
- [Freeze a model](doc/freeze/freeze.md)
- [Compress a model](doc/freeze/compress.md)
- [Test](doc/test/index.rst)
- [Test a model](doc/test/test.md)
- [Calculate Model Deviation](doc/test/model-deviation.md)
- [Inference](doc/inference/index.rst)
- [Python interface](doc/inference/python.md)
- [C++ interface](doc/inference/cxx.md)
- [Integrate with third-party packages](doc/third-party/index.rst)
- [Use deep potential with ASE](doc/third-party/ase.md)
- [Run MD with LAMMPS](doc/third-party/lammps.md)
- [LAMMPS commands](doc/third-party/lammps-command.md)
- [Run path-integral MD with i-PI](doc/third-party/ipi.md)
- [Run MD with GROMACS](doc/third-party/gromacs.md)
- [Interfaces out of DeePMD-kit](doc/third-party/out-of-deepmd-kit.md)
- [Use NVNMD](doc/nvnmd/index.md)
# Code structure
The code is organized as follows:
* `data/raw`: tools manipulating the raw data files.
* `examples`: examples.
* `deepmd`: DeePMD-kit python modules.
* `source/api_cc`: source code of DeePMD-kit C++ API.
* `source/ipi`: source code of i-PI client.
* `source/lib`: source code of DeePMD-kit library.
* `source/lmp`: source code of Lammps module.
* `source/gmx`: source code of Gromacs plugin.
* `source/op`: tensorflow op implementation. working with library.
# Troubleshooting
- [Model compatibility](doc/troubleshooting/model_compatability.md)
- [Installation](doc/troubleshooting/installation.md)
- [The temperature undulates violently during early stages of MD](doc/troubleshooting/md_energy_undulation.md)
- [MD: cannot run LAMMPS after installing a new version of DeePMD-kit](doc/troubleshooting/md_version_compatibility.md)
- [Do we need to set rcut < half boxsize?](doc/troubleshooting/howtoset_rcut.md)
- [How to set sel?](doc/troubleshooting/howtoset_sel.md)
- [How to control the number of nodes used by a job?](doc/troubleshooting/howtoset_num_nodes.md)
- [How to tune Fitting/embedding-net size?](doc/troubleshooting/howtoset_netsize.md)
# Contributing
See [DeePMD-kit Contributing Guide](CONTRIBUTING.md) to become a contributor! 🤓
[1]: https://arxiv.org/abs/1707.01478
[2]: https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.143001
[3]: https://arxiv.org/abs/1805.09003
[4]: https://aip.scitation.org/doi/full/10.1063/1.5027645
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Performing C SOURCE FILE Test CMAKE_HAVE_LIBC_PTHREAD failed with the following output:
Change Dir: /public/home/zhangqha/sourcecode/source/examples/TensorFlow/Train/new/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-GU1KWh
Run Build Command(s):/tmp/pip-build-env-6r5leccz/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_08e87 && [1/2] Building C object CMakeFiles/cmTC_08e87.dir/src.c.o
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CMakeFiles/cmTC_08e87.dir/src.c.o: In function `main':
src.c:(.text+0x3e): undefined reference to `pthread_create'
src.c:(.text+0x4a): undefined reference to `pthread_detach'
src.c:(.text+0x56): undefined reference to `pthread_cancel'
src.c:(.text+0x67): undefined reference to `pthread_join'
collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
Source file was:
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Change Dir: /public/home/zhangqha/sourcecode/source/examples/TensorFlow/Train/new/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-MeJDCi
Run Build Command(s):/tmp/pip-build-env-6r5leccz/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_e25c5 && [1/2] Building C object CMakeFiles/cmTC_e25c5.dir/CheckFunctionExists.c.o
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collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
Performing C SOURCE FILE Test CMAKE_HAVE_LIBC_PTHREAD failed with the following output:
Change Dir: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-maaLfJ
Run Build Command(s):/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_1daa7 && [1/2] Building C object CMakeFiles/cmTC_1daa7.dir/src.c.o
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FAILED: cmTC_1daa7
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CMakeFiles/cmTC_1daa7.dir/src.c.o: In function `main':
src.c:(.text+0x3e): undefined reference to `pthread_create'
src.c:(.text+0x4a): undefined reference to `pthread_detach'
src.c:(.text+0x56): undefined reference to `pthread_cancel'
src.c:(.text+0x67): undefined reference to `pthread_join'
collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
Source file was:
#include <pthread.h>
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pthread_t thread;
pthread_create(&thread, NULL, test_func, NULL);
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pthread_atfork(NULL, NULL, NULL);
pthread_exit(NULL);
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Determining if the function pthread_create exists in the pthreads failed with the following output:
Change Dir: /root/deepmd-kit/_skbuild/linux-x86_64-3.6/cmake-build/CMakeFiles/CMakeScratch/TryCompile-2YQCXJ
Run Build Command(s):/tmp/pip-build-env-5_1o2n9q/overlay/lib/python3.6/site-packages/ninja/data/bin/ninja cmTC_9db68 && [1/2] Building C object CMakeFiles/cmTC_9db68.dir/CheckFunctionExists.c.o
[2/2] Linking C executable cmTC_9db68
FAILED: cmTC_9db68
: && /usr/bin/cc -DCHECK_FUNCTION_EXISTS=pthread_create CMakeFiles/cmTC_9db68.dir/CheckFunctionExists.c.o -o cmTC_9db68 -lpthreads && :
/usr/bin/ld: cannot find -lpthreads
collect2: error: ld returned 1 exit status
ninja: build stopped: subcommand failed.
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