From d2ab010555acb6508452be1c41ce6ce2a9cd4e43 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 27 Nov 2023 05:30:24 +0000 Subject: [PATCH] refine docs --- README.md | 48 +++++++++++++++++++++++++----------------------- 1 file changed, 25 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 969f8cff3a..8eafba80ec 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,4 @@ -[DeePMD-kit logo](./doc/logo.md) - --------------------------------------------------------------------------------- - -## DeePMD-kit(PaddlePaddle backend) +# DeePMD-kit(PaddlePaddle backend) > [!IMPORTANT] > 本项目为 DeePMD-kit 的 PaddlePaddle 版本，主要修改了部分代码，使其可以运行在 PaddlePaddle 上。运行功能包括 water_se_e2_a 案例的单卡 GPU 训练、单卡 GPU 评估、导出静态图模型、接入 LAMMPS(GPU) 推理 4 部分的功能。 @@ -40,7 +36,7 @@ cd ./source/lib/paddle_src python setup_ins.py install ``` -安装完毕之后建议运行如下命令测试一下 python 自定义算子在 CPU、GPU 上的正确性： +安装完毕之后建议运行如下命令测试一下 python 端自定义算子在 CPU、GPU 上的正确性： ``` sh wget -nc https://paddle-org.bj.bcebos.com/paddlescience/deepmd/deepmd_custom_op_test_data.tar @@ -49,12 +45,9 @@ export UNITTEST_DIR=$PWD/deepmd_custom_op_test_data python ./custom_op_test.py ``` -除少量 `deprecated` 相关的警告外，如果输出全部都是 True，则说明自定义算子安装成功并且运行正常。 +除少量 `deprecated` 相关的警告外，如果输出全部都是 True，则说明 python 端自定义算子安装成功并且运行正常。 -### 2.1 训练 - -> [!NOTE] -> 暂时只支持 water_se_e2_a 案例的训练 +### 2.2 训练 ``` sh # 进入案例目录 @@ -63,7 +56,7 @@ cd examples/water/se_e2_a dp train ./input.json ``` -### 2.2 评估 +### 2.3 评估 ``` sh # 进入案例目录 @@ -74,7 +67,7 @@ WEIGHT_PATH="path/to/your_model.pdparams" dp test -m ${WEIGHT_PATH} -s ../data/data_3/ -n 30 ``` -### 2.3 导出静态图模型 +### 2.4 导出静态图模型 ``` sh # 进入案例目录 @@ -87,7 +80,7 @@ DUMP_PATH="path/to/your_dump" dp freeze -i ${WEIGHT_PATH} -o ${DUMP_PATH} ``` -### 2.4 在 LAMMPS(GPU) 中推理 +### 2.5 在 LAMMPS(GPU) 中推理 1. 修改 `examples/water/lmp/in.lammps` 文件，将 `pair_style deepmd` 后面的路径改为 **2.3 导出静态图模型** 这一章节内设置好的 DUMP_PATH 的值 @@ -95,7 +88,7 @@ dp freeze -i ${WEIGHT_PATH} -o ${DUMP_PATH} pair_style deepmd "path/to/your_dump" ``` -2. 编译 Paddle，得到未裁剪算子的 Paddle 推理库(LAMMPS 推理涉及到 `xxx_grad` 反向算子，因而需要使用未裁剪的 Paddle 推理库) +2. 编译 Paddle，得到未裁剪算子的 Paddle 推理库(LAMMPS 推理涉及到 `xxx_grad` 反向算子，因而在此需要手动编译 Paddle，得到未裁剪的 Paddle 推理库) ``` sh git clone https://github.com/PaddlePaddle/Paddle.git -b develop @@ -105,10 +98,12 @@ dp freeze -i ${WEIGHT_PATH} -o ${DUMP_PATH} # 推荐使用 Anaconda 安装 python3.9 环境，并在该环境下执行编译命令 cmake .. -DPY_VERSION=3.9 -DWITH_GPU=ON -WITH_DISTRIBUTE=ON -DWITH_TESTING=ON -DCMAKE_BUILD_TYPE=Release make -j$(nproc) - pip install python/dist/paddlepaddle_gpu-0.0.0-cp39-cp39-linux_x86_64.whl + + # 编译完成后，确认 paddle_inference_install_dir 推理库是否存在 + ls build/paddle_inference_install_dir ``` -3. 安装 LAMMPS 并运行推理 +3. Paddle 推理库和 LAMMPS 联合编译安装，并运行推理 ``` sh # 下载并解压 lammps 源码 @@ -166,7 +161,9 @@ dp freeze -i ${WEIGHT_PATH} -o ${DUMP_PATH} lmp_serial -in in.lammps ``` -4. 直接运行推理 +4. [可选]直接运行推理 + + 若已完成 **3. Paddle 推理库和 LAMMPS 联合编译安装，并运行推理**，且没有对 C++ 代码进行修改，则无需重新联合编译 Paddle 推理库和 LAMMPS，直接运行以下命令即可开始推理。 ``` sh # 设置推理时的 GPU 卡号 @@ -182,10 +179,13 @@ dp freeze -i ${WEIGHT_PATH} -o ${DUMP_PATH} lmp_serial -in in.lammps ``` ---- +-------------------------------------------------------------------------------- + +[DeePMD-kit logo](./doc/logo.md) -DeePMD-kit Manual -======== +-------------------------------------------------------------------------------- + +# DeePMD-kit Manual [![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) @@ -216,7 +216,8 @@ 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 4-15 times. +- [Model compression](doc/freeze/compress.md). Accelerate the efficiency of model inference 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 the 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. @@ -226,7 +227,8 @@ For more information, check the [documentation](https://deepmd.readthedocs.io/). ## 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 procedures. +- **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 procedures. + - **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, insulators, etc. - **implements MPI and GPU supports**, making it highly efficient for high-performance parallel and distributed computing.