新建
固态电解质实战 | DP Combo@APP体验
ZhexuanS
推荐镜像 :DeePMD-kit:2.2.1-cuda11.6-notebook
推荐机型 :c12_m46_1 * NVIDIA GPU B
赞 2
1
4
目录
数据集
CP2K_dpdata(v5)
固态电解质实战 | DP Combo@APP体验
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基本内容：使用DP Combo APP快速体验DeePMD-kit,DPA2训练固态电解质势函数
🚀使用DP Combo APP把软件安装-环境配置-模型准备-训练参数设置-提交任务的流程-->极简到上传数据集-一键提交-下载环节，内置的参数也节省了我们逐行调整json文件的时间。
快捷，灵活，内置资源丰富的特点让无论是小白/资深用户都能够无痛体验一把DP势函数训练。
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1. DP Combo基本介绍
1.1 页面简介/数据准备
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目前开发的子模块主要包括：1|从头开始训练，2|微调训练，及3|通过DP-GEN进行的微调训练.
模块一: DeePMD-kit的从头训练，对应于命令行中使用的dp train input.json;
模块二：DPA势函数的微调训练，对应于命令行中使用的dp train input.json --finetune dpa.pb.
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不过，无论是从哪个模块出发，我们都需要先准备自己的数据集并打包成tgz格式上传。
我们以固态电解质的数据集为例进行计算
注：本教程所使用的数据集源自科学智能广场（AIS-Square)，有更多模型和数据需求的同学赶快去探索一下吧~
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[1]
!wget https://aisquare-zjk.oss-cn-zhangjiakou.aliyuncs.com/AIS-Square/datasets/LiGePS-SSE-PBE/LiGePS-SSE-PBE.zip && unzip LiGePS-SSE-PBE.zip
--2023-09-19 11:21:19-- https://aisquare-zjk.oss-cn-zhangjiakou.aliyuncs.com/AIS-Square/datasets/LiGePS-SSE-PBE/LiGePS-SSE-PBE.zip Resolving ga.dp.tech (ga.dp.tech)... 10.255.254.18, 10.255.254.7, 10.255.254.37 Connecting to ga.dp.tech (ga.dp.tech)|10.255.254.18|:8118... connected. Proxy request sent, awaiting response... 200 OK Length: 58433057 (56M) [application/zip] Saving to: ‘LiGePS-SSE-PBE.zip’ LiGePS-SSE-PBE.zip 100%[===================>] 55.73M 13.2MB/s in 3.7s 2023-09-19 11:21:23 (15.1 MB/s) - ‘LiGePS-SSE-PBE.zip’ saved [58433057/58433057] Archive: LiGePS-SSE-PBE.zip creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-init.system/test inflating: 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home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.012/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.012/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000012-data.020/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.013/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000013-data.021/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.014/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000014-data.022/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.015/type.raw creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/ creating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/set.000/ inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/set.000/box.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/set.000/force.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/set.000/energy.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/set.000/virial.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/set.000/coord.npy inflating: home/chenglab/hjx/proj-sse-new/dpgen-LiGePS/data.init/LiGePS-iter.000015-data.023/type.raw creating: iter.000000/02.fp/data.000/ creating: iter.000000/02.fp/data.000/set.000/ inflating: iter.000000/02.fp/data.000/set.000/box.npy inflating: iter.000000/02.fp/data.000/set.000/force.npy inflating: iter.000000/02.fp/data.000/set.000/energy.npy inflating: iter.000000/02.fp/data.000/set.000/virial.npy inflating: iter.000000/02.fp/data.000/set.000/coord.npy extracting: iter.000000/02.fp/data.000/type_map.raw inflating: iter.000000/02.fp/data.000/energy.raw inflating: iter.000000/02.fp/data.000/force.raw inflating: iter.000000/02.fp/data.000/box.raw inflating: iter.000000/02.fp/data.000/virial.raw inflating: iter.000000/02.fp/data.000/type.raw inflating: iter.000000/02.fp/data.000/coord.raw creating: iter.000000/02.fp/data.001/ creating: iter.000000/02.fp/data.001/set.000/ inflating: iter.000000/02.fp/data.001/set.000/box.npy inflating: iter.000000/02.fp/data.001/set.000/force.npy inflating: iter.000000/02.fp/data.001/set.000/energy.npy inflating: iter.000000/02.fp/data.001/set.000/virial.npy inflating: iter.000000/02.fp/data.001/set.000/coord.npy extracting: iter.000000/02.fp/data.001/type_map.raw inflating: iter.000000/02.fp/data.001/energy.raw inflating: iter.000000/02.fp/data.001/force.raw inflating: iter.000000/02.fp/data.001/box.raw inflating: iter.000000/02.fp/data.001/virial.raw inflating: iter.000000/02.fp/data.001/type.raw inflating: iter.000000/02.fp/data.001/coord.raw creating: iter.000000/02.fp/data.002/ creating: iter.000000/02.fp/data.002/set.000/ inflating: iter.000000/02.fp/data.002/set.000/box.npy inflating: iter.000000/02.fp/data.002/set.000/force.npy inflating: iter.000000/02.fp/data.002/set.000/energy.npy inflating: iter.000000/02.fp/data.002/set.000/virial.npy inflating: iter.000000/02.fp/data.002/set.000/coord.npy extracting: iter.000000/02.fp/data.002/type_map.raw inflating: iter.000000/02.fp/data.002/energy.raw inflating: iter.000000/02.fp/data.002/force.raw inflating: iter.000000/02.fp/data.002/box.raw inflating: iter.000000/02.fp/data.002/virial.raw inflating: iter.000000/02.fp/data.002/type.raw inflating: iter.000000/02.fp/data.002/coord.raw creating: iter.000000/02.fp/data.003/ creating: iter.000000/02.fp/data.003/set.000/ inflating: iter.000000/02.fp/data.003/set.000/box.npy inflating: iter.000000/02.fp/data.003/set.000/force.npy inflating: iter.000000/02.fp/data.003/set.000/energy.npy inflating: iter.000000/02.fp/data.003/set.000/virial.npy inflating: iter.000000/02.fp/data.003/set.000/coord.npy extracting: iter.000000/02.fp/data.003/type_map.raw inflating: iter.000000/02.fp/data.003/energy.raw inflating: iter.000000/02.fp/data.003/force.raw inflating: iter.000000/02.fp/data.003/box.raw inflating: iter.000000/02.fp/data.003/virial.raw inflating: iter.000000/02.fp/data.003/type.raw inflating: iter.000000/02.fp/data.003/coord.raw creating: iter.000001/02.fp/data.000/ creating: iter.000001/02.fp/data.000/set.000/ inflating: iter.000001/02.fp/data.000/set.000/box.npy inflating: iter.000001/02.fp/data.000/set.000/force.npy inflating: iter.000001/02.fp/data.000/set.000/energy.npy inflating: iter.000001/02.fp/data.000/set.000/virial.npy inflating: iter.000001/02.fp/data.000/set.000/coord.npy extracting: iter.000001/02.fp/data.000/type_map.raw inflating: iter.000001/02.fp/data.000/energy.raw inflating: iter.000001/02.fp/data.000/force.raw inflating: iter.000001/02.fp/data.000/box.raw inflating: iter.000001/02.fp/data.000/virial.raw inflating: iter.000001/02.fp/data.000/type.raw inflating: iter.000001/02.fp/data.000/coord.raw creating: iter.000001/02.fp/data.001/ creating: iter.000001/02.fp/data.001/set.000/ inflating: iter.000001/02.fp/data.001/set.000/box.npy inflating: iter.000001/02.fp/data.001/set.000/force.npy inflating: iter.000001/02.fp/data.001/set.000/energy.npy inflating: iter.000001/02.fp/data.001/set.000/virial.npy inflating: iter.000001/02.fp/data.001/set.000/coord.npy extracting: iter.000001/02.fp/data.001/type_map.raw inflating: iter.000001/02.fp/data.001/energy.raw inflating: iter.000001/02.fp/data.001/force.raw inflating: iter.000001/02.fp/data.001/box.raw inflating: iter.000001/02.fp/data.001/virial.raw inflating: iter.000001/02.fp/data.001/type.raw inflating: iter.000001/02.fp/data.001/coord.raw creating: iter.000001/02.fp/data.002/ creating: iter.000001/02.fp/data.002/set.000/ inflating: iter.000001/02.fp/data.002/set.000/box.npy inflating: iter.000001/02.fp/data.002/set.000/force.npy inflating: iter.000001/02.fp/data.002/set.000/energy.npy inflating: iter.000001/02.fp/data.002/set.000/virial.npy inflating: iter.000001/02.fp/data.002/set.000/coord.npy extracting: iter.000001/02.fp/data.002/type_map.raw inflating: iter.000001/02.fp/data.002/energy.raw inflating: iter.000001/02.fp/data.002/force.raw inflating: iter.000001/02.fp/data.002/box.raw inflating: iter.000001/02.fp/data.002/virial.raw inflating: iter.000001/02.fp/data.002/type.raw inflating: iter.000001/02.fp/data.002/coord.raw creating: iter.000001/02.fp/data.003/ creating: iter.000001/02.fp/data.003/set.000/ inflating: iter.000001/02.fp/data.003/set.000/box.npy inflating: iter.000001/02.fp/data.003/set.000/force.npy inflating: iter.000001/02.fp/data.003/set.000/energy.npy inflating: iter.000001/02.fp/data.003/set.000/virial.npy inflating: iter.000001/02.fp/data.003/set.000/coord.npy extracting: iter.000001/02.fp/data.003/type_map.raw inflating: iter.000001/02.fp/data.003/energy.raw inflating: iter.000001/02.fp/data.003/force.raw inflating: iter.000001/02.fp/data.003/box.raw inflating: iter.000001/02.fp/data.003/virial.raw inflating: iter.000001/02.fp/data.003/type.raw inflating: iter.000001/02.fp/data.003/coord.raw creating: iter.000002/02.fp/data.000/ creating: iter.000002/02.fp/data.000/set.000/ inflating: iter.000002/02.fp/data.000/set.000/box.npy inflating: iter.000002/02.fp/data.000/set.000/force.npy inflating: iter.000002/02.fp/data.000/set.000/energy.npy inflating: iter.000002/02.fp/data.000/set.000/virial.npy inflating: iter.000002/02.fp/data.000/set.000/coord.npy extracting: iter.000002/02.fp/data.000/type_map.raw inflating: iter.000002/02.fp/data.000/energy.raw inflating: iter.000002/02.fp/data.000/force.raw inflating: iter.000002/02.fp/data.000/box.raw inflating: iter.000002/02.fp/data.000/virial.raw inflating: iter.000002/02.fp/data.000/type.raw inflating: iter.000002/02.fp/data.000/coord.raw creating: iter.000002/02.fp/data.001/ creating: iter.000002/02.fp/data.001/set.000/ inflating: iter.000002/02.fp/data.001/set.000/box.npy inflating: iter.000002/02.fp/data.001/set.000/force.npy inflating: iter.000002/02.fp/data.001/set.000/energy.npy inflating: iter.000002/02.fp/data.001/set.000/virial.npy inflating: iter.000002/02.fp/data.001/set.000/coord.npy extracting: iter.000002/02.fp/data.001/type_map.raw inflating: iter.000002/02.fp/data.001/energy.raw inflating: iter.000002/02.fp/data.001/force.raw inflating: iter.000002/02.fp/data.001/box.raw inflating: iter.000002/02.fp/data.001/virial.raw inflating: iter.000002/02.fp/data.001/type.raw inflating: iter.000002/02.fp/data.001/coord.raw creating: iter.000002/02.fp/data.002/ creating: iter.000002/02.fp/data.002/set.000/ inflating: iter.000002/02.fp/data.002/set.000/box.npy inflating: iter.000002/02.fp/data.002/set.000/force.npy inflating: iter.000002/02.fp/data.002/set.000/energy.npy inflating: iter.000002/02.fp/data.002/set.000/virial.npy inflating: iter.000002/02.fp/data.002/set.000/coord.npy extracting: iter.000002/02.fp/data.002/type_map.raw inflating: iter.000002/02.fp/data.002/energy.raw inflating: iter.000002/02.fp/data.002/force.raw inflating: iter.000002/02.fp/data.002/box.raw inflating: iter.000002/02.fp/data.002/virial.raw inflating: iter.000002/02.fp/data.002/type.raw inflating: iter.000002/02.fp/data.002/coord.raw creating: iter.000002/02.fp/data.003/ creating: iter.000002/02.fp/data.003/set.000/ inflating: iter.000002/02.fp/data.003/set.000/box.npy inflating: iter.000002/02.fp/data.003/set.000/force.npy inflating: iter.000002/02.fp/data.003/set.000/energy.npy inflating: iter.000002/02.fp/data.003/set.000/virial.npy inflating: iter.000002/02.fp/data.003/set.000/coord.npy extracting: iter.000002/02.fp/data.003/type_map.raw inflating: iter.000002/02.fp/data.003/energy.raw inflating: iter.000002/02.fp/data.003/force.raw inflating: iter.000002/02.fp/data.003/box.raw inflating: iter.000002/02.fp/data.003/virial.raw inflating: iter.000002/02.fp/data.003/type.raw inflating: iter.000002/02.fp/data.003/coord.raw
代码
文本
[2]
# 查看元素的种类，及coord信息
import numpy as np
!cat ./iter.000000/02.fp/data.000/type_map.raw
for i in range(4):
coord=np.load('./iter.000002/02.fp/data.{:03d}/set.000/coord.npy'.format(i))
print(coord.shape)
Li Ge P S (133, 1200) (133, 1200) (134, 1200) (129, 1200)
代码
文本
[3]
# 打包训练集/测试集
! tar -zcvf train.tgz ./iter.000000 ./iter.000001
! tar -zcvf test.tgz ./iter.000002
./iter.000000/ ./iter.000000/02.fp/ ./iter.000000/02.fp/data.000/ ./iter.000000/02.fp/data.000/box.raw ./iter.000000/02.fp/data.000/coord.raw ./iter.000000/02.fp/data.000/energy.raw ./iter.000000/02.fp/data.000/force.raw ./iter.000000/02.fp/data.000/set.000/ ./iter.000000/02.fp/data.000/set.000/box.npy ./iter.000000/02.fp/data.000/set.000/coord.npy ./iter.000000/02.fp/data.000/set.000/energy.npy ./iter.000000/02.fp/data.000/set.000/force.npy ./iter.000000/02.fp/data.000/set.000/virial.npy ./iter.000000/02.fp/data.000/type.raw ./iter.000000/02.fp/data.000/type_map.raw ./iter.000000/02.fp/data.000/virial.raw ./iter.000000/02.fp/data.001/ ./iter.000000/02.fp/data.001/box.raw ./iter.000000/02.fp/data.001/coord.raw ./iter.000000/02.fp/data.001/energy.raw ./iter.000000/02.fp/data.001/force.raw ./iter.000000/02.fp/data.001/set.000/ ./iter.000000/02.fp/data.001/set.000/box.npy ./iter.000000/02.fp/data.001/set.000/coord.npy ./iter.000000/02.fp/data.001/set.000/energy.npy ./iter.000000/02.fp/data.001/set.000/force.npy ./iter.000000/02.fp/data.001/set.000/virial.npy ./iter.000000/02.fp/data.001/type.raw ./iter.000000/02.fp/data.001/type_map.raw ./iter.000000/02.fp/data.001/virial.raw ./iter.000000/02.fp/data.002/ ./iter.000000/02.fp/data.002/box.raw ./iter.000000/02.fp/data.002/coord.raw ./iter.000000/02.fp/data.002/energy.raw ./iter.000000/02.fp/data.002/force.raw ./iter.000000/02.fp/data.002/set.000/ ./iter.000000/02.fp/data.002/set.000/box.npy ./iter.000000/02.fp/data.002/set.000/coord.npy ./iter.000000/02.fp/data.002/set.000/energy.npy ./iter.000000/02.fp/data.002/set.000/force.npy ./iter.000000/02.fp/data.002/set.000/virial.npy ./iter.000000/02.fp/data.002/type.raw ./iter.000000/02.fp/data.002/type_map.raw ./iter.000000/02.fp/data.002/virial.raw ./iter.000000/02.fp/data.003/ ./iter.000000/02.fp/data.003/box.raw ./iter.000000/02.fp/data.003/coord.raw ./iter.000000/02.fp/data.003/energy.raw ./iter.000000/02.fp/data.003/force.raw ./iter.000000/02.fp/data.003/set.000/ ./iter.000000/02.fp/data.003/set.000/box.npy ./iter.000000/02.fp/data.003/set.000/coord.npy ./iter.000000/02.fp/data.003/set.000/energy.npy ./iter.000000/02.fp/data.003/set.000/force.npy ./iter.000000/02.fp/data.003/set.000/virial.npy ./iter.000000/02.fp/data.003/type.raw ./iter.000000/02.fp/data.003/type_map.raw ./iter.000000/02.fp/data.003/virial.raw ./iter.000001/ ./iter.000001/02.fp/ ./iter.000001/02.fp/data.000/ ./iter.000001/02.fp/data.000/box.raw ./iter.000001/02.fp/data.000/coord.raw ./iter.000001/02.fp/data.000/energy.raw ./iter.000001/02.fp/data.000/force.raw ./iter.000001/02.fp/data.000/set.000/ ./iter.000001/02.fp/data.000/set.000/box.npy ./iter.000001/02.fp/data.000/set.000/coord.npy ./iter.000001/02.fp/data.000/set.000/energy.npy ./iter.000001/02.fp/data.000/set.000/force.npy ./iter.000001/02.fp/data.000/set.000/virial.npy ./iter.000001/02.fp/data.000/type.raw ./iter.000001/02.fp/data.000/type_map.raw ./iter.000001/02.fp/data.000/virial.raw ./iter.000001/02.fp/data.001/ ./iter.000001/02.fp/data.001/box.raw ./iter.000001/02.fp/data.001/coord.raw ./iter.000001/02.fp/data.001/energy.raw ./iter.000001/02.fp/data.001/force.raw ./iter.000001/02.fp/data.001/set.000/ ./iter.000001/02.fp/data.001/set.000/box.npy ./iter.000001/02.fp/data.001/set.000/coord.npy ./iter.000001/02.fp/data.001/set.000/energy.npy ./iter.000001/02.fp/data.001/set.000/force.npy ./iter.000001/02.fp/data.001/set.000/virial.npy ./iter.000001/02.fp/data.001/type.raw ./iter.000001/02.fp/data.001/type_map.raw ./iter.000001/02.fp/data.001/virial.raw ./iter.000001/02.fp/data.002/ ./iter.000001/02.fp/data.002/box.raw ./iter.000001/02.fp/data.002/coord.raw ./iter.000001/02.fp/data.002/energy.raw ./iter.000001/02.fp/data.002/force.raw ./iter.000001/02.fp/data.002/set.000/ ./iter.000001/02.fp/data.002/set.000/box.npy ./iter.000001/02.fp/data.002/set.000/coord.npy ./iter.000001/02.fp/data.002/set.000/energy.npy ./iter.000001/02.fp/data.002/set.000/force.npy ./iter.000001/02.fp/data.002/set.000/virial.npy ./iter.000001/02.fp/data.002/type.raw ./iter.000001/02.fp/data.002/type_map.raw ./iter.000001/02.fp/data.002/virial.raw ./iter.000001/02.fp/data.003/ ./iter.000001/02.fp/data.003/box.raw ./iter.000001/02.fp/data.003/coord.raw ./iter.000001/02.fp/data.003/energy.raw ./iter.000001/02.fp/data.003/force.raw ./iter.000001/02.fp/data.003/set.000/ ./iter.000001/02.fp/data.003/set.000/box.npy ./iter.000001/02.fp/data.003/set.000/coord.npy ./iter.000001/02.fp/data.003/set.000/energy.npy ./iter.000001/02.fp/data.003/set.000/force.npy ./iter.000001/02.fp/data.003/set.000/virial.npy ./iter.000001/02.fp/data.003/type.raw ./iter.000001/02.fp/data.003/type_map.raw ./iter.000001/02.fp/data.003/virial.raw ./iter.000002/ ./iter.000002/02.fp/ ./iter.000002/02.fp/data.000/ ./iter.000002/02.fp/data.000/box.raw ./iter.000002/02.fp/data.000/coord.raw ./iter.000002/02.fp/data.000/energy.raw ./iter.000002/02.fp/data.000/force.raw ./iter.000002/02.fp/data.000/set.000/ ./iter.000002/02.fp/data.000/set.000/box.npy ./iter.000002/02.fp/data.000/set.000/coord.npy ./iter.000002/02.fp/data.000/set.000/energy.npy ./iter.000002/02.fp/data.000/set.000/force.npy ./iter.000002/02.fp/data.000/set.000/virial.npy ./iter.000002/02.fp/data.000/type.raw ./iter.000002/02.fp/data.000/type_map.raw ./iter.000002/02.fp/data.000/virial.raw ./iter.000002/02.fp/data.001/ ./iter.000002/02.fp/data.001/box.raw ./iter.000002/02.fp/data.001/coord.raw ./iter.000002/02.fp/data.001/energy.raw ./iter.000002/02.fp/data.001/force.raw ./iter.000002/02.fp/data.001/set.000/ ./iter.000002/02.fp/data.001/set.000/box.npy ./iter.000002/02.fp/data.001/set.000/coord.npy ./iter.000002/02.fp/data.001/set.000/energy.npy ./iter.000002/02.fp/data.001/set.000/force.npy ./iter.000002/02.fp/data.001/set.000/virial.npy ./iter.000002/02.fp/data.001/type.raw ./iter.000002/02.fp/data.001/type_map.raw ./iter.000002/02.fp/data.001/virial.raw ./iter.000002/02.fp/data.002/ ./iter.000002/02.fp/data.002/box.raw ./iter.000002/02.fp/data.002/coord.raw ./iter.000002/02.fp/data.002/energy.raw ./iter.000002/02.fp/data.002/force.raw ./iter.000002/02.fp/data.002/set.000/ ./iter.000002/02.fp/data.002/set.000/box.npy ./iter.000002/02.fp/data.002/set.000/coord.npy ./iter.000002/02.fp/data.002/set.000/energy.npy ./iter.000002/02.fp/data.002/set.000/force.npy ./iter.000002/02.fp/data.002/set.000/virial.npy ./iter.000002/02.fp/data.002/type.raw ./iter.000002/02.fp/data.002/type_map.raw ./iter.000002/02.fp/data.002/virial.raw ./iter.000002/02.fp/data.003/ ./iter.000002/02.fp/data.003/box.raw ./iter.000002/02.fp/data.003/coord.raw ./iter.000002/02.fp/data.003/energy.raw ./iter.000002/02.fp/data.003/force.raw ./iter.000002/02.fp/data.003/set.000/ ./iter.000002/02.fp/data.003/set.000/box.npy ./iter.000002/02.fp/data.003/set.000/coord.npy ./iter.000002/02.fp/data.003/set.000/energy.npy ./iter.000002/02.fp/data.003/set.000/force.npy ./iter.000002/02.fp/data.003/set.000/virial.npy ./iter.000002/02.fp/data.003/type.raw ./iter.000002/02.fp/data.003/type_map.raw ./iter.000002/02.fp/data.003/virial.raw
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2. Sub Model1：从头训练
2.1 提交任务
① 选择模块1后，我们将npy文件的打包文件进行上传，作为初始的训练集和测试集
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② Job Option对训练的基本参数进行设置。
使用APP方便/新手友好的一点是：开发者已经设定好了基本的参数项，我们可以直接使用/微调就可以一键提交【“点点点”就可一把提交】;
对于资深用户，如果想自定义设置，也可选择Train Config - Options//Dp Train Custom Options；在train config部分灵活填写合适的参数
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在设置完成③/④Bohrium相关的系统设置，就已经提交结束
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2.2 下载模型/查看Reports
在任务正常结束后，我们可以去“My Jobs”查找对应结果。
下载模型：Outputs/graph.pb
此外，我们可以在Reports模块看到RMSE,lcurve随训练时间的变化。
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2.3 模型检验
这一部分的内容与以往对模型的检验操作类似，可以借助dp test实现
- 此处使用笔者提前在APP上训练了100w步的模型进行测试
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[4]
#模型检验
! cp -r /bohr/0914-yiic/v5/01_train .
! dp test -m ./01_train/graph.pb -s ./iter.000002 -d results_train
WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. Instructions for updating: non-resource variables are not supported in the long term WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. WARNING:root:Environment variable KMP_BLOCKTIME is empty. Use the default value 0 WARNING:root:Environment variable KMP_AFFINITY is empty. Use the default value granularity=fine,verbose,compact,1,0 /opt/deepmd-kit-2.2.1/lib/python3.10/importlib/__init__.py:169: UserWarning: The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged. _bootstrap._exec(spec, module) WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.config.list_physical_devices('GPU')` instead. WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.config.list_physical_devices('GPU')` instead. DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.000 DEEPMD INFO Adjust batch size from 1024 to 2048 DEEPMD INFO Adjust batch size from 2048 to 4096 DEEPMD INFO Adjust batch size from 4096 to 8192 DEEPMD INFO Adjust batch size from 8192 to 16384 DEEPMD INFO Adjust batch size from 16384 to 32768 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 2.612886e-01 eV DEEPMD INFO Energy RMSE : 3.202538e-01 eV DEEPMD INFO Energy MAE/Natoms : 6.532216e-04 eV DEEPMD INFO Energy RMSE/Natoms : 8.006344e-04 eV DEEPMD INFO Force MAE : 5.866493e-02 eV/A DEEPMD INFO Force RMSE : 7.968200e-02 eV/A DEEPMD INFO Virial MAE : 2.741458e+00 eV DEEPMD INFO Virial RMSE : 3.772022e+00 eV DEEPMD INFO Virial MAE/Natoms : 6.853645e-03 eV DEEPMD INFO Virial RMSE/Natoms : 9.430056e-03 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.001 DEEPMD INFO Adjust batch size from 32768 to 65536 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 2.766609e-01 eV DEEPMD INFO Energy RMSE : 3.549181e-01 eV DEEPMD INFO Energy MAE/Natoms : 6.916522e-04 eV DEEPMD INFO Energy RMSE/Natoms : 8.872951e-04 eV DEEPMD INFO Force MAE : 5.878417e-02 eV/A DEEPMD INFO Force RMSE : 7.965180e-02 eV/A DEEPMD INFO Virial MAE : 2.485362e+00 eV DEEPMD INFO Virial RMSE : 3.431657e+00 eV DEEPMD INFO Virial MAE/Natoms : 6.213406e-03 eV DEEPMD INFO Virial RMSE/Natoms : 8.579142e-03 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.002 2023-09-19 11:22:01.208705: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 366.21MiB (rounded to 384000000)requested by op load/gradients/Slice_6_grad/Pad If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. Current allocation summary follows. Current allocation summary follows. 2023-09-19 11:22:01.209161: W tensorflow/core/common_runtime/bfc_allocator.cc:491] ********************************************************_******************************************* 2023-09-19 11:22:01.209224: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at pad_op.cc:130 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[100,400,1200] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc 2023-09-19 11:22:11.209469: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 366.21MiB (rounded to 384000000)requested by op load/gradients/Slice_7_grad/Pad If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. Current allocation summary follows. Current allocation summary follows. 2023-09-19 11:22:11.209855: W tensorflow/core/common_runtime/bfc_allocator.cc:491] ********************************************************_******************************************* 2023-09-19 11:22:11.209895: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at pad_op.cc:130 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[100,400,1200] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc 2023-09-19 11:22:21.210099: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 366.21MiB (rounded to 384000000)requested by op load/gradients/Slice_8_grad/Pad If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. Current allocation summary follows. Current allocation summary follows. 2023-09-19 11:22:21.210494: W tensorflow/core/common_runtime/bfc_allocator.cc:491] *************************************************__*****_******************************************* 2023-09-19 11:22:21.210535: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at pad_op.cc:130 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[100,400,1200] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc DEEPMD INFO Adjust batch size from 65536 to 32768 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 2.341408e-01 eV DEEPMD INFO Energy RMSE : 2.884737e-01 eV DEEPMD INFO Energy MAE/Natoms : 5.853519e-04 eV DEEPMD INFO Energy RMSE/Natoms : 7.211843e-04 eV DEEPMD INFO Force MAE : 5.864011e-02 eV/A DEEPMD INFO Force RMSE : 7.960784e-02 eV/A DEEPMD INFO Virial MAE : 2.346456e+00 eV DEEPMD INFO Virial RMSE : 3.233939e+00 eV DEEPMD INFO Virial MAE/Natoms : 5.866140e-03 eV DEEPMD INFO Virial RMSE/Natoms : 8.084848e-03 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.003 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 2.619621e-01 eV DEEPMD INFO Energy RMSE : 3.414595e-01 eV DEEPMD INFO Energy MAE/Natoms : 6.549052e-04 eV DEEPMD INFO Energy RMSE/Natoms : 8.536487e-04 eV DEEPMD INFO Force MAE : 5.856228e-02 eV/A DEEPMD INFO Force RMSE : 7.955180e-02 eV/A DEEPMD INFO Virial MAE : 2.174577e+00 eV DEEPMD INFO Virial RMSE : 2.966414e+00 eV DEEPMD INFO Virial MAE/Natoms : 5.436443e-03 eV DEEPMD INFO Virial RMSE/Natoms : 7.416036e-03 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ----------weighted average of errors----------- DEEPMD INFO # number of systems : 4 DEEPMD INFO Energy MAE : 2.585131e-01 eV DEEPMD INFO Energy RMSE : 3.272388e-01 eV DEEPMD INFO Energy MAE/Natoms : 6.462827e-04 eV DEEPMD INFO Energy RMSE/Natoms : 8.180970e-04 eV DEEPMD INFO Force MAE : 5.866287e-02 eV/A DEEPMD INFO Force RMSE : 7.962338e-02 eV/A DEEPMD INFO Virial MAE : 2.436963e+00 eV DEEPMD INFO Virial RMSE : 3.363867e+00 eV DEEPMD INFO Virial MAE/Natoms : 6.092409e-03 eV DEEPMD INFO Virial RMSE/Natoms : 8.409667e-03 eV DEEPMD INFO # -----------------------------------------------
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[5]
import numpy as np
import matplotlib.pyplot as plt
# 定义绘制散点图和对角线的函数
def plot(ax, data, key, xlabel, ylabel, min_val, max_val,RMSE):
data_key = f'data_{key}'
pred_key = f'pred_{key}'
ax.scatter(data[data_key], data[pred_key], label=key, s=6)
if type(RMSE) != list:
ax.text(0.1, 0.8, 'RMSE: {:.4e}'.format(RMSE), transform=ax.transAxes, color='blue', size=14)
else:
ax.text(0.05, 0.6, 'RMSE: \nfx:{:.4e}\nfy:{:.4e}\nfz:{:.4e}'.format(RMSE[0],RMSE[1],RMSE[2]), transform=ax.transAxes, color='blue', size=12)
ax.legend()
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_xlim(min_val, max_val)
ax.set_ylim(min_val, max_val)
ax.plot([min_val, max_val], [min_val, max_val], 'r', lw=1)
# 使用dptest测试
num_list = ['train']
results = 'test_results'
# 读取数据,并对e_peratom数据
for num in num_list:
print("正在处理的文件: result_{}.e_peratom.out".format(num))
data_e = np.genfromtxt("./results_{}.e_peratom.out".format(num), names=["data_e", "pred_e"])
data_f = np.genfromtxt("./results_{}.f.out".format(num), names=["data_fx", "data_fy", "data_fz", "pred_fx", "pred_fy", "pred_fz"])
# 计算能量和力的RMSE
RMSE_f=[]
RMSE_e=np.linalg.norm(data_e['data_e']-data_e['pred_e'], ord=2)/len(data_e['data_e'])**0.5
for j in ['fx', 'fy', 'fz']:
RMSE_f.append((np.linalg.norm(data_f['data_{}'.format(j)]-data_f['pred_{}'.format(j)], ord=2)/len('data_{}'.format(j)))**0.5)
# 计算e和f的最小值和最大值
data_e_stacked = np.column_stack((data_e['data_e'], data_e['pred_e']))
data_f_stacked = np.column_stack((data_f['data_fx'], data_f['data_fy'], data_f['data_fz'], data_f['pred_fx'], data_f['pred_fy'], data_f['pred_fz']))
min_val_e, max_val_e = np.min(data_e_stacked), np.max(data_e_stacked)
min_val_f, max_val_f = np.min(data_f_stacked), np.max(data_f_stacked)
# 绘制散点图并保存结果
fig, axs = plt.subplots(1, 2, figsize=(12, 5))
plot(axs[0], data_e, 'e', 'DFT energy (eV/atom)', 'DP energy (eV/atom)', min_val_e, max_val_e, RMSE_e)
for force_direction in ['fx', 'fy', 'fz']:
plot(axs[1], data_f, force_direction, 'DFT force (eV/Å)', 'DP force (eV/Å)', min_val_f, max_val_f, RMSE_f)
print('输出位置：./dp_test_{}.png'.format(num))
plt.savefig('./dp_test_{}.png'.format(num), dpi=300)
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从输出的RMSE结果判断，我们的模型取得了较好的精度。对于模型的后处理及使用与以往手段并无差别。
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3. Sub Model2：微调训练
3.1 提交任务
与上一节的提交流程一致，我们上传数据包后就可通过点击的方式进行微调训练。
正如之前提到的微调训练是基于一个预训练模型开展的训练，APP已经内置好了多种类型的初始分支，我们可以选择体系相近的分支进行训练；
当然，若暂无合适的体系，直接选择random即可。
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APP上的微调模型是基于DPA2描述符，是否蒸馏取决于我们的需求~
- 注：默认参数可以选择不调整!
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💭势函数蒸馏作用：
- 知识蒸馏，将教师网络(teacher network)的知识迁移到学生网络(student network)上，使得学生网络的性能表现如教师网络一般。
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[6]
# 模型检验
! cp -r /bohr/0914-yiic/v5/02_finetune .
! dp test -m ./02_finetune/distilled_model.pb -s ./iter.000002 -d results_finetune
WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/tensorflow/python/compat/v2_compat.py:107: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version. Instructions for updating: non-resource variables are not supported in the long term WARNING:root:To get the best performance, it is recommended to adjust the number of threads by setting the environment variables OMP_NUM_THREADS, TF_INTRA_OP_PARALLELISM_THREADS, and TF_INTER_OP_PARALLELISM_THREADS. See https://deepmd.rtfd.io/parallelism/ for more information. WARNING:root:Environment variable KMP_BLOCKTIME is empty. Use the default value 0 WARNING:root:Environment variable KMP_AFFINITY is empty. Use the default value granularity=fine,verbose,compact,1,0 /opt/deepmd-kit-2.2.1/lib/python3.10/importlib/__init__.py:169: UserWarning: The NumPy module was reloaded (imported a second time). This can in some cases result in small but subtle issues and is discouraged. _bootstrap._exec(spec, module) WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.config.list_physical_devices('GPU')` instead. WARNING:tensorflow:From /opt/deepmd-kit-2.2.1/lib/python3.10/site-packages/deepmd/utils/batch_size.py:61: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version. Instructions for updating: Use `tf.config.list_physical_devices('GPU')` instead. DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.000 DEEPMD INFO Adjust batch size from 1024 to 2048 DEEPMD INFO Adjust batch size from 2048 to 4096 DEEPMD INFO Adjust batch size from 4096 to 8192 DEEPMD INFO Adjust batch size from 8192 to 16384 DEEPMD INFO Adjust batch size from 16384 to 32768 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 8.954894e-01 eV DEEPMD INFO Energy RMSE : 1.116630e+00 eV DEEPMD INFO Energy MAE/Natoms : 2.238723e-03 eV DEEPMD INFO Energy RMSE/Natoms : 2.791575e-03 eV DEEPMD INFO Force MAE : 7.667443e-02 eV/A DEEPMD INFO Force RMSE : 1.022249e-01 eV/A DEEPMD INFO Virial MAE : 3.445782e+00 eV DEEPMD INFO Virial RMSE : 5.086508e+00 eV DEEPMD INFO Virial MAE/Natoms : 8.614454e-03 eV DEEPMD INFO Virial RMSE/Natoms : 1.271627e-02 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.001 2023-09-19 11:22:52.601350: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.90GiB (rounded to 3110400000)requested by op load/filter_type_all/MatMul_2 If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. Current allocation summary follows. Current allocation summary follows. 2023-09-19 11:22:52.601560: W tensorflow/core/common_runtime/bfc_allocator.cc:491] _______________****************__________________________****************_____*********__******__*** 2023-09-19 11:22:52.601600: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at matmul_op_impl.h:681 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[3888000,100] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc 2023-09-19 11:22:52.717161: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.90GiB (rounded to 3110400000)requested by op load/filter_type_all/embedding_lookup If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. Current allocation summary follows. Current allocation summary follows. 2023-09-19 11:22:52.717373: W tensorflow/core/common_runtime/bfc_allocator.cc:491] _______________****************__________________________****************_____*********__******__*** 2023-09-19 11:22:52.717414: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at gather_op.cc:158 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[3888000,100] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc 2023-09-19 11:23:02.601746: W tensorflow/core/common_runtime/bfc_allocator.cc:479] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.90GiB (rounded to 3110400000)requested by op load/filter_type_all/concat_1 If the cause is memory fragmentation maybe the environment variable 'TF_GPU_ALLOCATOR=cuda_malloc_async' will improve the situation. Current allocation summary follows. Current allocation summary follows. 2023-09-19 11:23:02.601956: W tensorflow/core/common_runtime/bfc_allocator.cc:491] _______________****************__________________________****************_____*********__******__*** 2023-09-19 11:23:02.601996: W tensorflow/core/framework/op_kernel.cc:1745] OP_REQUIRES failed at concat_op.cc:158 : RESOURCE_EXHAUSTED: OOM when allocating tensor with shape[3888000,100] and type double on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc DEEPMD INFO Adjust batch size from 32768 to 16384 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 8.667567e-01 eV DEEPMD INFO Energy RMSE : 1.114373e+00 eV DEEPMD INFO Energy MAE/Natoms : 2.166892e-03 eV DEEPMD INFO Energy RMSE/Natoms : 2.785932e-03 eV DEEPMD INFO Force MAE : 7.741913e-02 eV/A DEEPMD INFO Force RMSE : 1.032156e-01 eV/A DEEPMD INFO Virial MAE : 3.952008e+00 eV DEEPMD INFO Virial RMSE : 5.823287e+00 eV DEEPMD INFO Virial MAE/Natoms : 9.880020e-03 eV DEEPMD INFO Virial RMSE/Natoms : 1.455822e-02 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.002 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 7.774230e-01 eV DEEPMD INFO Energy RMSE : 1.014263e+00 eV DEEPMD INFO Energy MAE/Natoms : 1.943558e-03 eV DEEPMD INFO Energy RMSE/Natoms : 2.535657e-03 eV DEEPMD INFO Force MAE : 7.685530e-02 eV/A DEEPMD INFO Force RMSE : 1.026521e-01 eV/A DEEPMD INFO Virial MAE : 4.466531e+00 eV DEEPMD INFO Virial RMSE : 6.507664e+00 eV DEEPMD INFO Virial MAE/Natoms : 1.116633e-02 eV DEEPMD INFO Virial RMSE/Natoms : 1.626916e-02 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ---------------output of dp test--------------- DEEPMD INFO # testing system : iter.000002/02.fp/data.003 DEEPMD INFO # number of test data : 100 DEEPMD INFO Energy MAE : 8.282735e-01 eV DEEPMD INFO Energy RMSE : 1.011050e+00 eV DEEPMD INFO Energy MAE/Natoms : 2.070684e-03 eV DEEPMD INFO Energy RMSE/Natoms : 2.527625e-03 eV DEEPMD INFO Force MAE : 7.698900e-02 eV/A DEEPMD INFO Force RMSE : 1.027105e-01 eV/A DEEPMD INFO Virial MAE : 4.992125e+00 eV DEEPMD INFO Virial RMSE : 7.313838e+00 eV DEEPMD INFO Virial MAE/Natoms : 1.248031e-02 eV DEEPMD INFO Virial RMSE/Natoms : 1.828459e-02 eV DEEPMD INFO # ----------------------------------------------- DEEPMD INFO # ----------weighted average of errors----------- DEEPMD INFO # number of systems : 4 DEEPMD INFO Energy MAE : 8.419856e-01 eV DEEPMD INFO Energy RMSE : 1.065322e+00 eV DEEPMD INFO Energy MAE/Natoms : 2.104964e-03 eV DEEPMD INFO Energy RMSE/Natoms : 2.663304e-03 eV DEEPMD INFO Force MAE : 7.698446e-02 eV/A DEEPMD INFO Force RMSE : 1.027014e-01 eV/A DEEPMD INFO Virial MAE : 4.214111e+00 eV DEEPMD INFO Virial RMSE : 6.237490e+00 eV DEEPMD INFO Virial MAE/Natoms : 1.053528e-02 eV DEEPMD INFO Virial RMSE/Natoms : 1.559373e-02 eV DEEPMD INFO # -----------------------------------------------
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[7]
# 使用dptest测试
num_list = ['finetune']
results = 'test_results'
# 读取数据,并对e_peratom数据
for num in num_list:
print("正在处理的文件: result_{}.e_peratom.out".format(num))
data_e = np.genfromtxt("./results_{}.e_peratom.out".format(num), names=["data_e", "pred_e"])
data_f = np.genfromtxt("./results_{}.f.out".format(num), names=["data_fx", "data_fy", "data_fz", "pred_fx", "pred_fy", "pred_fz"])
# 计算能量和力的RMSE
RMSE_f=[]
RMSE_e=np.linalg.norm(data_e['data_e']-data_e['pred_e'], ord=2)/len(data_e['data_e'])**0.5
for j in ['fx', 'fy', 'fz']:
RMSE_f.append((np.linalg.norm(data_f['data_{}'.format(j)]-data_f['pred_{}'.format(j)], ord=2)/len('data_{}'.format(j)))**0.5)
# 计算e和f的最小值和最大值
data_e_stacked = np.column_stack((data_e['data_e'], data_e['pred_e']))
data_f_stacked = np.column_stack((data_f['data_fx'], data_f['data_fy'], data_f['data_fz'], data_f['pred_fx'], data_f['pred_fy'], data_f['pred_fz']))
min_val_e, max_val_e = np.min(data_e_stacked), np.max(data_e_stacked)
min_val_f, max_val_f = np.min(data_f_stacked), np.max(data_f_stacked)
# 绘制散点图并保存结果
fig, axs = plt.subplots(1, 2, figsize=(12, 5))
plot(axs[0], data_e, 'e', 'DFT energy (eV/atom)', 'DP energy (eV/atom)', min_val_e, max_val_e, RMSE_e)
for force_direction in ['fx', 'fy', 'fz']:
plot(axs[1], data_f, force_direction, 'DFT force (eV/Å)', 'DP force (eV/Å)', min_val_f, max_val_f, RMSE_f)
print('输出位置：./dp_test_{}.png'.format(num))
plt.savefig('./dp_test_{}.png'.format(num), dpi=300)
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注：这里的DPA训练步数为10w步，上一节的从头训为100w步
100w DPA2训练效果待更新
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