中
Hands-on to APEX (v1.2) on Bohrium
zhuoyli@connect.hku.hk
更新于 2025-04-01
推荐镜像 :Basic Image:ubuntu22.04-py3.10
推荐机型 :c2_m4_cpu
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目录
数据集
apex_demo(v3)
Hands-on to APEX (v1.2) on Bohrium
©️ Copyright 2024 @ Authors
作者：
zhuoyli@connect.hku.hk 📨
日期：2024-03-15
共享协议：本作品采用知识共享署名-非商业性使用-相同方式共享 4.0 国际许可协议进行许可。
快速开始：点击上方的 开始连接 按钮，选择 APEX Demo 项目 ubuntu22.04-py3.10 镜像 和任意配置机型即可开始。
📖 Getting Started Guide
This document can be executed directly on the Bohrium Notebook. To begin, click the Connect button located at the top of the interface, then select APEX Demo project and the ubuntu22.04-py3.10 Image and choose your desired machine configuration to proceed.
This Bohrium notebook demonstrates how to perform alloy properties simulation workflow via APEX (v1.2). The dflow server and simulation resources of this case are based on the Bohrium.
If you have any problems about the APEX, issues are welcomed in GitHub repository
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Installation
Installation of APEX can be easily achieved through pip install pydflow or directly from source code:
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!if [ -e APEX ];then rm -rf APEX; fi; # remove APEX package if exist
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!git clone https://github.com/deepmodeling/APEX.git # clone APEX source code from GitHub repository
Cloning into 'APEX'... remote: Enumerating objects: 3946, done. remote: Counting objects: 100% (957/957), done. remote: Compressing objects: 100% (318/318), done. remote: Total 3946 (delta 713), reused 678 (delta 639), pack-reused 2989 (from 3) Receiving objects: 100% (3946/3946), 18.94 MiB | 4.20 MiB/s, done. Resolving deltas: 100% (2657/2657), done.
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!pip install pip --upgrade -i https://pypi.tuna.tsinghua.edu.cn/simple # upgrade pip
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Requirement already satisfied: pip in /opt/mamba/lib/python3.10/site-packages (22.3) Collecting pip Downloading https://pypi.tuna.tsinghua.edu.cn/packages/c9/bc/b7db44f5f39f9d0494071bddae6880eb645970366d0a200022a1a93d57f5/pip-25.0.1-py3-none-any.whl (1.8 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 2.2 MB/s eta 0:00:0000:0100:01m Installing collected packages: pip Attempting uninstall: pip Found existing installation: pip 22.3 Uninstalling pip-22.3: Successfully uninstalled pip-22.3 Successfully installed pip-25.0.1 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv
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pip install APEX/ -i https://pypi.tuna.tsinghua.edu.cn/simple # install apex-flow (APEX)
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple Processing /APEX Preparing metadata (setup.py) ... done Requirement already satisfied: numpy<2.0.0 in /opt/mamba/lib/python3.10/site-packages (from apex-flow==1.2.14) (1.24.2) Collecting pydflow>=1.7.83 (from apex-flow==1.2.14) Downloading https://pypi.tuna.tsinghua.edu.cn/packages/4b/2b/a46efe175cf28e87fcb7cbe3884d0e8e7303658b800a53b387d521a6fc52/pydflow-1.8.108-py3-none-any.whl (179 kB) Collecting pymatgen>=2023.8.10 (from apex-flow==1.2.14) Downloading https://pypi.tuna.tsinghua.edu.cn/packages/28/04/5a46a6af231bf6a5a155367fe983a2b2ee724bb8319d026b98c0b6f47dbc/pymatgen-2025.3.10-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.0 MB) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 5.0/5.0 MB 1.8 MB/s eta 0:00:00a 0:00:01 Collecting pymatgen-analysis-defects>=2023.8.22 (from apex-flow==1.2.14) Downloading 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requests 2.28.1 Uninstalling requests-2.28.1: Successfully uninstalled requests-2.28.1 Attempting uninstall: numpy Found existing installation: numpy 1.24.2 Uninstalling numpy-1.24.2: Successfully uninstalled numpy-1.24.2 Attempting uninstall: scipy Found existing installation: scipy 1.10.1 Uninstalling scipy-1.10.1: Successfully uninstalled scipy-1.10.1 Attempting uninstall: pandas Found existing installation: pandas 1.5.3 Uninstalling pandas-1.5.3: Successfully uninstalled pandas-1.5.3 Attempting uninstall: monty Found existing installation: monty 2022.9.9 Uninstalling monty-2022.9.9: Successfully uninstalled monty-2022.9.9 Attempting uninstall: dpdata Found existing installation: dpdata 0.2.14 Uninstalling dpdata-0.2.14: Successfully uninstalled dpdata-0.2.14 Successfully installed Flask-3.0.3 Werkzeug-3.0.6 apex-flow-1.2.14 argo-workflows-5.0.0 ase-3.24.0 blinker-1.9.0 boto3-1.37.24 botocore-1.37.24 cachetools-5.5.2 click-8.1.8 cloudpickle-2.2.0 contourpy-1.3.1 cycler-0.12.1 dash-3.0.1 dash_bootstrap_components-2.0.0 dnspython-2.7.0 dpdata-0.2.17 durationpy-0.9 filelock-3.18.0 fonttools-4.56.0 fpop-0.0.8 google-auth-2.38.0 imageio-2.37.0 importlib-metadata-8.6.1 itsdangerous-2.2.0 joblib-1.4.2 jsonpickle-4.0.5 kiwisolver-1.4.8 kubernetes-32.0.1 latexcodec-3.0.0 lazy-loader-0.4 matplotlib-3.10.1 minio-7.2.15 monty-2025.3.3 mp-pyrho-0.4.5 mpmath-1.3.0 narwhals-1.33.0 networkx-3.4.2 numpy-1.26.4 oauthlib-3.2.2 palettable-3.3.3 pandas-2.2.3 phonopy-2.38.0 pillow-11.1.0 plotly-6.0.1 pyasn1-0.6.1 pyasn1-modules-0.4.2 pybtex-0.24.0 pydflow-1.8.108 pymatgen-2025.3.10 pymatgen-analysis-defects-2025.1.18 pymongo-4.11.3 pyparsing-3.2.3 requests-2.32.3 requests-oauthlib-2.0.0 retrying-1.3.4 rsa-4.9 ruamel.yaml-0.18.10 ruamel.yaml.clib-0.2.12 s3transfer-0.11.4 scikit-image-0.25.2 scipy-1.15.2 seekpath-2.1.0 spglib-2.6.0 symfc-1.3.4 sympy-1.13.3 tabulate-0.9.0 tifffile-2025.3.30 typing-extensions-4.13.0 tzdata-2025.2 uncertainties-3.2.2 zipp-3.21.0 WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager, possibly rendering your system unusable. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv. Use the --root-user-action option if you know what you are doing and want to suppress this warning. Note: you may need to restart the kernel to use updated packages.
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Check whether APEX is properly installed by version check:
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[5]
!apex --version
APEX v1.2.14
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LAMMPS Case Demonstration
1. Prepare work directory
Here we will use a DP potential [1] of titanium to do MD calculation for elastic properties and equation of state curve (EOS) of Ti for HCP, FCC and BCC phase respectively with one joint APEX workflow.
Firstly, change to the prepared example work directory:
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[6]
!cp -r /bohr/apex-demo-kaay/v3/DP_Ti_case . # copy example work directory (DP_Ti_case)
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[7]
cd ./DP_Ti_case
/DP_Ti_case
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Use tree to see current file structure:
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[8]
!sudo apt install tree # install tree command
/usr/bin/sh: 1: sudo: not found
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[9]
!tree
. ├── Ti_frozen_model.pb ├── confs │ ├── std-bcc │ │ └── POSCAR │ ├── std-fcc │ │ └── POSCAR │ └── std-hcp │ └── POSCAR ├── global_bohrium.json └── param_joint.json 4 directories, 6 files
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As seen from tree output above, four elements are prepared under current work directory:
- confs: contains sub-pathes for initial POSCAR for HCP, FCC and BCC Ti seperately.
- Ti_frozen_model.pb: DP potential file of Ti [1] for MD simulation
- global_bohrium.json:
JSONparameter file for global configuration settings including image name, machine info, computing resources and run command et al. (Note: a temporary Bohrium account has been configured only for demonstration. One can also substitute to use their own account and project of Bohrium platform) - param_joint.json:
JSONparameter file for specific calculation settings of thejointworkflow
we can cat to see them one by one (detailed parameter introduction can be found at GitHub repository):
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[10]
!cat global_bohrium.json # browse global_bohrium.json
{
"dflow_host": "https://workflows.deepmodeling.com",
"k8s_api_server": "https://workflows.deepmodeling.com",
"email": "a931126924@outlook.com",
"password": "1tmpaccountpasswd!",
"program_id": 26924,
"apex_image_name":"registry.dp.tech/dptech/prod-11045/apex-dependency:1.2.0",
"lammps_image_name": "registry.dp.tech/dptech/prod-11045/deepmdkit-phonolammps:2.1.1",
"group_size": 3,
"pool_size": 1,
"run_command":"lmp -in in.lammps",
"batch_type": "Bohrium",
"context_type": "Bohrium",
"scass_type":"c16_m62_1 * NVIDIA T4"
}
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Before Submission:
Please input your Bohrium
account information and valid program_id with enough
remaining balance by running following python code:
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[11]
import os
import json
import getpass
from monty.serialization import loadfn, dumpfn
j = loadfn('global_bohrium.json')
j['email'] = getpass.getpass("Email: ")
j['phone'] = getpass.getpass("Phone: ")
j['password'] = getpass.getpass("Password: ")
j['program_id'] = int(os.environ.get("BOHRIUM_PROJECT_ID"))
j['scass_type'] = "1 * NVIDIA P100_16g"
with open('global_bohrium.json', 'w') as f: json.dump(j, f, indent=4)
Email:
Phone:
Password:
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[12]
!cat param_joint.json # browse param_joint.json
{
"structures": ["confs/std-*"],
"interaction": {
"type": "deepmd",
"model": "Ti_frozen_model.pb",
"type_map": {"Ti": 0}
},
"relaxation": {
"cal_setting": {"etol": 0,
"ftol": 1e-10,
"maxiter": 5000,
"maximal": 500000}
},
"properties": [
{
"type": "eos",
"skip": false,
"vol_start": 0.6,
"vol_end": 1.4,
"vol_step": 0.05,
"cal_setting": {"etol": 0,
"ftol": 1e-10}
},
{
"type": "elastic",
"skip": false,
"norm_deform": 1e-2,
"shear_deform": 1e-2,
"cal_setting": {"etol": 0,
"ftol": 1e-10}
}
]
}
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2. Submit workflow
Since eos and elastic has been turned on, we can directly submit a joint workflow by passing two parameter files to apex submit command:
Note:
- Once the workflow is submitted, one can monitor it on the Argo UI via the workflow link.
- DO NOT directly click the workflow link as it may override current page. Please open it in a new page (You may required to input account email and password again to access the Argo workflow)
- Once a sub-workflow finished, its results will be retrieved automatically to local.
- Wait for all seven sub-workflows to complete (totally takes about 5 - 10 minutes)
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[13]
!apex submit param_joint.json -c global_bohrium.json # run the command to submit the workflow
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==>> Alloy Property EXplorer using simulations (v1.2.14)
Please cite DOI: 10.48550/arXiv.2404.17330
Li et al, An extendable cloud-native alloy property explorer (2024).
See https://github.com/deepmodeling/APEX for more information.
---------------------------------------------------------------
Checking input files...
-------Submit Workflow Mode-------
Running APEX calculation via lammps
Submitting joint workflow...
INFO:root:Working on: /DP_Ti_case
Workflow has been submitted (ID: dp-ti-case-joint-w5sg6, UID: 3d4020a6-130d-4684-8105-571d7a2a3b4b)
Workflow link: https://workflows.deepmodeling.com/workflows/argo/dp-ti-case-joint-w5sg6
Waiting for relaxation result...
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3. Check results
Once all workflows finished, all results will be archived to all_result.json under current directory by default.
we can tree again to see if all finished tasks are back to local directory:
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!tree -L 4
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One can download all_result.json to local computer with interactive UI interface to call APEX visualization report function by apex report -w all_result.json.
Here we will just use some simple scripts to check the results:
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elastic_hcp = 'confs/std-hcp/elastic_00/result.out'
elastic_bcc = 'confs/std-bcc/elastic_00/result.out'
elastic_fcc = 'confs/std-fcc/elastic_00/result.out'
with open(elastic_hcp, 'r') as f1: print(f1.read())
with open(elastic_bcc, 'r') as f1: print(f1.read())
with open(elastic_fcc, 'r') as f1: print(f1.read())
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import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate
# read & prepare data
def prep(l):
line = np.loadtxt(l, dtype=float, skiprows=2)
x = line[4:-4, 0]
y = line[4:-4, 1]
x_m = np.linspace(line[0, 0], line[-1, 0], 100)
Spline = interpolate.make_interp_spline(x, y)
y_m = Spline(x_m)
return x_m, y_m
eos_hcp = 'confs/std-hcp/eos_00/result.out'
eos_bcc = 'confs/std-bcc/eos_00/result.out'
eos_fcc = 'confs/std-fcc/eos_00/result.out'
hcp_x, hcp_y = prep(eos_hcp)
bcc_x, bcc_y = prep(eos_bcc)
fcc_x, fcc_y = prep(eos_fcc)
# plot figures
plt.figure(figsize=(25, 5))
plt.subplot(1,3,1)
plt.plot(hcp_x, hcp_y, 'g-')
plt.xlabel('volume per atom $(\AA^3)$')
plt.ylabel('Energy per atom E (eV/atom)')
plt.title('HCP')
plt.subplot(1,3,2)
plt.plot(bcc_x, bcc_y, 'r-')
plt.xlabel('volume per atom $(\AA^3)$')
plt.ylabel('Energy per atom E (eV/atom)')
plt.title('BCC')
plt.subplot(1,3,3)
plt.plot(fcc_x, fcc_y, 'b-')
plt.xlabel('volume per atom $(\AA^3)$')
plt.ylabel('Energy per atom E (eV/atom)')
plt.title('FCC')
# Adjust subplot spacing
plt.subplots_adjust(wspace=0.2)
plt.show()
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Reference
[1] T. Wen, R. Wang, L. Zhu, L. Zhang, H. Wang, D. J. Srolovitz, and Z. Wu, Specialising neural network potentials for accurate properties and application to the mechanical response of titanium, npj Computational Materials 7, 206 (2021).
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caic@bjaisi.com
更新于 2024-12-16
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APEX
zhuoyli@connect.hku.hk
更新于 2024-03-17
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