Benchmark and baselines for offline multi-objective optimization.
For a stable installation and usage, we suggest that you use a machine with CUDA version 11.7 or higher.
Our proposed offline collected data can be accessed and downloaded via Google Drive.
In order to run our Regex, RFP, and ZINC tasks, following LaMBO (Paper, Code), you may first download FoldX Emulator.
FoldX is available under a free academic license. After creating an account you will be emailed a link to download the FoldX executable and supporting assets. Copy the contents of the downloaded archive to ~/foldx. You may also need to rename the FoldX executable (e.g. mv -v ~/foldx/foldx_20221231 ~/foldx/foldx).
After installing FoldX, generate an instance proxy_rfp_problem.pkl of RFP task by running
cd off_moo_bench/problem/lambo/
python scripts/black_box_opt.py optimizer=mf_genetic optimizer/algorithm=nsga2 task=proxy_rfp tokenizer=proteinMake sure that the lines of saving instance of proxy_rfp_problem.pkl exist in line 203 of off_moo_bench/problem/lambo/lambo/optimizers/pymoo.py such that
if round_idx == self.num_rounds:
import pickle
with open('proxy_rfp_problem.pkl', 'wb+') as f:
pickle.dump(problem, f)After successfully installing FoldX, run
bash install.shfor a quick installation.
To test with our offline MO-NAS problems, installing EvoXBench (paper, code) is needed. Before running pip install evoxbench, you should first download their database via Google Drive or Baidu Netdisk, and data via Google Drive or Baidu Netdisk, and save it to the path off_moo_bench/problem/mo_nas/database and off_moo_bench/problem/mo_nas/data.
Following How to download from Google Drive, we propose a more stable method for downloading in a commandline interface. Please first go to OAuth 2.0 Playground to obtain your Google Drive download APIs, then download it with
curl -H "Authorization: Bearer <Your Google Drive APIs>" https://drive.google.com/file/d/11bQ1paHEWHDnnTPtxs2OyVY_Re-38DiO/view?usp=sharing -o database.zip curl -H "Authorization: Bearer <Your Google Drive APIs>" https://drive.google.com/file/d/1r0iSCq1gLFs5xnmp1MDiqcqxNcY5q6Hp/view?usp=sharing -o data.zip
Then configure the EvoXBench as:
from evoxbench.database.init import config
config("Path to database", "Path to data")
# For instance:
# With this structure:
# /home/Downloads/
# └─ database/
# | | __init__.py
# | | db.sqlite3
# | | ...
# |
# └─ data/
# └─ darts/
# └─ mnv3/
# └─ ...
# Then, execute:
# config("PATH_TO_M2BO/off_moo_bench/problem/mo_nas/database", "PATH_TO_M2BO/off_moo_bench/problem/mo_nas/data")mkdir ~/.mujoco
tar -zxvf mujoco210_linux_x86_64.tar.gz -C ~/.mujocoto put MuJoCo under ~/.mujoco and set environment variables.
- Make sure that you have installed needed dependancy.
sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3- Install required packages
cython,mujoco_py,gymand set environmental variables.
pip install cython==3.0.0a10 mujoco-py==2.1.2.14 gym==0.14.0
conda env config vars set LD_LIBRARY_PATH=$LD_LIBRARY_PATH:~/.mujoco/mujoco210/bin:/usr/lib/nvidia- After the above steps, you can check MuJoCo installation by
import mujoco_py
import os
mj_path = mujoco_py.utils.discover_mujoco()
xml_path = os.path.join(mj_path, 'model', 'humanoid.xml')
model = mujoco_py.load_model_from_path(xml_path)
sim = mujoco_py.MjSim(model)
print(sim.data.qpos)
# [0. 0. 1.4 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
# 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. ]
sim.step()
print(sim.data.qpos)
# [-1.12164337e-05 7.29847036e-22 1.39975300e+00 9.99999999e-01
# 1.80085466e-21 4.45933954e-05 -2.70143345e-20 1.30126513e-19
# -4.63561234e-05 -1.88020744e-20 -2.24492958e-06 4.79357124e-05
# -6.38208396e-04 -1.61130312e-03 -1.37554006e-03 5.54173825e-05
# -2.24492958e-06 4.79357124e-05 -6.38208396e-04 -1.61130312e-03
# -1.37554006e-03 -5.54173825e-05 -5.73572648e-05 7.63833991e-05
# -2.12765194e-05 5.73572648e-05 -7.63833991e-05 -2.12765194e-05]- Due to conflict versions among different packages, we design special orders to install packages correctly. Note that conducting
conda env create -f environment.ymlmay raise some errors during installing Pip dependancy. Do not stop then and keep going on with rest scripts we proposed ininstall.sh. - Due to packages conflicts, we use
fix_contents.shto solve such conflict bugs by runningbash fix_contents.sh ${YOUR_PATH_TO_CONDA}/envs/off-moo/lib/python3.8/site-packages/sklearn/cross_decomposition/pls_.py "pinv2" "pinv"
- We provide serveral test suites under
tests/folder to check for successful installation. - If you meet up with
libstdc++.so.6: version `GLIBCXX_3.4.29' not foundissue, since the reasons can be various, we recommend that you refer to StackOverflow: Where can I find GLIBCXX_3.4.29? or GitHub: libstdc++.so.6: version `GLIBCXX_3.4.29' not found for further advice to solve this issue. - We have test different hardware environments, including:
- Ubuntu 22.04, 4x4090, CUDA 12.3
- Ubuntu 22.04, 4x4090, CUDA 12.1
- Ubuntu 22.04, 8xV100(32G), CUDA 12.1
- Ubuntu 22.04, 1x3090, CUDA 12.0
- Ubuntu 20.04, 2x3090, CUDA 11.8
- Ubuntu 20.04, 2xA6000, CUDA 11.8
To reproduce the performance of baseline algorithms reported in our work, you may then run end2end.sh/multi_head.sh/multiple_models.sh/mobo.sh, or run the following series of commands in a bash terminal. Also, please ensure that the conda environment off-moo is activated in the bash session.
If you want to change some configurations, fix it in the script or in
./config/.
If you have any questions, feel free to contact Rongxi Tan or raise an issue.
@inproceedings{offline-moo,
author = {Ke Xue, Rong-Xi Tan, Xiaobin Huang, Chao Qian.},
title = {Offline Multi-Objective Optimization},
booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML'24)},
year = {2024},
address={Vienna, Austria},
pages={55595-55624}
}