metadata

license: mit

APEBench Scraped

A representative subset of datasets created using the APEBench benchmark suite using version 0.1.0

Download

Download without large files

GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/thuerey-group/apebench-scraped

Afterwards, you can inspect the repository and download the files you need. For example, for 1d_diff_adv:

git lfs install
git lfs pull -I "data/1d_diff_adv*"

Alternatively, you can download the entire repository with large files:

git lfs install
git clone [email protected]:datasets/thuerey-group/apebench-scraped

Reproduction

Obtained via:

conda create -n apebench python=3.12 -y
conda activate apebench
pip install -U "jax[cuda12]"
pip install apebench==0.1.0

Alternatively, you can use the provided environment.yml file:

conda env create -f environment.yml
conda activate apebench

And then executed the following script (also found under reproduce.py):

import apebench
from tqdm import tqdm
import os

DATA_PATH = "data"

os.makedirs(DATA_PATH, exist_ok=True)

for config in tqdm(apebench.scraper.CURATION_APEBENCH_V1):
    apebench.scraper.scrape_data_and_metadata(DATA_PATH, **config)

⚠️ Small️️er variations of the generated data can occur due to different JAX versions, backends (CPU, GPU, TPU), drivers, etc. This might be especially pronounced for the chaotic problems (like KS or Kolmogorov flow).

nvidia driver version: 535.183.01
cuda version: 12.2
GPU: RTX 3060