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---
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license: mit
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---
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# APEBench Scraped
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A representative subset of datasets created using the [APEBench benchmark suite](https://github.com/tum-pbs/apebench) using version `0.1.0`
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## Download
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Download without large files
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```bash
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GIT_LFS_SKIP_SMUDGE=1 git clone [email protected]:datasets/thuerey-group/apebench-scraped
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```
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Afterwards, you can inspect the repository and download the files you need. For
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example, for `1d_diff_adv`:
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```bash
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git lfs install
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git lfs pull -I "data/1d_diff_adv*"
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```
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Alternatively, you can download the entire repository with large files:
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```bash
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git lfs install
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git clone [email protected]:datasets/thuerey-group/apebench-scraped
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```
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## Reproduction
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Obtained via:
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```bash
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conda create -n apebench python=3.12 -y
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conda activate apebench
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pip install -U "jax[cuda12]"
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pip install apebench==0.1.0
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```
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Alternatively, you can use the provided `environment.yml` file:
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```bash
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conda env create -f environment.yml
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conda activate apebench
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```
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And then executed the following script (also found under `reproduce.py`):
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```python
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import apebench
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from tqdm import tqdm
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import os
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DATA_PATH = "data"
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os.makedirs(DATA_PATH, exist_ok=True)
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for config in tqdm(apebench.scraper.CURATION_APEBENCH_V1):
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apebench.scraper.scrape_data_and_metadata(DATA_PATH, **config)
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```
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⚠️ Small️️er variations of the generated data can occur due to different JAX
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versions, backends (CPU, GPU, TPU), drivers, etc. This might be especially
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pronounced for the chaotic problems (like KS or Kolmogorov flow).
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- nvidia driver version: 535.183.01
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- cuda version: 12.2
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- GPU: RTX 3060 |
