thuerey-group/apebench-scraped

--- license: mit --- # APEBench Scraped A representative subset of datasets created using the [APEBench benchmark suite](https://github.com/tum-pbs/apebench) using version `0.1.0`. ⚠️ Note that [APEBench](https://github.com/tum-pbs/apebench) is designed to procedurally generate all its training and test data. This allows for advanced features like benchmarking approaches with differentiable physics. Hence, there is no need to download this dataset as it can be easily re-generated using APEBench which can be installed via `pip install apebench`. See also [here](https://tum-pbs.github.io/apebench/examples/scrape_datasets/) for how to scrape datasets. ## Download Download without large files ```bash GIT_LFS_SKIP_SMUDGE=1 git clone [e-mail]:datasets/thuerey-group/apebench-scraped ``` Afterwards, you can inspect the repository and download the files you need. For example, for `1d_diff_adv`: ```bash git lfs install git lfs pull -I "data/1d_diff_adv*" ``` Alternatively, you can download the entire repository with large files (~30GB): ```bash git lfs install git clone [e-mail]:datasets/thuerey-group/apebench-scraped ``` ## Reproduction Obtained via: ```bash 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: ```bash conda env create -f environment.yml conda activate apebench ``` And then executed the following script (also found under `reproduce.py`): ```python 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--- 许可证：MIT --- # APEBench 爬取数据集 本数据集为基于版本`0.1.0`的[APEBench基准测试套件（APEBench benchmark suite）](https://github.com/tum-pbs/apebench)所生成数据集的代表性子集。 ⚠️ 请注意，[APEBench](https://github.com/tum-pbs/apebench) 采用程序化方式生成全部训练与测试数据，可支持可微物理基准测试等高级功能。因此无需下载本数据集，只需通过`pip install apebench`安装APEBench即可轻松重新生成所需数据。有关数据集爬取的具体方法，可参考[此处](https://tum-pbs.github.io/apebench/examples/scrape_datasets/)。 ## 下载 ### 无需下载大文件 执行以下命令即可克隆仓库并跳过大文件： bash GIT_LFS_SKIP_SMUDGE=1 git clone [e-mail]:datasets/thuerey-group/apebench-scraped 克隆完成后，您可浏览该仓库并按需下载所需文件。以`1d_diff_adv`数据集为例： bash git lfs install git lfs pull -I "data/1d_diff_adv*" 或者，您也可以下载包含全部大文件的完整仓库（约30GB）： bash git lfs install git clone [e-mail]:datasets/thuerey-group/apebench-scraped ## 复现方法 数据集可通过以下步骤复现： bash conda create -n apebench python=3.12 -y conda activate apebench pip install -U "jax[cuda12]" pip install apebench==0.1.0 或者，您也可以使用仓库中提供的`environment.yml`文件创建环境： bash conda env create -f environment.yml conda activate apebench 随后执行以下脚本（脚本文件`reproduce.py`已包含在仓库中）： python 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) ⚠️ 由于JAX版本、运行后端（CPU、GPU、TPU）、驱动程序等因素的差异，生成的数据可能存在细微差异，对于混沌类问题（如KS或Kolmogorov流）尤为明显。 - NVIDIA驱动版本：535.183.01 - CUDA版本：12.2 - GPU型号：RTX 3060