Add remark on procedurally generated data

README.md

@@ -1,71 +1,73 @@
----
-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`
-
-## Download
-
-Download without large files
-
-```bash
-GIT_LFS_SKIP_SMUDGE=1 git clone git@hf.co: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 git@hf.co: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
+---
+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 git@hf.co: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 git@hf.co: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