1. Downloading AhmedML data for Surface Variable Prediction (43G)

The AhmedML Dataset is a publicly available dataset hosted in Hugging Face datasets.

1.1. Download Commands

The tutorial includes a convenient command to download the complete training data (~43G):

./download-dataset /path/to/dataset

Replace /path/to/dataset to your own directory and follow the prompt.

You may download a subset of runs by specifying a prefix at the prompt. For example, 1* gets runs 1, 1x, 1xx and reduces the download size to approximately 9.2G. Please note that you need at least 18 data samples to run the tutorials with the default configurations. You may adjust the train/validation/test sizes during the preprocessing step or modify the batch size during the training step to enable running the tutorials with fewer data samples.

The download command wraps the Hugging Face snapshot_download function, which you may want to customize for specific downloads. For example, the following code downloads the training data for all runs:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="neashton/ahmedml",
    repo_type="dataset",
    local_dir="/path/to/dataset",
    allow_patterns=[
        "run_*/boundary_*.vtp",
        "run_*/ahmed_*.stl"
    ]
)

Note

If you encounter a “Too Many Requests” error like this:

huggingface_hub.errors.HfHubHTTPError: 429 Client Error: Too Many Requests

This is due to rate limiting. Wait and try again. Hugging Face uses a local cache, so already downloaded files won’t need to be re-downloaded on retry. If the error continues, set the function argument max_workers=1 to download one file at a time.

1.2. Next Step: Training

Follow Training Surface Variable Prediction on the AhmedML Dataset to learn how to train a surface variable prediction model on the AhmedML dataset.

For more detailed information on the surface variable prediction use case, including how to build a model, configuration options, and advanced topics, refer to the Surface variable prediction user guide documentation.