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Exception: SplitsNotFoundError Message: The split names could not be parsed from the dataset config. Traceback: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 159, in compute compute_split_names_from_info_response( File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 131, in compute_split_names_from_info_response config_info_response = get_previous_step_or_raise(kind="config-info", dataset=dataset, config=config) File "/src/libs/libcommon/src/libcommon/simple_cache.py", line 567, in get_previous_step_or_raise raise CachedArtifactError( libcommon.simple_cache.CachedArtifactError: The previous step failed. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 499, in get_dataset_config_info for split_generator in builder._split_generators( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 99, in _split_generators inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema File "pyarrow/table.pxi", line 5317, in pyarrow.lib.concat_tables File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowTypeError: Unable to merge: Field npz has incompatible types: struct<inputs: list<item: list<item: list<item: list<item: list<item: double>>>>>, masks: list<item: list<item: list<item: double>>>, targets: list<item: list<item: list<item: list<item: list<item: double>>>>>> vs struct<inputs: list<item: list<item: list<item: list<item: list<item: float>>>>>, masks: list<item: list<item: list<item: double>>>, targets: list<item: list<item: list<item: list<item: list<item: float>>>>>>: Unable to merge: Field inputs has incompatible types: list<item: list<item: list<item: list<item: list<item: double>>>>> vs list<item: list<item: list<item: list<item: list<item: float>>>>>: Unable to merge: Field item has incompatible types: list<item: list<item: list<item: list<item: double>>>> vs list<item: list<item: list<item: list<item: float>>>>: Unable to merge: Field item has incompatible types: list<item: list<item: list<item: double>>> vs list<item: list<item: list<item: float>>>: Unable to merge: Field item has incompatible types: list<item: list<item: double>> vs list<item: list<item: float>>: Unable to merge: Field item has incompatible types: list<item: double> vs list<item: float>: Unable to merge: Field item has incompatible types: double vs float The above exception was the direct cause of the following exception: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 75, in compute_split_names_from_streaming_response for split in get_dataset_split_names( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 572, in get_dataset_split_names info = get_dataset_config_info( File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 504, in get_dataset_config_info raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.
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HR-Extreme Dataset
Overview
HR-Extreme is a high-resolution dataset designed to evaluate the performance of state-of-the-art models in predicting extreme weather events. The dataset contains 17 types of extreme weather events from 2020, based on High-Resolution Rapid Refresh (HRRR) data. It is intended for researchers in weather forecasting, encompassing both physical and deep learning methods. [Github Link](github_link: https://github.com/HuskyNian/HR-Extreme)
Dataset Structure
The dataset is divided into two main folders:
202001_202006
: Contains data from January 2020 to June 2020.202007_202012
: Contains data from July 2020 to December 2020.
Each folder stores the dataset in the WebDataset format, following Hugging Face's recommendations. Every 10 .npz
files are aggregated into a single .tar
file, named sequentially as i.tar
(e.g., 0001.tar
).
Usage
To construct the dataset, use the provided scripts in the GitHub repository. The main script, make_datasetall.py
, generates an index file for the dataset:
python make_datasetall.py 20200101 20200630
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