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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.
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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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