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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
plan: string
anchor: bool
pos: string
site: string
label: string
label_col: int64
label_name: string
seed: int64
hp: struct<lr: double, epochs: int64, batch: int64, source: string>
child 0, lr: double
child 1, epochs: int64
child 2, batch: int64
child 3, source: string
model: struct<model_dir: string, model_type: string, n_layers: int64, hidden_size: int64, vocab_size: int64 (... 115 chars omitted)
child 0, model_dir: string
child 1, model_type: string
child 2, n_layers: int64
child 3, hidden_size: int64
child 4, vocab_size: int64
child 5, name_or_path: string
child 6, dtype: string
child 7, peft: bool
child 8, adapter: string
child 9, adapter_merged: bool
child 10, inner_model_type: string
sets: struct<test: int64, train: int64>
child 0, test: int64
child 1, train: int64
ref: struct<test: struct<all: struct<start: struct<n: int64, median: double, p10: double, p90: double>, f (... 784 chars omitted)
child 0, test: struct<all: struct<start: struct<n: int64, median: double, p10: double, p90: double>, floor: struct< (... 770 chars omitted)
child 0, all: struct<start: struct<n: int64, median: double, p10: double, p90: double>, floor: struct<n: int64, me (... 180 chars omitted)
child 0, start: struct<n: int64, median: double, p10: double, p90: double>
child 0, n: int64
child 1, median: double
child 2, p10: double
child 3, p90: double
child 1, floor: struct<n: int64
...
truct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: int64>
child 0, p0: double
child 1, p10: double
child 2, p50: double
child 3, p90: double
child 4, p100: double
child 5, mean: double
child 6, n: int64
child 16, m_recorded_only: struct<T_d: struct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: (... 110 chars omitted)
child 0, T_d: struct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: int64>
child 0, p0: double
child 1, p10: double
child 2, p50: double
child 3, p90: double
child 4, p100: double
child 5, mean: double
child 6, n: int64
child 1, T_b: struct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: int64>
child 0, p0: double
child 1, p10: double
child 2, p50: double
child 3, p90: double
child 4, p100: double
child 5, mean: double
child 6, n: int64
written: timestamp[s]
script: string
seconds: double
gpu: struct<gpu: string, cuda_visible: string, hip_visible: string, torch: string, hip: string, cuda: nul (... 2 chars omitted)
child 0, gpu: string
child 1, cuda_visible: string
child 2, hip_visible: string
child 3, torch: string
child 4, hip: string
child 5, cuda: null
units_kind: string
units_file: string
to
{'plan': Value('string'), 'script': Value('string'), 'stage': Value('string'), 'units_kind': Value('string'), 'units_file': Value('string'), 'natural_file': Value('string'), 'model': {'model_dir': Value('string'), 'model_type': Value('string'), 'n_layers': Value('int64'), 'hidden_size': Value('int64'), 'vocab_size': Value('int64'), 'name_or_path': Value('string'), 'dtype': Value('string'), 'peft': Value('bool'), 'adapter': Value('string'), 'adapter_merged': Value('bool'), 'inner_model_type': Value('string')}, 'n_prompts': Value('int64'), 'n_batches': Value('int64'), 'batch': Value('int64'), 'definitions': {'T_d': Value('string'), 'T_b': Value('string'), 's': Value('string'), 'beta': Value('string'), 'landing': Value('string')}, 'selfcheck': {'layout': {'n_prompts': Value('int64'), 'n_bad_layout': Value('int64'), 'n_bad_parse': Value('int64'), 'pass_': Value('bool')}, 'tokenizer_vs_carrier': {'n_prompts': Value('int64'), 'n_differ': Value('int64'), 'pass_': Value('bool')}, 'determinism_first_batch': {'batch_size': Value('int64'), 'L': Value('int64'), 'logits_bitwise_equal': Value('bool')}, 'landing_vs_cube_v3_fold_pred_km': {'max': Value('float64'), 'median': Value('float64'), 'pass_': Value('bool')}, 'X8_recomputed_vs_natural_npz_max_abs_km': Value('float64'), 'n_units': Value('int64'), 'n_cross': Value('int64'), 'n_same': Value('int64'), 'n_both_short': Value('int64'), 'same_side_Td_vs_p001_abs_dX_km': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64')
...
oat64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, '110': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, '111': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, 'T_d': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, 'T_b': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}}, 'm_recorded_only': {'T_d': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, 'T_b': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}}}, 'seconds': Value('float64'), 'gpu': {'gpu': Value('string'), 'cuda_visible': Value('string'), 'hip_visible': Value('string'), 'torch': Value('string'), 'hip': Value('string'), 'cuda': Value('null')}, 'written': Value('timestamp[s]')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
plan: string
anchor: bool
pos: string
site: string
label: string
label_col: int64
label_name: string
seed: int64
hp: struct<lr: double, epochs: int64, batch: int64, source: string>
child 0, lr: double
child 1, epochs: int64
child 2, batch: int64
child 3, source: string
model: struct<model_dir: string, model_type: string, n_layers: int64, hidden_size: int64, vocab_size: int64 (... 115 chars omitted)
child 0, model_dir: string
child 1, model_type: string
child 2, n_layers: int64
child 3, hidden_size: int64
child 4, vocab_size: int64
child 5, name_or_path: string
child 6, dtype: string
child 7, peft: bool
child 8, adapter: string
child 9, adapter_merged: bool
child 10, inner_model_type: string
sets: struct<test: int64, train: int64>
child 0, test: int64
child 1, train: int64
ref: struct<test: struct<all: struct<start: struct<n: int64, median: double, p10: double, p90: double>, f (... 784 chars omitted)
child 0, test: struct<all: struct<start: struct<n: int64, median: double, p10: double, p90: double>, floor: struct< (... 770 chars omitted)
child 0, all: struct<start: struct<n: int64, median: double, p10: double, p90: double>, floor: struct<n: int64, me (... 180 chars omitted)
child 0, start: struct<n: int64, median: double, p10: double, p90: double>
child 0, n: int64
child 1, median: double
child 2, p10: double
child 3, p90: double
child 1, floor: struct<n: int64
...
truct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: int64>
child 0, p0: double
child 1, p10: double
child 2, p50: double
child 3, p90: double
child 4, p100: double
child 5, mean: double
child 6, n: int64
child 16, m_recorded_only: struct<T_d: struct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: (... 110 chars omitted)
child 0, T_d: struct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: int64>
child 0, p0: double
child 1, p10: double
child 2, p50: double
child 3, p90: double
child 4, p100: double
child 5, mean: double
child 6, n: int64
child 1, T_b: struct<p0: double, p10: double, p50: double, p90: double, p100: double, mean: double, n: int64>
child 0, p0: double
child 1, p10: double
child 2, p50: double
child 3, p90: double
child 4, p100: double
child 5, mean: double
child 6, n: int64
written: timestamp[s]
script: string
seconds: double
gpu: struct<gpu: string, cuda_visible: string, hip_visible: string, torch: string, hip: string, cuda: nul (... 2 chars omitted)
child 0, gpu: string
child 1, cuda_visible: string
child 2, hip_visible: string
child 3, torch: string
child 4, hip: string
child 5, cuda: null
units_kind: string
units_file: string
to
{'plan': Value('string'), 'script': Value('string'), 'stage': Value('string'), 'units_kind': Value('string'), 'units_file': Value('string'), 'natural_file': Value('string'), 'model': {'model_dir': Value('string'), 'model_type': Value('string'), 'n_layers': Value('int64'), 'hidden_size': Value('int64'), 'vocab_size': Value('int64'), 'name_or_path': Value('string'), 'dtype': Value('string'), 'peft': Value('bool'), 'adapter': Value('string'), 'adapter_merged': Value('bool'), 'inner_model_type': Value('string')}, 'n_prompts': Value('int64'), 'n_batches': Value('int64'), 'batch': Value('int64'), 'definitions': {'T_d': Value('string'), 'T_b': Value('string'), 's': Value('string'), 'beta': Value('string'), 'landing': Value('string')}, 'selfcheck': {'layout': {'n_prompts': Value('int64'), 'n_bad_layout': Value('int64'), 'n_bad_parse': Value('int64'), 'pass_': Value('bool')}, 'tokenizer_vs_carrier': {'n_prompts': Value('int64'), 'n_differ': Value('int64'), 'pass_': Value('bool')}, 'determinism_first_batch': {'batch_size': Value('int64'), 'L': Value('int64'), 'logits_bitwise_equal': Value('bool')}, 'landing_vs_cube_v3_fold_pred_km': {'max': Value('float64'), 'median': Value('float64'), 'pass_': Value('bool')}, 'X8_recomputed_vs_natural_npz_max_abs_km': Value('float64'), 'n_units': Value('int64'), 'n_cross': Value('int64'), 'n_same': Value('int64'), 'n_both_short': Value('int64'), 'same_side_Td_vs_p001_abs_dX_km': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64')
...
oat64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, '110': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, '111': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, 'T_d': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, 'T_b': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}}, 'm_recorded_only': {'T_d': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}, 'T_b': {'p0': Value('float64'), 'p10': Value('float64'), 'p50': Value('float64'), 'p90': Value('float64'), 'p100': Value('float64'), 'mean': Value('float64'), 'n': Value('int64')}}}, 'seconds': Value('float64'), 'gpu': {'gpu': Value('string'), 'cuda_visible': Value('string'), 'hip_visible': Value('string'), 'torch': Value('string'), 'hip': Value('string'), 'cuda': Value('null')}, 'written': Value('timestamp[s]')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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