Datasets:

---

FerrariKazu

/

rhan-checkpoints-rolling

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
baseline_label: string
batch_size: int64
checkpoints: list<item: struct<arch: string, freeze: bool, label: string, path: string, sha256: string>>
  child 0, item: struct<arch: string, freeze: bool, label: string, path: string, sha256: string>
      child 0, arch: string
      child 1, freeze: bool
      child 2, label: string
      child 3, path: string
      child 4, sha256: string
crossover_verdicts: list<item: struct<acc_mean: double, acc_std: double, baseline: string, baseline_acc_mean: double, ch (... 89 chars omitted)
  child 0, item: struct<acc_mean: double, acc_std: double, baseline: string, baseline_acc_mean: double, checkpoint: s (... 77 chars omitted)
      child 0, acc_mean: double
      child 1, acc_std: double
      child 2, baseline: string
      child 3, baseline_acc_mean: double
      child 4, checkpoint: string
      child 5, diff_pp: double
      child 6, eps: double
      child 7, threshold_2sig: double
      child 8, verdict: string
eps_list: list<item: double>
  child 0, item: double
eps_mode: string
git_branch: string
git_sha: string
n_samples: int64
pgd_steps: int64
results: list<item: struct<acc_mean: string, acc_std: string, ckpt_label: string, eps_norm_B: string, eps_nor (... 122 chars omitted)
  child 0, item: struct<acc_mean: string, acc_std: string, ckpt_label: string, eps_norm_B: string, eps_norm_G: string (... 110 chars omitted)
      child 0, acc_mean: string
      child 1, acc_std: string
      child 2, ckpt_label: string
      child 3, eps_norm_B: string
      child 4, eps_norm_G: string
      child 5, eps_norm_R: string
      child 6, eps_pixel: string
      child 7, macro_dprime_mean: string
      child 8, macro_dprime_std: string
      child 9, n_seeds: string
results_csv: string
schema: string
seed_extension: struct<applied: bool, eps: double, extended_seeds: list<item: int64>, main_seeds: list<item: int64>, (... 14 chars omitted)
  child 0, applied: bool
  child 1, eps: double
  child 2, extended_seeds: list<item: int64>
      child 0, item: int64
  child 3, main_seeds: list<item: int64>
      child 0, item: int64
  child 4, note: string
seeds: list<item: int64>
  child 0, item: int64
timestamp_utc: timestamp[s]
tool: string
to
{'baseline_label': Value('string'), 'batch_size': Value('int64'), 'checkpoints': List({'arch': Value('string'), 'freeze': Value('bool'), 'label': Value('string'), 'path': Value('string'), 'sha256': Value('string')}), 'crossover_verdicts': List({'acc_mean': Value('float64'), 'acc_std': Value('float64'), 'baseline': Value('string'), 'baseline_acc_mean': Value('float64'), 'checkpoint': Value('string'), 'diff_pp': Value('float64'), 'eps': Value('float64'), 'threshold_2sig': Value('float64'), 'verdict': Value('string')}), 'eps_list': List(Value('float64')), 'eps_mode': Value('string'), 'git_branch': Value('string'), 'git_sha': Value('string'), 'n_samples': Value('int64'), 'pgd_steps': Value('int64'), 'results': List({'acc_mean': Value('string'), 'acc_std': Value('string'), 'ckpt_label': Value('string'), 'eps_norm_B': Value('string'), 'eps_norm_G': Value('string'), 'eps_norm_R': Value('string'), 'eps_pixel': Value('string'), 'macro_dprime_mean': Value('string'), 'macro_dprime_std': Value('string'), 'n_seeds': Value('string')}), 'results_csv': Value('string'), 'schema': Value('string'), 'seeds': List(Value('int64')), 'timestamp_utc': Value('timestamp[s]'), 'tool': Value('string')}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              baseline_label: string
              batch_size: int64
              checkpoints: list<item: struct<arch: string, freeze: bool, label: string, path: string, sha256: string>>
                child 0, item: struct<arch: string, freeze: bool, label: string, path: string, sha256: string>
                    child 0, arch: string
                    child 1, freeze: bool
                    child 2, label: string
                    child 3, path: string
                    child 4, sha256: string
              crossover_verdicts: list<item: struct<acc_mean: double, acc_std: double, baseline: string, baseline_acc_mean: double, ch (... 89 chars omitted)
                child 0, item: struct<acc_mean: double, acc_std: double, baseline: string, baseline_acc_mean: double, checkpoint: s (... 77 chars omitted)
                    child 0, acc_mean: double
                    child 1, acc_std: double
                    child 2, baseline: string
                    child 3, baseline_acc_mean: double
                    child 4, checkpoint: string
                    child 5, diff_pp: double
                    child 6, eps: double
                    child 7, threshold_2sig: double
                    child 8, verdict: string
              eps_list: list<item: double>
                child 0, item: double
              eps_mode: string
              git_branch: string
              git_sha: string
              n_samples: int64
              pgd_steps: int64
              results: list<item: struct<acc_mean: string, acc_std: string, ckpt_label: string, eps_norm_B: string, eps_nor (... 122 chars omitted)
                child 0, item: struct<acc_mean: string, acc_std: string, ckpt_label: string, eps_norm_B: string, eps_norm_G: string (... 110 chars omitted)
                    child 0, acc_mean: string
                    child 1, acc_std: string
                    child 2, ckpt_label: string
                    child 3, eps_norm_B: string
                    child 4, eps_norm_G: string
                    child 5, eps_norm_R: string
                    child 6, eps_pixel: string
                    child 7, macro_dprime_mean: string
                    child 8, macro_dprime_std: string
                    child 9, n_seeds: string
              results_csv: string
              schema: string
              seed_extension: struct<applied: bool, eps: double, extended_seeds: list<item: int64>, main_seeds: list<item: int64>, (... 14 chars omitted)
                child 0, applied: bool
                child 1, eps: double
                child 2, extended_seeds: list<item: int64>
                    child 0, item: int64
                child 3, main_seeds: list<item: int64>
                    child 0, item: int64
                child 4, note: string
              seeds: list<item: int64>
                child 0, item: int64
              timestamp_utc: timestamp[s]
              tool: string
              to
              {'baseline_label': Value('string'), 'batch_size': Value('int64'), 'checkpoints': List({'arch': Value('string'), 'freeze': Value('bool'), 'label': Value('string'), 'path': Value('string'), 'sha256': Value('string')}), 'crossover_verdicts': List({'acc_mean': Value('float64'), 'acc_std': Value('float64'), 'baseline': Value('string'), 'baseline_acc_mean': Value('float64'), 'checkpoint': Value('string'), 'diff_pp': Value('float64'), 'eps': Value('float64'), 'threshold_2sig': Value('float64'), 'verdict': Value('string')}), 'eps_list': List(Value('float64')), 'eps_mode': Value('string'), 'git_branch': Value('string'), 'git_sha': Value('string'), 'n_samples': Value('int64'), 'pgd_steps': Value('int64'), 'results': List({'acc_mean': Value('string'), 'acc_std': Value('string'), 'ckpt_label': Value('string'), 'eps_norm_B': Value('string'), 'eps_norm_G': Value('string'), 'eps_norm_R': Value('string'), 'eps_pixel': Value('string'), 'macro_dprime_mean': Value('string'), 'macro_dprime_std': Value('string'), 'n_seeds': Value('string')}), 'results_csv': Value('string'), 'schema': Value('string'), 'seeds': List(Value('int64')), 'timestamp_utc': Value('timestamp[s]'), 'tool': Value('string')}
              because column names don't match