Datasets:

---

GEOSCIENCES-AI

/

alluvial-geomodels

Like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
patch_shape_z_x: list<item: int64>
  child 0, item: int64
dtype: string
porosity_dtype: string
classes: struct<0: string, 1: string, 2: string>
  child 0, 0: string
  child 1, 1: string
  child 2, 2: string
plane_step_cells: int64
patches_per_seed: int64
seeds: list<item: int64>
  child 0, item: int64
file_pattern: string
models_3d: struct<folder: string, facies: string, porosity: string, z: string>
  child 0, folder: string
  child 1, facies: string
  child 2, porosity: string
  child 3, z: string
note: string
column_null_prob: double
noise_seed: int64
ckpt_net_hash: string
column_mode: string
facies_values: list<item: int64>
  child 0, item: int64
teacher_has_cond: bool
batch: int64
column_max: int64
column_min: int64
session: int64
n_train: int64
ckpt_ema_hash: string
ema: bool
teacher_kind: string
steps: int64
column_seed: int64
noise_generator: string
run_id: string
iteration: int64
global_step: int64
ckpt: string
front_load_power: double
concat_dropout: double
data: string
image_w: int64
image_h: int64
null_seed: int64
run: struct<c: int64, f: int64, generator_hash: string, latent_mean: list<item: double>, latent_std: list (... 135 chars omitted)
  child 0, c: int64
  child 1, f: int64
  child 2, generator_hash: string
  child 3, latent_mean: list<item: double>
      child 0, item: double
  child 4, latent_std: list<item: double>
      child 0, item: double
  child 5, latent_target: string
  child 6, noise_source: string
  child 7, space: string
  child 8, vae_hash: string
  child 9, x_shape: list<item: int64>
      child 0, item: int64
  child 10, z_dim: int64
cond_dtype: string
method: string
conditioning: string
framework: string
n_samples: int64
combo_counts: struct<column: int64, null: int64>
  child 0, column: int64
  child 1, null: int64
to
{'batch': Value('int64'), 'ckpt': Value('string'), 'ckpt_ema_hash': Value('string'), 'ckpt_net_hash': Value('string'), 'column_max': Value('int64'), 'column_min': Value('int64'), 'column_mode': Value('string'), 'column_null_prob': Value('float64'), 'column_seed': Value('int64'), 'combo_counts': {'column': Value('int64'), 'null': Value('int64')}, 'concat_dropout': Value('float64'), 'cond_dtype': Value('string'), 'conditioning': Value('string'), 'data': Value('string'), 'ema': Value('bool'), 'facies_values': List(Value('int64')), 'framework': Value('string'), 'front_load_power': Value('float64'), 'global_step': Value('int64'), 'image_h': Value('int64'), 'image_w': Value('int64'), 'iteration': Value('int64'), 'method': Value('string'), 'n_samples': Value('int64'), 'n_train': Value('int64'), 'noise_generator': Value('string'), 'noise_seed': Value('int64'), 'null_seed': Value('int64'), 'run': {'c': Value('int64'), 'f': Value('int64'), 'generator_hash': Value('string'), 'latent_mean': List(Value('float64')), 'latent_std': List(Value('float64')), 'latent_target': Value('string'), 'noise_source': Value('string'), 'space': Value('string'), 'vae_hash': Value('string'), 'x_shape': List(Value('int64')), 'z_dim': Value('int64')}, 'run_id': Value('string'), 'session': Value('int64'), 'steps': Value('int64'), 'teacher_has_cond': Value('bool'), 'teacher_kind': Value('string')}
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
              patch_shape_z_x: list<item: int64>
                child 0, item: int64
              dtype: string
              porosity_dtype: string
              classes: struct<0: string, 1: string, 2: string>
                child 0, 0: string
                child 1, 1: string
                child 2, 2: string
              plane_step_cells: int64
              patches_per_seed: int64
              seeds: list<item: int64>
                child 0, item: int64
              file_pattern: string
              models_3d: struct<folder: string, facies: string, porosity: string, z: string>
                child 0, folder: string
                child 1, facies: string
                child 2, porosity: string
                child 3, z: string
              note: string
              column_null_prob: double
              noise_seed: int64
              ckpt_net_hash: string
              column_mode: string
              facies_values: list<item: int64>
                child 0, item: int64
              teacher_has_cond: bool
              batch: int64
              column_max: int64
              column_min: int64
              session: int64
              n_train: int64
              ckpt_ema_hash: string
              ema: bool
              teacher_kind: string
              steps: int64
              column_seed: int64
              noise_generator: string
              run_id: string
              iteration: int64
              global_step: int64
              ckpt: string
              front_load_power: double
              concat_dropout: double
              data: string
              image_w: int64
              image_h: int64
              null_seed: int64
              run: struct<c: int64, f: int64, generator_hash: string, latent_mean: list<item: double>, latent_std: list (... 135 chars omitted)
                child 0, c: int64
                child 1, f: int64
                child 2, generator_hash: string
                child 3, latent_mean: list<item: double>
                    child 0, item: double
                child 4, latent_std: list<item: double>
                    child 0, item: double
                child 5, latent_target: string
                child 6, noise_source: string
                child 7, space: string
                child 8, vae_hash: string
                child 9, x_shape: list<item: int64>
                    child 0, item: int64
                child 10, z_dim: int64
              cond_dtype: string
              method: string
              conditioning: string
              framework: string
              n_samples: int64
              combo_counts: struct<column: int64, null: int64>
                child 0, column: int64
                child 1, null: int64
              to
              {'batch': Value('int64'), 'ckpt': Value('string'), 'ckpt_ema_hash': Value('string'), 'ckpt_net_hash': Value('string'), 'column_max': Value('int64'), 'column_min': Value('int64'), 'column_mode': Value('string'), 'column_null_prob': Value('float64'), 'column_seed': Value('int64'), 'combo_counts': {'column': Value('int64'), 'null': Value('int64')}, 'concat_dropout': Value('float64'), 'cond_dtype': Value('string'), 'conditioning': Value('string'), 'data': Value('string'), 'ema': Value('bool'), 'facies_values': List(Value('int64')), 'framework': Value('string'), 'front_load_power': Value('float64'), 'global_step': Value('int64'), 'image_h': Value('int64'), 'image_w': Value('int64'), 'iteration': Value('int64'), 'method': Value('string'), 'n_samples': Value('int64'), 'n_train': Value('int64'), 'noise_generator': Value('string'), 'noise_seed': Value('int64'), 'null_seed': Value('int64'), 'run': {'c': Value('int64'), 'f': Value('int64'), 'generator_hash': Value('string'), 'latent_mean': List(Value('float64')), 'latent_std': List(Value('float64')), 'latent_target': Value('string'), 'noise_source': Value('string'), 'space': Value('string'), 'vae_hash': Value('string'), 'x_shape': List(Value('int64')), 'z_dim': Value('int64')}, 'run_id': Value('string'), 'session': Value('int64'), 'steps': Value('int64'), 'teacher_has_cond': Value('bool'), 'teacher_kind': Value('string')}
              because column names don't match