Datasets:

---

Stanford-PSI-Lab

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yangdata

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Stanford Physical and Spatial Intelligence Lab 1

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
checkpoint_contents: string
dtype: string
filename: string
initialization: string
model: string
note: string
output_latent_channels: int64
parameters: int64
patch_embedding_input_channels: int64
run: string
sha256: string
size_bytes: int64
step: int64
tensors: int64
training: struct<data_seed: int64, dataset_repeat: int64, devices: int64, global_batch_size: int64, gradient_a (... 242 chars omitted)
  child 0, data_seed: int64
  child 1, dataset_repeat: int64
  child 2, devices: int64
  child 3, global_batch_size: int64
  child 4, gradient_accumulation_steps: int64
  child 5, height: int64
  child 6, learning_rate: double
  child 7, max_steps: int64
  child 8, microbatch_per_gpu: int64
  child 9, num_frames: int64
  child 10, real_dataset_repeat: int64
  child 11, resume_from: null
  child 12, resume_training_state: null
  child 13, vel_loss_weight: double
  child 14, width: int64
continuation: struct<start_step: int64, devices: int64, microbatch_per_gpu: int64, gradient_accumulation_steps: in (... 78 chars omitted)
  child 0, start_step: int64
  child 1, devices: int64
  child 2, microbatch_per_gpu: int64
  child 3, gradient_accumulation_steps: int64
  child 4, global_batch_size: int64
  child 5, learning_rate: double
  child 6, original_resume: string
to
{'step': Value('int64'), 'filename': Value('string'), 'sha256': Value('string'), 'size_bytes': Value('int64'), 'tensors': Value('int64'), 'parameters': Value('int64'), 'dtype': Value('string'), 'model': Value('string'), 'patch_embedding_input_channels': Value('int64'), 'output_latent_channels': Value('int64'), 'checkpoint_contents': Value('string'), 'continuation': {'start_step': Value('int64'), 'devices': Value('int64'), 'microbatch_per_gpu': Value('int64'), 'gradient_accumulation_steps': Value('int64'), 'global_batch_size': Value('int64'), 'learning_rate': Value('float64'), 'original_resume': Value('string')}, 'note': 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
              checkpoint_contents: string
              dtype: string
              filename: string
              initialization: string
              model: string
              note: string
              output_latent_channels: int64
              parameters: int64
              patch_embedding_input_channels: int64
              run: string
              sha256: string
              size_bytes: int64
              step: int64
              tensors: int64
              training: struct<data_seed: int64, dataset_repeat: int64, devices: int64, global_batch_size: int64, gradient_a (... 242 chars omitted)
                child 0, data_seed: int64
                child 1, dataset_repeat: int64
                child 2, devices: int64
                child 3, global_batch_size: int64
                child 4, gradient_accumulation_steps: int64
                child 5, height: int64
                child 6, learning_rate: double
                child 7, max_steps: int64
                child 8, microbatch_per_gpu: int64
                child 9, num_frames: int64
                child 10, real_dataset_repeat: int64
                child 11, resume_from: null
                child 12, resume_training_state: null
                child 13, vel_loss_weight: double
                child 14, width: int64
              continuation: struct<start_step: int64, devices: int64, microbatch_per_gpu: int64, gradient_accumulation_steps: in (... 78 chars omitted)
                child 0, start_step: int64
                child 1, devices: int64
                child 2, microbatch_per_gpu: int64
                child 3, gradient_accumulation_steps: int64
                child 4, global_batch_size: int64
                child 5, learning_rate: double
                child 6, original_resume: string
              to
              {'step': Value('int64'), 'filename': Value('string'), 'sha256': Value('string'), 'size_bytes': Value('int64'), 'tensors': Value('int64'), 'parameters': Value('int64'), 'dtype': Value('string'), 'model': Value('string'), 'patch_embedding_input_channels': Value('int64'), 'output_latent_channels': Value('int64'), 'checkpoint_contents': Value('string'), 'continuation': {'start_step': Value('int64'), 'devices': Value('int64'), 'microbatch_per_gpu': Value('int64'), 'gradient_accumulation_steps': Value('int64'), 'global_batch_size': Value('int64'), 'learning_rate': Value('float64'), 'original_resume': Value('string')}, 'note': Value('string')}
              because column names don't match