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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
experiment: string
model: string
total_configs: int64
completed: int64
results: list<item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: doub (... 366 chars omitted)
child 0, item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: double, initial (... 354 chars omitted)
child 0, status: string
child 1, trainable_params: int64
child 2, total_params: int64
child 3, trainable_frac: double
child 4, initial_loss: double
child 5, final_loss: double
child 6, loss_reduction: double
child 7, loss_trend_per_step: double
child 8, mean_grad_norm: double
child 9, max_grad_norm: double
child 10, min_grad_norm: double
child 11, converged: bool
child 12, loss_trajectory: list<item: struct<step: int64, loss: double>>
child 0, item: struct<step: int64, loss: double>
child 0, step: int64
child 1, loss: double
child 13, config: struct<rank: int64, target: string, target_group: string, mask_ratio: double, steps: int64, group: s (... 6 chars omitted)
child 0, rank: int64
child 1, target: string
child 2, target_group: string
child 3, mask_ratio: double
child 4, steps: int64
child 5, group: string
models_tested: list<item: string>
child 0, item: string
author: string
date: timestamp[s]
datasets: struct<dim1_ablation_results: struct<experiment:
...
hild 1, model: string
child 2, key_findings: list<item: string>
child 0, item: string
child 3, results: list<item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: doub (... 308 chars omitted)
child 0, item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: double, losses: (... 296 chars omitted)
child 0, status: string
child 1, trainable_params: int64
child 2, total_params: int64
child 3, trainable_frac: double
child 4, losses: list<item: struct<step: int64, loss: double, grad_norm: double>>
child 0, item: struct<step: int64, loss: double, grad_norm: double>
child 0, step: int64
child 1, loss: double
child 2, grad_norm: double
child 5, final_loss: double
child 6, initial_loss: double
child 7, loss_reduction: double
child 8, loss_trend_per_step: double
child 9, mean_grad_norm: double
child 10, converged: bool
child 11, config: struct<rank: int64, target: string, mask_ratio: double, steps: int64, group: string>
child 0, rank: int64
child 1, target: string
child 2, mask_ratio: double
child 3, steps: int64
child 4, group: string
paper: string
to
{'paper': Value('string'), 'author': Value('string'), 'date': Value('timestamp[s]'), 'models_tested': List(Value('string')), 'datasets': {'dim1_ablation_results': {'experiment': Value('string'), 'model': Value('string'), 'total_configs': Value('int64'), 'completed': Value('int64'), 'results': List({'status': Value('string'), 'trainable_params': Value('int64'), 'total_params': Value('int64'), 'trainable_frac': Value('float64'), 'initial_loss': Value('float64'), 'final_loss': Value('float64'), 'loss_reduction': Value('float64'), 'loss_trend_per_step': Value('float64'), 'mean_grad_norm': Value('float64'), 'max_grad_norm': Value('float64'), 'min_grad_norm': Value('float64'), 'converged': Value('bool'), 'loss_trajectory': List({'step': Value('int64'), 'loss': Value('float64')}), 'config': {'rank': Value('int64'), 'target': Value('string'), 'target_group': Value('string'), 'mask_ratio': Value('float64'), 'steps': Value('int64'), 'group': Value('string')}})}, 'dim1_dream_peft_v2_results': {'model': Value('string'), 'timestamp': Value('string'), 'seed': Value('int64'), 'api_incompatibilities': {'dream_attn_mask_dtype': Value('string'), 'dream_attn_mask_shape': Value('string'), 'note': Value('string')}, 'grid': List({'model': Value('string'), 'rank': Value('int64'), 'mask_ratio': Value('float64'), 'final_loss': Value('float64'), 'avg_grad_norm': Value('float64'), 'n_steps_completed': Value('int64'), 'converged': Value('bool')}), 'flip_detected': Value('bool'), 'best_rank_low_mask': Va
...
Value('float64'), 'label': Value('string')}, 'seeds': List({'seed': Value('int64'), 'final_loss': Value('float64'), 'peak_grad_norm': Value('float64')}), 'summary': {'mean_loss': Value('float64'), 'std_loss': Value('float64'), 'ci_95_half_width': Value('float64'), 'mean_peak_grad_norm': Value('float64'), 'n_successful': Value('int64')}}, 'rank16_mask0.5': {'config': {'rank': Value('int64'), 'target': Value('string'), 'mask_ratio': Value('float64'), 'label': Value('string')}, 'seeds': List({'seed': Value('int64'), 'final_loss': Value('float64'), 'peak_grad_norm': Value('float64')}), 'summary': {'mean_loss': Value('float64'), 'std_loss': Value('float64'), 'ci_95_half_width': Value('float64'), 'mean_peak_grad_norm': Value('float64'), 'n_successful': Value('int64')}}}, 'dim1_peft_v4_results': {'experiment': Value('string'), 'model': Value('string'), 'key_findings': List(Value('string')), 'results': List({'status': Value('string'), 'trainable_params': Value('int64'), 'total_params': Value('int64'), 'trainable_frac': Value('float64'), 'losses': List({'step': Value('int64'), 'loss': Value('float64'), 'grad_norm': Value('float64')}), 'final_loss': Value('float64'), 'initial_loss': Value('float64'), 'loss_reduction': Value('float64'), 'loss_trend_per_step': Value('float64'), 'mean_grad_norm': Value('float64'), 'converged': Value('bool'), 'config': {'rank': Value('int64'), 'target': Value('string'), 'mask_ratio': Value('float64'), 'steps': Value('int64'), 'group': Value('string')}})}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
return get_rows(
^^^^^^^^^
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 77, in get_rows
rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
for key, pa_table in self._iter_arrow():
^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 289, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 124, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
experiment: string
model: string
total_configs: int64
completed: int64
results: list<item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: doub (... 366 chars omitted)
child 0, item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: double, initial (... 354 chars omitted)
child 0, status: string
child 1, trainable_params: int64
child 2, total_params: int64
child 3, trainable_frac: double
child 4, initial_loss: double
child 5, final_loss: double
child 6, loss_reduction: double
child 7, loss_trend_per_step: double
child 8, mean_grad_norm: double
child 9, max_grad_norm: double
child 10, min_grad_norm: double
child 11, converged: bool
child 12, loss_trajectory: list<item: struct<step: int64, loss: double>>
child 0, item: struct<step: int64, loss: double>
child 0, step: int64
child 1, loss: double
child 13, config: struct<rank: int64, target: string, target_group: string, mask_ratio: double, steps: int64, group: s (... 6 chars omitted)
child 0, rank: int64
child 1, target: string
child 2, target_group: string
child 3, mask_ratio: double
child 4, steps: int64
child 5, group: string
models_tested: list<item: string>
child 0, item: string
author: string
date: timestamp[s]
datasets: struct<dim1_ablation_results: struct<experiment:
...
hild 1, model: string
child 2, key_findings: list<item: string>
child 0, item: string
child 3, results: list<item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: doub (... 308 chars omitted)
child 0, item: struct<status: string, trainable_params: int64, total_params: int64, trainable_frac: double, losses: (... 296 chars omitted)
child 0, status: string
child 1, trainable_params: int64
child 2, total_params: int64
child 3, trainable_frac: double
child 4, losses: list<item: struct<step: int64, loss: double, grad_norm: double>>
child 0, item: struct<step: int64, loss: double, grad_norm: double>
child 0, step: int64
child 1, loss: double
child 2, grad_norm: double
child 5, final_loss: double
child 6, initial_loss: double
child 7, loss_reduction: double
child 8, loss_trend_per_step: double
child 9, mean_grad_norm: double
child 10, converged: bool
child 11, config: struct<rank: int64, target: string, mask_ratio: double, steps: int64, group: string>
child 0, rank: int64
child 1, target: string
child 2, mask_ratio: double
child 3, steps: int64
child 4, group: string
paper: string
to
{'paper': Value('string'), 'author': Value('string'), 'date': Value('timestamp[s]'), 'models_tested': List(Value('string')), 'datasets': {'dim1_ablation_results': {'experiment': Value('string'), 'model': Value('string'), 'total_configs': Value('int64'), 'completed': Value('int64'), 'results': List({'status': Value('string'), 'trainable_params': Value('int64'), 'total_params': Value('int64'), 'trainable_frac': Value('float64'), 'initial_loss': Value('float64'), 'final_loss': Value('float64'), 'loss_reduction': Value('float64'), 'loss_trend_per_step': Value('float64'), 'mean_grad_norm': Value('float64'), 'max_grad_norm': Value('float64'), 'min_grad_norm': Value('float64'), 'converged': Value('bool'), 'loss_trajectory': List({'step': Value('int64'), 'loss': Value('float64')}), 'config': {'rank': Value('int64'), 'target': Value('string'), 'target_group': Value('string'), 'mask_ratio': Value('float64'), 'steps': Value('int64'), 'group': Value('string')}})}, 'dim1_dream_peft_v2_results': {'model': Value('string'), 'timestamp': Value('string'), 'seed': Value('int64'), 'api_incompatibilities': {'dream_attn_mask_dtype': Value('string'), 'dream_attn_mask_shape': Value('string'), 'note': Value('string')}, 'grid': List({'model': Value('string'), 'rank': Value('int64'), 'mask_ratio': Value('float64'), 'final_loss': Value('float64'), 'avg_grad_norm': Value('float64'), 'n_steps_completed': Value('int64'), 'converged': Value('bool')}), 'flip_detected': Value('bool'), 'best_rank_low_mask': Va
...
Value('float64'), 'label': Value('string')}, 'seeds': List({'seed': Value('int64'), 'final_loss': Value('float64'), 'peak_grad_norm': Value('float64')}), 'summary': {'mean_loss': Value('float64'), 'std_loss': Value('float64'), 'ci_95_half_width': Value('float64'), 'mean_peak_grad_norm': Value('float64'), 'n_successful': Value('int64')}}, 'rank16_mask0.5': {'config': {'rank': Value('int64'), 'target': Value('string'), 'mask_ratio': Value('float64'), 'label': Value('string')}, 'seeds': List({'seed': Value('int64'), 'final_loss': Value('float64'), 'peak_grad_norm': Value('float64')}), 'summary': {'mean_loss': Value('float64'), 'std_loss': Value('float64'), 'ci_95_half_width': Value('float64'), 'mean_peak_grad_norm': Value('float64'), 'n_successful': Value('int64')}}}, 'dim1_peft_v4_results': {'experiment': Value('string'), 'model': Value('string'), 'key_findings': List(Value('string')), 'results': List({'status': Value('string'), 'trainable_params': Value('int64'), 'total_params': Value('int64'), 'trainable_frac': Value('float64'), 'losses': List({'step': Value('int64'), 'loss': Value('float64'), 'grad_norm': Value('float64')}), 'final_loss': Value('float64'), 'initial_loss': Value('float64'), 'loss_reduction': Value('float64'), 'loss_trend_per_step': Value('float64'), 'mean_grad_norm': Value('float64'), 'converged': Value('bool'), 'config': {'rank': Value('int64'), 'target': Value('string'), 'mask_ratio': Value('float64'), 'steps': Value('int64'), 'group': Value('string')}})}}}
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