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domrachev03
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green-vla-spiral-trackio-data

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Auto-converted to Parquet Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
id: int64
timestamp: string
run_name: string
log_id: string
space_id: null
cpu/0/utilization: double
cpu/1/utilization: double
cpu/2/utilization: double
cpu/3/utilization: double
cpu/4/utilization: double
cpu/5/utilization: double
cpu/6/utilization: double
cpu/7/utilization: double
cpu/8/utilization: double
cpu/9/utilization: double
cpu/10/utilization: double
cpu/11/utilization: double
cpu/12/utilization: double
cpu/13/utilization: double
cpu/14/utilization: double
cpu/15/utilization: double
cpu/16/utilization: double
cpu/17/utilization: double
cpu/18/utilization: double
cpu/19/utilization: double
cpu/20/utilization: double
cpu/21/utilization: double
cpu/22/utilization: double
cpu/23/utilization: double
cpu/24/utilization: double
cpu/25/utilization: double
cpu/26/utilization: double
cpu/27/utilization: double
cpu/28/utilization: double
cpu/29/utilization: double
cpu/30/utilization: double
cpu/31/utilization: double
cpu/32/utilization: double
cpu/33/utilization: double
cpu/34/utilization: double
cpu/35/utilization: double
cpu/36/utilization: double
cpu/37/utilization: double
cpu/38/utilization: double
cpu/39/utilization: double
cpu/40/utilization: double
cpu/41/utilization: double
cpu/42/utilization: double
cpu/43/utilization: double
cpu/44/utilization: double
cpu/45/utilization: double
cpu/46/utilization: double
cpu/47/utilization: double
cpu/utilization: double
cpu/frequency: double
cpu/count_logical: int64
cpu/count_physical: int64
memory/used: double
memory/total: double
memory/available: double
memory/percent: double
swap/used: double
swap/total: double
swap/percent: double
disk/read_mb_per_sec: double
disk/write_mb_per_sec: double
disk/read_iops: double
disk/write_iops: double
network/sent_mb_per_sec: double
network/recv_mb_per_sec: double
to
{'id': Value('int64'), 'run_name': Value('string'), 'created_at': Value('string'), 'observation_dim': Value('int64'), 'action_dim': Value('int64'), 'action_horizon': Value('int64'), 'latent_dim': Value('int64'), 'hidden_dim': Value('int64'), 'hidden_layers': Value('int64'), 'critic_ensemble_size': Value('int64'), 'latent_candidates': Value('int64'), 'gamma': Value('float64'), 'monte_carlo_mix': Value('float64'), 'residual_bc_beta': Value('float64'), 'target_noise_std': Value('float64'), 'target_noise_clip': Value('float64'), 'target_tau': Value('float64'), 'learning_rate': Value('float64'), 'weight_decay': Value('float64'), 'actor_update_period': Value('int64'), 'max_residual': Value('float64'), 'batch_size': Value('int64'), 'updates_per_round': Value('int64'), 'rollouts_per_task': Value('int64'), 'reward_source': Value('string'), '_Username': Value('string'), '_Created': Value('string'), '_Group': Value('string'), 'feature_dim': Value('int64'), 'noise_candidates': Value('int64'), 'actor_output_scale': Value('float64'), 'actor_learning_rate': Value('float64'), 'critic_learning_rate': Value('float64'), 'max_grad_norm': Value('float64'), 'base_checkpoint': Value('string'), 'selection_index': Value('string'), 'labels_index': Value('string'), 'dataset': {'tasks': List(Value('string')), 'episodes': Value('int64'), 'frames': Value('int64'), 'source_frames': {'red_soda_can': Value('int64'), 'sber_ring_box': Value('int64'), 'sugar_bowl': Value('int64')}, 'reward_source': Value('string'), 'sampling': Value('string')}, 'seed': Value('int64'), 'candidate_microbatch': Value('int64'), 'fuse_frozen_batches': Value('bool'), 'target_policy_noise': Value('float64'), 'monte_carlo_loss_weight': Value('float64'), 'bc_regularizer': Value('string'), 'actor_weight_decay': Value('float64'), 'monte_carlo_return_mode': Value('string'), 'action_contrast_weight': Value('float64'), 'action_contrast_temporal': Value('bool'), 'action_contrast_noise_std': Value('float64'), 'action_contrast_candidates': Value('bool'), 'action_contrast_margin': Value('float64'), 'executed_row_start': Value('int64'), 'executed_row_end': Value('int64'), 'demo_residual_weight': Value('float64'), 'feature_layernorm': Value('bool')}
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/parquet/parquet.py", line 220, in _generate_tables
                  yield Key(file_idx, batch_idx), self._cast_table(pa_table)
                                                  ~~~~~~~~~~~~~~~~^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/parquet/parquet.py", line 156, 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
              id: int64
              timestamp: string
              run_name: string
              log_id: string
              space_id: null
              cpu/0/utilization: double
              cpu/1/utilization: double
              cpu/2/utilization: double
              cpu/3/utilization: double
              cpu/4/utilization: double
              cpu/5/utilization: double
              cpu/6/utilization: double
              cpu/7/utilization: double
              cpu/8/utilization: double
              cpu/9/utilization: double
              cpu/10/utilization: double
              cpu/11/utilization: double
              cpu/12/utilization: double
              cpu/13/utilization: double
              cpu/14/utilization: double
              cpu/15/utilization: double
              cpu/16/utilization: double
              cpu/17/utilization: double
              cpu/18/utilization: double
              cpu/19/utilization: double
              cpu/20/utilization: double
              cpu/21/utilization: double
              cpu/22/utilization: double
              cpu/23/utilization: double
              cpu/24/utilization: double
              cpu/25/utilization: double
              cpu/26/utilization: double
              cpu/27/utilization: double
              cpu/28/utilization: double
              cpu/29/utilization: double
              cpu/30/utilization: double
              cpu/31/utilization: double
              cpu/32/utilization: double
              cpu/33/utilization: double
              cpu/34/utilization: double
              cpu/35/utilization: double
              cpu/36/utilization: double
              cpu/37/utilization: double
              cpu/38/utilization: double
              cpu/39/utilization: double
              cpu/40/utilization: double
              cpu/41/utilization: double
              cpu/42/utilization: double
              cpu/43/utilization: double
              cpu/44/utilization: double
              cpu/45/utilization: double
              cpu/46/utilization: double
              cpu/47/utilization: double
              cpu/utilization: double
              cpu/frequency: double
              cpu/count_logical: int64
              cpu/count_physical: int64
              memory/used: double
              memory/total: double
              memory/available: double
              memory/percent: double
              swap/used: double
              swap/total: double
              swap/percent: double
              disk/read_mb_per_sec: double
              disk/write_mb_per_sec: double
              disk/read_iops: double
              disk/write_iops: double
              network/sent_mb_per_sec: double
              network/recv_mb_per_sec: double
              to
              {'id': Value('int64'), 'run_name': Value('string'), 'created_at': Value('string'), 'observation_dim': Value('int64'), 'action_dim': Value('int64'), 'action_horizon': Value('int64'), 'latent_dim': Value('int64'), 'hidden_dim': Value('int64'), 'hidden_layers': Value('int64'), 'critic_ensemble_size': Value('int64'), 'latent_candidates': Value('int64'), 'gamma': Value('float64'), 'monte_carlo_mix': Value('float64'), 'residual_bc_beta': Value('float64'), 'target_noise_std': Value('float64'), 'target_noise_clip': Value('float64'), 'target_tau': Value('float64'), 'learning_rate': Value('float64'), 'weight_decay': Value('float64'), 'actor_update_period': Value('int64'), 'max_residual': Value('float64'), 'batch_size': Value('int64'), 'updates_per_round': Value('int64'), 'rollouts_per_task': Value('int64'), 'reward_source': Value('string'), '_Username': Value('string'), '_Created': Value('string'), '_Group': Value('string'), 'feature_dim': Value('int64'), 'noise_candidates': Value('int64'), 'actor_output_scale': Value('float64'), 'actor_learning_rate': Value('float64'), 'critic_learning_rate': Value('float64'), 'max_grad_norm': Value('float64'), 'base_checkpoint': Value('string'), 'selection_index': Value('string'), 'labels_index': Value('string'), 'dataset': {'tasks': List(Value('string')), 'episodes': Value('int64'), 'frames': Value('int64'), 'source_frames': {'red_soda_can': Value('int64'), 'sber_ring_box': Value('int64'), 'sugar_bowl': Value('int64')}, 'reward_source': Value('string'), 'sampling': Value('string')}, 'seed': Value('int64'), 'candidate_microbatch': Value('int64'), 'fuse_frozen_batches': Value('bool'), 'target_policy_noise': Value('float64'), 'monte_carlo_loss_weight': Value('float64'), 'bc_regularizer': Value('string'), 'actor_weight_decay': Value('float64'), 'monte_carlo_return_mode': Value('string'), 'action_contrast_weight': Value('float64'), 'action_contrast_temporal': Value('bool'), 'action_contrast_noise_std': Value('float64'), 'action_contrast_candidates': Value('bool'), 'action_contrast_margin': Value('float64'), 'executed_row_start': Value('int64'), 'executed_row_end': Value('int64'), 'demo_residual_weight': Value('float64'), 'feature_layernorm': Value('bool')}
              because column names don't match

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