Datasets:

---

slxhere

/

GPR

like 0

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
eta_seconds: double
lr: double
step: int64
step_time: double
train_loss: double
train_top1: double
train_top5: double
val_loss: double
val_top1: double
val_top5: double
min_lr_ratio: double
pretrained: bool
backbone: string
sparsity_reg: double
config: string
resume: bool
aggregation: string
fp16_compression: bool
gate_bottleneck: int64
dataset: string
root: string
sgd_momentum: double
workers: int64
entropy_reg: bool
mid_layer_anchor_weight: double
max_steps: int64
save_period: int64
seed: int64
log_period: int64
attention_bias_init: double
broadcast_buffers: bool
save_best: bool
init_checkpoint: string
warmup_ratio: double
output_dir: string
attention_bias: bool
device: string
pretrained_source_backbone: string
amp: bool
epochs: int64
keep_last: int64
mid_layer_anchor_layer: int64
sparsity_reg_type: string
dry_run_batches: int64
specialize_norms: bool
topk: int64
beta2: double
optimizer_name: string
clip_stats: bool
grad_accum_steps: int64
image_size: int64
specialize_first_blocks: int64
freeze_backbone: bool
eval_batch_size: int64
entropy_reg_weight: double
entropy_reg_last_k: int64
batch_size: int64
eval_period: int64
weight_decay: double
register_init: string
beta1: double
score_formula: string
sparse_cls_bias: bool
patch_cls_cossim_reg_weight: double
warmup_factor: double
specialize_qkv: bool
find_unused_parameters: bool
cls_gate: bool
to
{'aggregation': Value('string'), 'amp': Value('bool'), 'attention_bias': Value('bool'), 'attention_bias_init': Value('float64'), 'backbone': Value('string'), 'batch_size': Value('int64'), 'beta1': Value('float64'), 'beta2': Value('float64'), 'broadcast_buffers': Value('bool'), 'clip_stats': Value('bool'), 'cls_gate': Value('bool'), 'config': Value('string'), 'dataset': Value('string'), 'device': Value('string'), 'dry_run_batches': Value('int64'), 'entropy_reg': Value('bool'), 'entropy_reg_last_k': Value('int64'), 'entropy_reg_weight': Value('float64'), 'epochs': Value('int64'), 'eval_batch_size': Value('int64'), 'eval_period': Value('int64'), 'find_unused_parameters': Value('bool'), 'fp16_compression': Value('bool'), 'freeze_backbone': Value('bool'), 'gate_bottleneck': Value('int64'), 'grad_accum_steps': Value('int64'), 'image_size': Value('int64'), 'init_checkpoint': Value('string'), 'keep_last': Value('int64'), 'log_period': Value('int64'), 'lr': Value('float64'), 'max_steps': Value('int64'), 'mid_layer_anchor_layer': Value('int64'), 'mid_layer_anchor_weight': Value('float64'), 'min_lr_ratio': Value('float64'), 'optimizer_name': Value('string'), 'output_dir': Value('string'), 'patch_cls_cossim_reg_weight': Value('float64'), 'pretrained': Value('bool'), 'pretrained_source_backbone': Value('string'), 'register_init': Value('string'), 'resume': Value('bool'), 'root': Value('string'), 'save_best': Value('bool'), 'save_period': Value('int64'), 'score_formula': Value('string'), 'seed': Value('int64'), 'sgd_momentum': Value('float64'), 'sparse_cls_bias': Value('bool'), 'sparsity_reg': Value('float64'), 'sparsity_reg_type': Value('string'), 'specialize_first_blocks': Value('int64'), 'specialize_norms': Value('bool'), 'specialize_qkv': Value('bool'), 'topk': Value('int64'), 'warmup_factor': Value('float64'), 'warmup_ratio': Value('float64'), 'weight_decay': Value('float64'), 'workers': Value('int64')}
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 295, 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 128, 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 2281, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2227, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              eta_seconds: double
              lr: double
              step: int64
              step_time: double
              train_loss: double
              train_top1: double
              train_top5: double
              val_loss: double
              val_top1: double
              val_top5: double
              min_lr_ratio: double
              pretrained: bool
              backbone: string
              sparsity_reg: double
              config: string
              resume: bool
              aggregation: string
              fp16_compression: bool
              gate_bottleneck: int64
              dataset: string
              root: string
              sgd_momentum: double
              workers: int64
              entropy_reg: bool
              mid_layer_anchor_weight: double
              max_steps: int64
              save_period: int64
              seed: int64
              log_period: int64
              attention_bias_init: double
              broadcast_buffers: bool
              save_best: bool
              init_checkpoint: string
              warmup_ratio: double
              output_dir: string
              attention_bias: bool
              device: string
              pretrained_source_backbone: string
              amp: bool
              epochs: int64
              keep_last: int64
              mid_layer_anchor_layer: int64
              sparsity_reg_type: string
              dry_run_batches: int64
              specialize_norms: bool
              topk: int64
              beta2: double
              optimizer_name: string
              clip_stats: bool
              grad_accum_steps: int64
              image_size: int64
              specialize_first_blocks: int64
              freeze_backbone: bool
              eval_batch_size: int64
              entropy_reg_weight: double
              entropy_reg_last_k: int64
              batch_size: int64
              eval_period: int64
              weight_decay: double
              register_init: string
              beta1: double
              score_formula: string
              sparse_cls_bias: bool
              patch_cls_cossim_reg_weight: double
              warmup_factor: double
              specialize_qkv: bool
              find_unused_parameters: bool
              cls_gate: bool
              to
              {'aggregation': Value('string'), 'amp': Value('bool'), 'attention_bias': Value('bool'), 'attention_bias_init': Value('float64'), 'backbone': Value('string'), 'batch_size': Value('int64'), 'beta1': Value('float64'), 'beta2': Value('float64'), 'broadcast_buffers': Value('bool'), 'clip_stats': Value('bool'), 'cls_gate': Value('bool'), 'config': Value('string'), 'dataset': Value('string'), 'device': Value('string'), 'dry_run_batches': Value('int64'), 'entropy_reg': Value('bool'), 'entropy_reg_last_k': Value('int64'), 'entropy_reg_weight': Value('float64'), 'epochs': Value('int64'), 'eval_batch_size': Value('int64'), 'eval_period': Value('int64'), 'find_unused_parameters': Value('bool'), 'fp16_compression': Value('bool'), 'freeze_backbone': Value('bool'), 'gate_bottleneck': Value('int64'), 'grad_accum_steps': Value('int64'), 'image_size': Value('int64'), 'init_checkpoint': Value('string'), 'keep_last': Value('int64'), 'log_period': Value('int64'), 'lr': Value('float64'), 'max_steps': Value('int64'), 'mid_layer_anchor_layer': Value('int64'), 'mid_layer_anchor_weight': Value('float64'), 'min_lr_ratio': Value('float64'), 'optimizer_name': Value('string'), 'output_dir': Value('string'), 'patch_cls_cossim_reg_weight': Value('float64'), 'pretrained': Value('bool'), 'pretrained_source_backbone': Value('string'), 'register_init': Value('string'), 'resume': Value('bool'), 'root': Value('string'), 'save_best': Value('bool'), 'save_period': Value('int64'), 'score_formula': Value('string'), 'seed': Value('int64'), 'sgd_momentum': Value('float64'), 'sparse_cls_bias': Value('bool'), 'sparsity_reg': Value('float64'), 'sparsity_reg_type': Value('string'), 'specialize_first_blocks': Value('int64'), 'specialize_norms': Value('bool'), 'specialize_qkv': Value('bool'), 'topk': Value('int64'), 'warmup_factor': Value('float64'), 'warmup_ratio': Value('float64'), 'weight_decay': Value('float64'), 'workers': Value('int64')}
              because column names don't match