Dataset Viewer

Split (3)

train

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
release_root: string
infer_exe: string
infer_config: string
enc_model: string
beats_artifact: string
staged_model_root: string
sample_path: string
backend_url: string
infer_url: string
staged_files: list<item: struct<target: string, source: string>>
  child 0, item: struct<target: string, source: string>
      child 0, target: string
      child 1, source: string
status: string
backend_stub_thread_alive: bool
infer_payload: struct<filename: string, anomaly_prob: double, result: string, n_segments: int64, threshold: double, (... 169 chars omitted)
  child 0, filename: string
  child 1, anomaly_prob: double
  child 2, result: string
  child 3, n_segments: int64
  child 4, threshold: double
  child 5, model: string
  child 6, timing_model: struct<decode_dispatch_ms: double, decode_ms: double, resample_ms: double, segment_ms: double, forwa (... 38 chars omitted)
      child 0, decode_dispatch_ms: double
      child 1, decode_ms: double
      child 2, resample_ms: double
      child 3, segment_ms: double
      child 4, forward_ms: double
      child 5, total_model_ms: double
trial_count: int64
trials: list<item: struct<trial_id: string, learning_rate: double, weight_decay: double, focal_gamma: double (... 101 chars omitted)
  child 0, item: struct<trial_id: string, learning_rate: double, weight_decay: double, focal_gamma: double, anomaly_w (... 89 chars omitted)
      child 0, trial_id: string
      child 1, learning_rate: double
      child 2, weight_decay: double
      child 3, focal_gamma: double
      child 4, anomaly_weight: double
      child 5, batch_size: int64
      child 6, seed: int64
      child 7, epochs: int64
      child 8, early_stop_patience: int64
selected_training_strategies: list<item: string>
  child 0, item: string
budget: string
to
{'status': Value('string'), 'budget': Value('string'), 'trial_count': Value('int64'), 'trials': List({'trial_id': Value('string'), 'learning_rate': Value('float64'), 'weight_decay': Value('float64'), 'focal_gamma': Value('float64'), 'anomaly_weight': Value('float64'), 'batch_size': Value('int64'), 'seed': Value('int64'), 'epochs': Value('int64'), 'early_stop_patience': Value('int64')}), 'selected_training_strategies': List(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 299, 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 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              release_root: string
              infer_exe: string
              infer_config: string
              enc_model: string
              beats_artifact: string
              staged_model_root: string
              sample_path: string
              backend_url: string
              infer_url: string
              staged_files: list<item: struct<target: string, source: string>>
                child 0, item: struct<target: string, source: string>
                    child 0, target: string
                    child 1, source: string
              status: string
              backend_stub_thread_alive: bool
              infer_payload: struct<filename: string, anomaly_prob: double, result: string, n_segments: int64, threshold: double, (... 169 chars omitted)
                child 0, filename: string
                child 1, anomaly_prob: double
                child 2, result: string
                child 3, n_segments: int64
                child 4, threshold: double
                child 5, model: string
                child 6, timing_model: struct<decode_dispatch_ms: double, decode_ms: double, resample_ms: double, segment_ms: double, forwa (... 38 chars omitted)
                    child 0, decode_dispatch_ms: double
                    child 1, decode_ms: double
                    child 2, resample_ms: double
                    child 3, segment_ms: double
                    child 4, forward_ms: double
                    child 5, total_model_ms: double
              trial_count: int64
              trials: list<item: struct<trial_id: string, learning_rate: double, weight_decay: double, focal_gamma: double (... 101 chars omitted)
                child 0, item: struct<trial_id: string, learning_rate: double, weight_decay: double, focal_gamma: double, anomaly_w (... 89 chars omitted)
                    child 0, trial_id: string
                    child 1, learning_rate: double
                    child 2, weight_decay: double
                    child 3, focal_gamma: double
                    child 4, anomaly_weight: double
                    child 5, batch_size: int64
                    child 6, seed: int64
                    child 7, epochs: int64
                    child 8, early_stop_patience: int64
              selected_training_strategies: list<item: string>
                child 0, item: string
              budget: string
              to
              {'status': Value('string'), 'budget': Value('string'), 'trial_count': Value('int64'), 'trials': List({'trial_id': Value('string'), 'learning_rate': Value('float64'), 'weight_decay': Value('float64'), 'focal_gamma': Value('float64'), 'anomaly_weight': Value('float64'), 'batch_size': Value('int64'), 'seed': Value('int64'), 'epochs': Value('int64'), 'early_stop_patience': Value('int64')}), 'selected_training_strategies': List(Value('string'))}
              because column names don't match

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Check out the documentation for more information.

SAS4 TrainPlat Current-Best Artifacts

This repo stores the promoted TrainPlat current-best model instance and lightweight reproducibility proof for SAS4 release/dev synchronization.

Current Best

Runtime threshold: 0.535
.enc sha256: 00476CEA54DE9F6F28AB3B2AC70BCCE1A7A8940FB6CF0A36B7FA3A7656B4E6F8
Training strategy: continue_from_current_best_seed7
Backend profile: compat_cuda_proxy

Layout

current-best/best_model.pt.enc
current-best/best_model.portable-v2.pt
current-best/model_card.toml
proof/current-best-proof
proof/release-validation
proof/training-record

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Total file size:

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