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Error code: StreamingRowsError Exception: CastError Message: Couldn't cast stake: float emission: float trust: float validator_trust: float dividends: float incentive: float ranks: float total_stake: float consensus: float validator_permit: bool last_update: int64 block: int64 netuid: int64 uid: int64 hotkey: string coldkey: string ip: string timestamp: timestamp[ns] __index_level_0__: int64 -- schema metadata -- pandas: '{"index_columns": ["__index_level_0__"], "column_indexes": [{"na' + 2412 to {'mean_forward_time': Value(dtype='float64', id=None), 'task_distribution': Value(dtype='float64', id=None), 'mean_query_length': Value(dtype='float64', id=None), 'mean_reference_tokens_per_second': Value(dtype='float64', id=None), 'mean_query_tokens_per_second': Value(dtype='float64', id=None), 'unique_challenges': Value(dtype='float64', id=None), 'mean_reward': Value(dtype='float64', id=None), 'zero_rewards': Value(dtype='float64', id=None), 'status_codes': Value(dtype='float64', id=None), 'completion_tokens_per_second': Value(dtype='float64', id=None), '__index_level_0__': Value(dtype='string', id=None)} because column names don't match Traceback: Traceback (most recent call last): File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 437, in query pa_table = pa.concat_tables( File "pyarrow/table.pxi", line 5317, in pyarrow.lib.concat_tables File "pyarrow/error.pxi", line 154, in pyarrow.lib.pyarrow_internal_check_status File "pyarrow/error.pxi", line 91, in pyarrow.lib.check_status pyarrow.lib.ArrowInvalid: Schema at index 1 was different: mean_forward_time: double task_distribution: double mean_query_length: double mean_reference_tokens_per_second: double mean_query_tokens_per_second: double unique_challenges: double mean_reward: double zero_rewards: double status_codes: double completion_tokens_per_second: double __index_level_0__: string vs __index_level_0__: int64 During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 105, in get_rows_content pa_table = rows_index.query(offset=0, length=rows_max_number) File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 586, in query return self.parquet_index.query(offset=offset, length=length) File "/src/libs/libcommon/src/libcommon/parquet_utils.py", line 444, in query raise SchemaMismatchError("Parquet files have different schema.", err) libcommon.parquet_utils.SchemaMismatchError: ('Parquet files have different schema.', ArrowInvalid('Schema at index 1 was different: \nmean_forward_time: double\ntask_distribution: double\nmean_query_length: double\nmean_reference_tokens_per_second: double\nmean_query_tokens_per_second: double\nunique_challenges: double\nmean_reward: double\nzero_rewards: double\nstatus_codes: double\ncompletion_tokens_per_second: double\n__index_level_0__: string\nvs\n__index_level_0__: int64')) During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 322, in compute compute_first_rows_from_parquet_response( File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 119, in compute_first_rows_from_parquet_response return create_first_rows_response( File "/src/libs/libcommon/src/libcommon/viewer_utils/rows.py", line 135, in create_first_rows_response rows_content = get_rows_content(rows_max_number) File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 114, in get_rows_content raise SplitParquetSchemaMismatchError( libcommon.exceptions.SplitParquetSchemaMismatchError: Split parquet files being processed have different schemas. Ensure all files have identical column names. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "/src/services/worker/src/worker/utils.py", line 96, in get_rows_or_raise return get_rows( File "/src/libs/libcommon/src/libcommon/utils.py", line 183, in decorator return func(*args, **kwargs) File "/src/services/worker/src/worker/utils.py", line 73, in get_rows rows_plus_one = list(itertools.islice(ds, rows_max_number + 1)) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1389, in __iter__ for key, example in ex_iterable: File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 282, in __iter__ for key, pa_table in self.generate_tables_fn(**self.kwargs): File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 97, in _generate_tables yield f"{file_idx}_{batch_idx}", self._cast_table(pa_table) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/packaged_modules/parquet/parquet.py", line 75, in _cast_table pa_table = table_cast(pa_table, self.info.features.arrow_schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2302, in table_cast return cast_table_to_schema(table, schema) File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2256, in cast_table_to_schema raise CastError( datasets.table.CastError: Couldn't cast stake: float emission: float trust: float validator_trust: float dividends: float incentive: float ranks: float total_stake: float consensus: float validator_permit: bool last_update: int64 block: int64 netuid: int64 uid: int64 hotkey: string coldkey: string ip: string timestamp: timestamp[ns] __index_level_0__: int64 -- schema metadata -- pandas: '{"index_columns": ["__index_level_0__"], "column_indexes": [{"na' + 2412 to {'mean_forward_time': Value(dtype='float64', id=None), 'task_distribution': Value(dtype='float64', id=None), 'mean_query_length': Value(dtype='float64', id=None), 'mean_reference_tokens_per_second': Value(dtype='float64', id=None), 'mean_query_tokens_per_second': Value(dtype='float64', id=None), 'unique_challenges': Value(dtype='float64', id=None), 'mean_reward': Value(dtype='float64', id=None), 'zero_rewards': Value(dtype='float64', id=None), 'status_codes': Value(dtype='float64', id=None), 'completion_tokens_per_second': Value(dtype='float64', id=None), '__index_level_0__': Value(dtype='string', id=None)} because column names don't match
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