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The dataset generation failed
Error code: DatasetGenerationError
Exception: DatasetGenerationError
Message: An error occurred while generating the dataset
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 585, in write_table
pa_table = table_cast(pa_table, self._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 2261, in cast_table_to_schema
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2261, in <listcomp>
arrays = [cast_array_to_feature(table[name], feature) for name, feature in features.items()]
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 1802, in <listcomp>
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2122, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<start_epoch: double, verify_torch_installation: struct<is_cuda_available: bool, cuda_device: string>, trainable_parameters_summary: struct<trainable_params: int64, all_params: int64, trainable_params_pct: double>, trainer_log: list<item: struct<loss: double, learning_rate: double, epoch: double, step: int64, train_runtime: double, train_samples_per_second: double, train_steps_per_second: double, total_flos: double, train_loss: double>>, model_memory_footprint: int64, cuda_memory_stats: struct<active.all.allocated: int64, active.all.current: int64, active.all.freed: int64, active.all.peak: int64, active.large_pool.allocated: int64, active.large_pool.current: int64, active.large_pool.freed: int64, active.large_pool.peak: int64, active.small_pool.allocated: int64, active.small_pool.current: int64, active.small_pool.freed: int64, active.small_pool.peak: int64, active_bytes.all.allocated: int64, active_bytes.all.current: int64, active_bytes.all.freed: int64, active_bytes.all.peak: int64, active_bytes.large_pool.allocated: int64, active_bytes.large_pool.current: int64, active_bytes.large_pool.freed: int64, active_bytes.large_pool.peak: int64, active_bytes.small_pool.allocated: int64, active_bytes.small_pool.current: int64, active_bytes.small_pool.freed: int64, active_bytes.small_pool.peak: int64, allocated_bytes.all.allocated: int64, allocated_bytes.all.current: int64, allocated_bytes.all.freed: int64, allocated_bytes.all.peak: int64, allocated_bytes.large_pool.allocated: in
...
segments.allocated: int64, oversize_segments.current: int64, oversize_segments.freed: int64, oversize_segments.peak: int64, requested_bytes.all.allocated: int64, requested_bytes.all.current: int64, requested_bytes.all.freed: int64, requested_bytes.all.peak: int64, requested_bytes.large_pool.allocated: int64, requested_bytes.large_pool.current: int64, requested_bytes.large_pool.freed: int64, requested_bytes.large_pool.peak: int64, requested_bytes.small_pool.allocated: int64, requested_bytes.small_pool.current: int64, requested_bytes.small_pool.freed: int64, requested_bytes.small_pool.peak: int64, reserved_bytes.all.allocated: int64, reserved_bytes.all.current: int64, reserved_bytes.all.freed: int64, reserved_bytes.all.peak: int64, reserved_bytes.large_pool.allocated: int64, reserved_bytes.large_pool.current: int64, reserved_bytes.large_pool.freed: int64, reserved_bytes.large_pool.peak: int64, reserved_bytes.small_pool.allocated: int64, reserved_bytes.small_pool.current: int64, reserved_bytes.small_pool.freed: int64, reserved_bytes.small_pool.peak: int64, segment.all.allocated: int64, segment.all.current: int64, segment.all.freed: int64, segment.all.peak: int64, segment.large_pool.allocated: int64, segment.large_pool.current: int64, segment.large_pool.freed: int64, segment.large_pool.peak: int64, segment.small_pool.allocated: int64, segment.small_pool.current: int64, segment.small_pool.freed: int64, segment.small_pool.peak: int64>, train_batch_size: int64, elasped_time: double>
to
{'start_epoch': Value(dtype='float64', id=None), 'trainable_parameters_summary': {'trainable_params': Value(dtype='int64', id=None), 'all_params': Value(dtype='int64', id=None), 'trainable_params_pct': Value(dtype='float64', id=None)}, 'trainer_log': [{'loss': Value(dtype='float64', id=None), 'learning_rate': Value(dtype='float64', id=None), 'epoch': Value(dtype='float64', id=None), 'step': Value(dtype='int64', id=None), 'train_runtime': Value(dtype='float64', id=None), 'train_samples_per_second': Value(dtype='float64', id=None), 'train_steps_per_second': Value(dtype='float64', id=None), 'total_flos': Value(dtype='float64', id=None), 'train_loss': Value(dtype='float64', id=None)}], 'model_memory_footprint': Value(dtype='int64', id=None), 'cuda_memory_stats': {'active.all.allocated': Value(dtype='int64', id=None), 'active.all.current': Value(dtype='int64', id=None), 'active.all.freed': Value(dtype='int64', id=None), 'active.all.peak': Value(dtype='int64', id=None), 'active.large_pool.allocated': Value(dtype='int64', id=None), 'active.large_pool.current': Value(dtype='int64', id=None), 'active.large_pool.freed': Value(dtype='int64', id=None), 'active.large_pool.peak': Value(dtype='int64', id=None), 'active.small_pool.allocated': Value(dtype='int64', id=None), 'active.small_pool.current': Value(dtype='int64', id=None), 'active.small_pool.freed': Value(dtype='int64', id=None), 'active.small_pool.peak': Value(dtype='int64', id=None), 'active_bytes.all.allocated': Value(dtype='int6
...
ocated': Value(dtype='int64', id=None), 'reserved_bytes.all.current': Value(dtype='int64', id=None), 'reserved_bytes.all.freed': Value(dtype='int64', id=None), 'reserved_bytes.all.peak': Value(dtype='int64', id=None), 'reserved_bytes.large_pool.allocated': Value(dtype='int64', id=None), 'reserved_bytes.large_pool.current': Value(dtype='int64', id=None), 'reserved_bytes.large_pool.freed': Value(dtype='int64', id=None), 'reserved_bytes.large_pool.peak': Value(dtype='int64', id=None), 'reserved_bytes.small_pool.allocated': Value(dtype='int64', id=None), 'reserved_bytes.small_pool.current': Value(dtype='int64', id=None), 'reserved_bytes.small_pool.freed': Value(dtype='int64', id=None), 'reserved_bytes.small_pool.peak': Value(dtype='int64', id=None), 'segment.all.allocated': Value(dtype='int64', id=None), 'segment.all.current': Value(dtype='int64', id=None), 'segment.all.freed': Value(dtype='int64', id=None), 'segment.all.peak': Value(dtype='int64', id=None), 'segment.large_pool.allocated': Value(dtype='int64', id=None), 'segment.large_pool.current': Value(dtype='int64', id=None), 'segment.large_pool.freed': Value(dtype='int64', id=None), 'segment.large_pool.peak': Value(dtype='int64', id=None), 'segment.small_pool.allocated': Value(dtype='int64', id=None), 'segment.small_pool.current': Value(dtype='int64', id=None), 'segment.small_pool.freed': Value(dtype='int64', id=None), 'segment.small_pool.peak': Value(dtype='int64', id=None)}, 'elasped_time': Value(dtype='float64', id=None)}
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1321, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 935, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1027, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1122, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1882, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 2038, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Open a discussion for direct support.
streamlit_config
dict | elt
dict | train
dict | eval
dict |
|---|---|---|---|
{
"n_epochs": 5,
"optim_name": "Lion",
"limit_samples": false,
"model_size": "Small",
"FormSubmitter:Configuration-Submit Run": true
} | {
"start_epoch": 1685390675.1638606,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 10.796925067901611
} | {
"start_epoch": 1685390685.9612148,
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
"loss": 1.917,
"learning_rate": 0.0001,
"epoch": 1,
"step": 1842,
"train_runtime": null,
"train_samples_per_second": null,
"train_steps_per_second": null,
"total_flos": null,
"train_loss": null
},
{
"loss": 1.8827,
"learning_rate": 0.0001,
"epoch": 2,
"step": 3684,
"train_runtime": null,
"train_samples_per_second": null,
"train_steps_per_second": null,
"total_flos": null,
"train_loss": null
},
{
"loss": 1.8599,
"learning_rate": 0.0001,
"epoch": 3,
"step": 5526,
"train_runtime": null,
"train_samples_per_second": null,
"train_steps_per_second": null,
"total_flos": null,
"train_loss": null
},
{
"loss": 1.84,
"learning_rate": 0.0001,
"epoch": 4,
"step": 7368,
"train_runtime": null,
"train_samples_per_second": null,
"train_steps_per_second": null,
"total_flos": null,
"train_loss": null
},
{
"loss": 1.8223,
"learning_rate": 0.0001,
"epoch": 5,
"step": 9210,
"train_runtime": null,
"train_samples_per_second": null,
"train_steps_per_second": null,
"total_flos": null,
"train_loss": null
},
{
"loss": null,
"learning_rate": null,
"epoch": 5,
"step": 9210,
"train_runtime": 2908.9079,
"train_samples_per_second": 25.322,
"train_steps_per_second": 3.166,
"total_flos": 6924203301273600,
"train_loss": 1.8643896113260043
}
],
"model_memory_footprint": 137843456,
"cuda_memory_stats": {
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} | {
"start_epoch": 1685393600.8396957,
"results": {
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"rouge2": 0.16367781268003684,
"rougeL": 0.3317591720505857,
"rougeLsum": 0.33125763901336547
},
"elasped_time": 4390.246840238571
} |
{
"model_size": "Small",
"FormSubmitter:Configuration-Submit Run": true,
"limit_samples": false,
"optim_name": "AdamW",
"n_epochs": 5
} | {
"start_epoch": 1685398872.1328888,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 10.78488564491272
} | {
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},
"trainer_log": [
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},
{
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},
{
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{
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}
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