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The dataset generation failed
Error code: DatasetGenerationError
Exception: TypeError
Message: 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)}
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 1529, 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 1154, 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? Make sure to review how to configure the dataset viewer, and 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_run... | {
"start_epoch": 1685393600.8396957,
"results": {
"rouge1": 0.4203825767319269,
"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
} | {
"start_epoch": 1685398882.9181912,
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
"loss": 1.92,
"learning_rate": 0.001,
"epoch": 1,
"step": 1842,
"train_runti... | {
"start_epoch": 1685401776.234568,
"results": {
"rouge1": 0.41739200377936836,
"rouge2": 0.16806568665530175,
"rougeL": 0.3317583163688007,
"rougeLsum": 0.3315065604661238
},
"elasped_time": 1422.7693049907684
} |
{
"model_size": "Small",
"FormSubmitter:Configuration-Submit Run": true,
"optim_name": "AdamW 32-bit",
"n_epochs": 10,
"limit_samples": false
} | {
"start_epoch": 1685561430.336439,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 11.3593168258667
} | {
"start_epoch": 1685561441.696393,
"verify_torch_installation": {
"is_cuda_available": true,
"cuda_device": "NVIDIA A10G"
},
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
... | {
"start_epoch": 1685563035.9584875,
"results": {
"rouge1": 0.41531916115994516,
"rouge2": 0.16192351962587326,
"rougeL": 0.32742390260557797,
"rougeLsum": 0.32707491244140957
},
"elasped_time": 1495.3234617710114
} |
{
"model_size": "Small",
"limit_samples": false,
"optim_name": "AdamW 8-bit",
"FormSubmitter:Configuration-Submit Run": true,
"n_epochs": 10
} | {
"start_epoch": 1685564640.452059,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 11.005797147750854
} | {
"start_epoch": 1685564651.4583197,
"verify_torch_installation": {
"is_cuda_available": true,
"cuda_device": "NVIDIA A10G"
},
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
... | {
"start_epoch": 1685566248.557421,
"results": {
"rouge1": 0.3976970227954125,
"rouge2": 0.15240325599158291,
"rougeL": 0.3142343799776748,
"rougeLsum": 0.31421092891324676
},
"elasped_time": 1482.8388330936432
} |
{
"limit_samples": false,
"model_size": "Small",
"optim_name": "Lion 32-bit",
"FormSubmitter:Configuration-Submit Run": true,
"n_epochs": 10
} | {
"start_epoch": 1685568292.8175752,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 10.799864292144775
} | {
"start_epoch": 1685568303.6178327,
"verify_torch_installation": {
"is_cuda_available": true,
"cuda_device": "NVIDIA A10G"
},
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
... | {
"start_epoch": 1685569891.4363928,
"results": {
"rouge1": 0.40976329078150037,
"rouge2": 0.1580715611472297,
"rougeL": 0.32446756739931526,
"rougeLsum": 0.32451437722965304
},
"elasped_time": 1390.5557925701141
} |
{
"optim_name": "AdamW 32-bit",
"model_size": "Small",
"n_epochs": 1
} | {
"start_epoch": 1687875015.715664,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 7.184280633926392
} | {
"start_epoch": 1687875022.9000044,
"verify_torch_installation": {
"is_cuda_available": true,
"cuda_device": "NVIDIA A10G"
},
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
... | {
"start_epoch": 1687875183.8020654,
"results": {
"rouge1": 0.39280850805899814,
"rouge2": 0.14742448999141616,
"rougeL": 0.3147110920365568,
"rougeLsum": 0.31457765837639695
},
"elasped_time": 1081.7206673622131
} |
null | {
"start_epoch": 1687891793.05988,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 7.5830771923065186
} | {
"start_epoch": 1687891800.6430902,
"verify_torch_installation": {
"is_cuda_available": true,
"cuda_device": "NVIDIA A10G"
},
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
{
... | {
"start_epoch": 1687891960.924907,
"results": {
"rouge1": 0.3965927297580114,
"rouge2": 0.14600320409597428,
"rougeL": 0.3147717561001532,
"rougeLsum": 0.31460761856922453
},
"elasped_time": 1077.3855304718018
} |
null | {
"start_epoch": 1687960682.9612699,
"train_dataset_size": 14732,
"test_dataset_size": 819,
"max_source_length": 255,
"max_target_length": 50,
"elasped_time": 8.253013134002686
} | {
"start_epoch": 1687960691.214331,
"verify_torch_installation": {
"is_cuda_available": true,
"cuda_device": "NVIDIA A10G"
},
"trainable_parameters_summary": {
"trainable_params": 688128,
"all_params": 77649280,
"trainable_params_pct": 0.008862001038515747
},
"trainer_log": [
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