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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'model_kwargs' with no child field to Parquet. Consider adding a dummy child field.
Traceback: Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1870, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 620, in write_table
self._build_writer(inferred_schema=pa_table.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 441, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'model_kwargs' with no child field to Parquet. Consider adding a dummy child field.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1886, in _prepare_split_single
num_examples, num_bytes = writer.finalize()
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 639, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 441, in _build_writer
self.pa_writer = self._WRITER_CLASS(self.stream, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/pyarrow/parquet/core.py", line 1010, in __init__
self.writer = _parquet.ParquetWriter(
File "pyarrow/_parquet.pyx", line 2157, in pyarrow._parquet.ParquetWriter.__cinit__
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.ArrowNotImplementedError: Cannot write struct type 'model_kwargs' with no child field to Parquet. Consider adding a dummy child field.
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 1417, 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 1049, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 924, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1000, 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 1741, 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 1897, 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.
config
dict | report
dict | name
string | backend
dict | scenario
dict | launcher
dict | environment
dict | print_report
bool | log_report
bool | overall
dict | warmup
dict | train
dict |
|---|---|---|---|---|---|---|---|---|---|---|---|
{
"name": "cuda_training_transformers_fill-mask_google-bert/bert-base-uncased",
"backend": {
"name": "pytorch",
"version": "2.5.1+cu124",
"_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
"task": "fill-mask",
"library": "transformers",
"model_type": "bert",
"model": "google-bert/bert-base-uncased",
"processor": "google-bert/bert-base-uncased",
"device": "cuda",
"device_ids": "0",
"seed": 42,
"inter_op_num_threads": null,
"intra_op_num_threads": null,
"model_kwargs": {},
"processor_kwargs": {},
"no_weights": true,
"device_map": null,
"torch_dtype": null,
"eval_mode": true,
"to_bettertransformer": false,
"low_cpu_mem_usage": null,
"attn_implementation": null,
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"autocast_enabled": false,
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"torch_compile": false,
"torch_compile_target": "forward",
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"quantization_config": {},
"deepspeed_inference": false,
"deepspeed_inference_config": {},
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},
"scenario": {
"name": "training",
"_target_": "optimum_benchmark.scenarios.training.scenario.TrainingScenario",
"max_steps": 5,
"warmup_steps": 2,
"dataset_shapes": {
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"sequence_length": 16,
"num_choices": 1
},
"training_arguments": {
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"gradient_accumulation_steps": 1,
"output_dir": "./trainer_output",
"evaluation_strategy": "no",
"eval_strategy": "no",
"save_strategy": "no",
"do_train": true,
"use_cpu": false,
"max_steps": 5,
"do_eval": false,
"do_predict": false,
"report_to": "none",
"skip_memory_metrics": true,
"ddp_find_unused_parameters": false
},
"latency": true,
"memory": true,
"energy": true
},
"launcher": {
"name": "process",
"_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher",
"device_isolation": true,
"device_isolation_action": "error",
"numactl": false,
"numactl_kwargs": {},
"start_method": "spawn"
},
"environment": {
"cpu": " AMD EPYC 7R32",
"cpu_count": 16,
"cpu_ram_mb": 66697.248768,
"system": "Linux",
"machine": "x86_64",
"platform": "Linux-5.10.228-219.884.amzn2.x86_64-x86_64-with-glibc2.35",
"processor": "x86_64",
"python_version": "3.10.12",
"gpu": [
"NVIDIA A10G"
],
"gpu_count": 1,
"gpu_vram_mb": 24146608128,
"optimum_benchmark_version": "0.5.0.dev0",
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"transformers_version": "4.47.0",
"transformers_commit": null,
"accelerate_version": "1.2.0",
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"diffusers_version": "0.31.0",
"diffusers_commit": null,
"optimum_version": null,
"optimum_commit": null,
"timm_version": "1.0.12",
"timm_commit": null,
"peft_version": "0.14.0",
"peft_commit": null
},
"print_report": true,
"log_report": true
} | {
"overall": {
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"max_process_vram": 0,
"max_reserved": 2520.776704,
"max_allocated": 2211.86048
},
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}
} | null | null | null | null | null | null | null | null | null | null |
null | null | cuda_training_transformers_fill-mask_google-bert/bert-base-uncased | {
"name": "pytorch",
"version": "2.5.1+cu124",
"_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
"task": "fill-mask",
"library": "transformers",
"model_type": "bert",
"model": "google-bert/bert-base-uncased",
"processor": "google-bert/bert-base-uncased",
"device": "cuda",
"device_ids": "0",
"seed": 42,
"inter_op_num_threads": null,
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"model_kwargs": {},
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"no_weights": true,
"device_map": null,
"torch_dtype": null,
"eval_mode": true,
"to_bettertransformer": false,
"low_cpu_mem_usage": null,
"attn_implementation": null,
"cache_implementation": null,
"autocast_enabled": false,
"autocast_dtype": null,
"torch_compile": false,
"torch_compile_target": "forward",
"torch_compile_config": {},
"quantization_scheme": null,
"quantization_config": {},
"deepspeed_inference": false,
"deepspeed_inference_config": {},
"peft_type": null,
"peft_config": {}
} | {
"name": "training",
"_target_": "optimum_benchmark.scenarios.training.scenario.TrainingScenario",
"max_steps": 5,
"warmup_steps": 2,
"dataset_shapes": {
"dataset_size": 500,
"sequence_length": 16,
"num_choices": 1
},
"training_arguments": {
"per_device_train_batch_size": 2,
"gradient_accumulation_steps": 1,
"output_dir": "./trainer_output",
"evaluation_strategy": "no",
"eval_strategy": "no",
"save_strategy": "no",
"do_train": true,
"use_cpu": false,
"max_steps": 5,
"do_eval": false,
"do_predict": false,
"report_to": "none",
"skip_memory_metrics": true,
"ddp_find_unused_parameters": false
},
"latency": true,
"memory": true,
"energy": true
} | {
"name": "process",
"_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher",
"device_isolation": true,
"device_isolation_action": "error",
"numactl": false,
"numactl_kwargs": {},
"start_method": "spawn"
} | {
"cpu": " AMD EPYC 7R32",
"cpu_count": 16,
"cpu_ram_mb": 66697.248768,
"system": "Linux",
"machine": "x86_64",
"platform": "Linux-5.10.228-219.884.amzn2.x86_64-x86_64-with-glibc2.35",
"processor": "x86_64",
"python_version": "3.10.12",
"gpu": [
"NVIDIA A10G"
],
"gpu_count": 1,
"gpu_vram_mb": 24146608128,
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"transformers_version": "4.47.0",
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"accelerate_version": "1.2.0",
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"optimum_commit": null,
"timm_version": "1.0.12",
"timm_commit": null,
"peft_version": "0.14.0",
"peft_commit": null
} | true | true | null | null | null |
null | null | null | null | null | null | null | null | null | {
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},
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} | {
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} |
{
"name": "cuda_training_transformers_fill-mask_google-bert/bert-base-uncased",
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"_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
"task": "fill-mask",
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"device_ids": "0",
"seed": 42,
"inter_op_num_threads": null,
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"hub_kwargs": {
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"force_download": false,
"local_files_only": false,
"trust_remote_code": false
},
"no_weights": true,
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},
"scenario": {
"name": "training",
"_target_": "optimum_benchmark.scenarios.training.scenario.TrainingScenario",
"max_steps": 5,
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"dataset_shapes": {
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},
"training_arguments": {
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"output_dir": "./trainer_output",
"do_train": true,
"use_cpu": false,
"max_steps": 5,
"do_eval": false,
"do_predict": false,
"report_to": "none",
"skip_memory_metrics": true,
"ddp_find_unused_parameters": false
},
"latency": true,
"memory": true,
"energy": false
},
"launcher": {
"name": "process",
"_target_": "optimum_benchmark.launchers.process.launcher.ProcessLauncher",
"device_isolation": true,
"device_isolation_action": "error",
"start_method": "spawn"
},
"environment": {
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],
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}
} | {
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},
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},
"train": {
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},
"throughput": {
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"value": 130.4470066499722
},
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}
} | null | null | null | null | null | null | null | null | null | null |
{
"name": "cuda_training_transformers_fill-mask_hf-internal-testing/tiny-random-BertModel",
"backend": {
"name": "pytorch",
"version": "2.5.1+cu124",
"_target_": "optimum_benchmark.backends.pytorch.backend.PyTorchBackend",
"model": "hf-internal-testing/tiny-random-BertModel",
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} | null | null | null | null | null | null | null | null | null | null |
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