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The dataset generation failed
Error code: DatasetGenerationError
Exception: ArrowNotImplementedError
Message: Cannot write struct type 'sampler_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 2011, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 583, 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 404, 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 'sampler_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 2027, 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 602, in finalize
self._build_writer(self.schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 404, 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 'sampler_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 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.
hp_activation_fn_1
dict | hp_activation_fn_2
dict | hp_batch_size
dict | hp_dropout_1
dict | hp_dropout_2
dict | hp_init_lr
dict | hp_lr_schedule
dict | hp_n_units_1
dict | hp_n_units_2
dict | hp_epoch
dict | metric_elapsed_time
string | metric_default
string | resource_attr
string | objectives_names
sequence | task_names
sequence | hp_num_layers
dict | hp_num_cells
dict | hp_dropout_rate
dict | hp_learning_rate
dict | hp_context_length_ratio
dict | seed_col
null | categorical_cols
sequence |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
"domain_cls": "Categorical",
"domain_kwargs": {
"categories": [
"tanh",
"relu"
]
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | {
"domain_cls": "Categorical",
"domain_kwargs": {
"categories": [
"tanh",
"relu"
]
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | {
"domain_cls": "FiniteRange",
"domain_kwargs": {
"lower": 8,
"upper": 64,
"log_scale": true,
"cast_int": true,
"size": 4
}
} | {
"domain_cls": "FiniteRange",
"domain_kwargs": {
"lower": 0,
"upper": 0.6,
"log_scale": false,
"cast_int": false,
"size": 3
}
} | {
"domain_cls": "FiniteRange",
"domain_kwargs": {
"lower": 0,
"upper": 0.6,
"log_scale": false,
"cast_int": false,
"size": 3
}
} | {
"domain_cls": "Categorical",
"domain_kwargs": {
"categories": [
0.0005,
0.001,
0.005,
0.01,
0.05,
0.1
]
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | {
"domain_cls": "Categorical",
"domain_kwargs": {
"categories": [
"cosine",
"const"
]
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | {
"domain_cls": "FiniteRange",
"domain_kwargs": {
"lower": 16,
"upper": 512,
"log_scale": true,
"cast_int": true,
"size": 6
}
} | {
"domain_cls": "FiniteRange",
"domain_kwargs": {
"lower": 16,
"upper": 512,
"log_scale": true,
"cast_int": true,
"size": 6
}
} | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | {
"domain_cls": "Integer",
"domain_kwargs": {
"lower": 1,
"upper": 100
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | metric_elapsed_time | metric_valid_loss | hp_epoch | [
"metric_valid_loss",
"metric_train_loss",
"metric_final_test_error",
"metric_n_params",
"metric_elapsed_time"
] | [
"protein_structure",
"naval_propulsion",
"parkinsons_telemonitoring",
"slice_localization"
] | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | {
"domain_cls": "Integer",
"domain_kwargs": {
"lower": 2,
"upper": 4
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | {
"domain_cls": "Integer",
"domain_kwargs": {
"lower": 10,
"upper": 10000
},
"sampler_cls": "LogUniform",
"sampler_kwargs": {
"base": 2.718281828459045
}
} | {
"domain_cls": "Float",
"domain_kwargs": {
"lower": 0.01,
"upper": 0.51
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | {
"domain_cls": "Float",
"domain_kwargs": {
"lower": 0.0001,
"upper": 0.01
},
"sampler_cls": "LogUniform",
"sampler_kwargs": {
"base": 2.718281828459045
}
} | {
"domain_cls": "Float",
"domain_kwargs": {
"lower": 0.05,
"upper": 4
},
"sampler_cls": "LogUniform",
"sampler_kwargs": {
"base": 2.718281828459045
}
} | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | [
"metric_CRPS",
"metric_train_loss",
"metric_throughput",
"metric_RMSE",
"metric_time"
] | [
"m4-Daily",
"exchange-rate",
"m4-Yearly",
"solar",
"m4-Monthly",
"electricity",
"traffic",
"m4-Quarterly",
"m4-Weekly",
"m4-Hourly",
"wiki-rolling"
] | null | null | null | null | null | null | [] |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | [
"metric_error"
] | [
"heart",
"w6a",
"skin_nonskin",
"svmguide1",
"spambase",
"australian",
"madelon",
"german.numer",
"ijcnn1",
"a6a"
] | null | null | null | null | null | null | [] |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | time | val_accuracy | epoch | [
"val_accuracy",
"val_balanced_accuracy",
"val_cross_entropy",
"test_accuracy",
"test_balanced_accuracy",
"test_cross_entropy",
"time"
] | [
"APSFailure",
"Amazon_employee_access",
"Australian",
"Fashion-MNIST",
"KDDCup09_appetency",
"MiniBooNE",
"adult",
"airlines",
"albert",
"bank-marketing",
"blood-transfusion-service-center",
"car",
"christine",
"cnae-9",
"connect-4",
"covertype",
"credit-g",
"dionis",
"fabert",
"helena",
"higgs",
"jannis",
"jasmine",
"jungle_chess_2pcs_raw_endgame_complete",
"kc1",
"kr-vs-kp",
"mfeat-factors",
"nomao",
"numerai28.6",
"phoneme",
"segment",
"shuttle",
"sylvine",
"vehicle",
"volkert"
] | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | {
"domain_cls": "Integer",
"domain_kwargs": {
"lower": 1,
"upper": 201
},
"sampler_cls": "Uniform",
"sampler_kwargs": {}
} | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | metric_elapsed_time | metric_valid_error | hp_epoch | [
"metric_valid_error",
"metric_train_error",
"metric_runtime",
"metric_elapsed_time",
"metric_latency",
"metric_flops",
"metric_params"
] | [
"cifar10",
"cifar100",
"ImageNet16-120"
] | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
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null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | metric_elapsed_time | metric_valid_error_rate | global_step | [
"metric_valid_error_rate",
"metric_valid_ce_loss",
"metric_elapsed_time"
] | [
"imagenet_resnet_batch_size_512",
"uniref50_transformer_batch_size_128",
"translate_wmt_xformer_translate_batch_size_64",
"lm1b_transformer_batch_size_2048",
"imagenet_resnet_batch_size_256",
"mnist_max_pooling_cnn_tanh_batch_size_2048",
"mnist_max_pooling_cnn_tanh_batch_size_256",
"mnist_max_pooling_cnn_relu_batch_size_2048",
"mnist_max_pooling_cnn_relu_batch_size_256",
"mnist_simple_cnn_batch_size_2048",
"mnist_simple_cnn_batch_size_256",
"fashion_mnist_max_pooling_cnn_tanh_batch_size_2048",
"fashion_mnist_max_pooling_cnn_tanh_batch_size_256",
"fashion_mnist_max_pooling_cnn_relu_batch_size_2048",
"fashion_mnist_max_pooling_cnn_relu_batch_size_256",
"fashion_mnist_simple_cnn_batch_size_2048",
"fashion_mnist_simple_cnn_batch_size_256",
"svhn_no_extra_wide_resnet_batch_size_1024",
"svhn_no_extra_wide_resnet_batch_size_256",
"cifar100_wide_resnet_batch_size_2048",
"cifar100_wide_resnet_batch_size_256",
"cifar10_wide_resnet_batch_size_2048",
"cifar10_wide_resnet_batch_size_256"
] | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null | null |
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