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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 |
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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 | 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 |
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Blackbox Repository
This dataset contains hyperparameter optimization (HPO) evaluations from several paper:
- fcnet: Tabular benchmarks for joint architecture and hyperparameter optimization. Klein, A. and Hutter, F. 2019.
- icml-deepar, icml-xgboost: A quantile-based approach for hyperparameter transfer learning. Salinas, D., Shen, H., and Perrone, V. 2021.
- lcbench: Auto-PyTorch: Multi-Fidelity MetaLearning for Efficient and Robust AutoDL. Lucas Zimmer, Marius Lindauer, Frank Hutter. 2020.
- nasbench201: NAS-Bench-201: Extending the scope of reproducible neural architecture search. Dong, X. and Yang, Y. 2020.
- pd1: Pre-trained Gaussian processes for Bayesian optimization. Wang, Z. and Dahl G. and Swersky K. and Lee C. and Mariet Z. and Nado Z. and Gilmer J. and Snoek J. and Ghahramani Z. 2021.
- yahpo: YAHPO Gym - An Efficient Multi-Objective Multi-Fidelity Benchmark for Hyperparameter Optimization. Pfisterer F., Schneider S., Moosbauer J., Binder M., Bischl B., 2022
The evaluations can be accessed through Syne Tune HPO library by calling the following:
from syne_tune.blackbox_repository import load_blackbox
blackbox = load_blackbox("nasbench201")["cifar10"]
blackbox_hyperparameter = next(iter(blackbox.hyperparameters.to_dict(orient="records")))
print(f"First hyperparameter: {blackbox_hyperparameter}")
print(
f"Objectives for first hyperparameters: {blackbox(configuration=blackbox_hyperparameter, fidelity=100)}"
)
# > First hyperparameter: {'hp_x0': 'avg_pool_3x3', 'hp_x1': 'nor_conv_1x1', 'hp_x2': 'skip_connect', 'hp_x3': 'nor_conv_1x1', 'hp_x4': 'skip_connect', 'hp_x5': 'skip_connect'}
# > Objective for first hyperparameters: {'metric_valid_error': 0.4177, 'metric_train_error': 0.2246, 'metric_runtime': 15.461778, 'metric_elapsed_time': 1546.179, 'metric_latency': 0.013935976, 'metric_flops': 15.64737, 'metric_params': 0.129306}
In addition, the blackboxes can be used to simulate HPO methods such as ASHA or Bayesian Optimization very fast while keeping identical results with non-simulated tuning.
The files can also be accessed directly from here.
If you are interested in having other blackboxes feel free to create an issue on Syne Tune project, we aim to grow the set over time.
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