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The dataset viewer is not available for this split.

Cannot load the dataset split (in streaming mode) to extract the first rows.

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
epoch: struct<domain_cls: string, domain_kwargs: struct<lower: int64, upper: int64>, sampler_cls: string, s (... 24 chars omitted)
  child 0, domain_cls: string
  child 1, domain_kwargs: struct<lower: int64, upper: int64>
      child 0, lower: int64
      child 1, upper: int64
  child 2, sampler_cls: string
  child 3, sampler_kwargs: struct<>
k_filter: null
n_layers: null
enc_factor: null
batch_size: null
drop_p: null
latent_dim: null
weight_decay: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
  child 0, domain_cls: string
  child 1, domain_kwargs: struct<lower: double, upper: double>
      child 0, lower: double
      child 1, upper: double
  child 2, sampler_cls: string
  child 3, sampler_kwargs: struct<base: double>
      child 0, base: double
beta_tc: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: int64>, sampler_cls: string,  (... 37 chars omitted)
  child 0, domain_cls: string
  child 1, domain_kwargs: struct<lower: double, upper: int64>
      child 0, lower: double
      child 1, upper: int64
  child 2, sampler_cls: string
  child 3, sampler_kwargs: struct<base: double>
      child 0, base: double
beta_dimKL: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
  child 0, domain_cls: string
  child 1, domain_kwargs: struct<lower: double, upper: double>
      child 0, lower: double
      child 1, upper: double
  child 2, sampler_cls: string
  child 3, sampler_kwargs: struct<base: double>
      child 0, base: double
learning_rate: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
  child 0, domain_cls: string
  child 1, domain_kwargs: struct<lower: double, upper: double>
      child 0, lower: double
      child 1, upper: double
  child 2, sampler_cls: string
  child 3, sampler_kwargs: struct<base: double>
      child 0, base: double
beta_mi: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
  child 0, domain_cls: string
  child 1, domain_kwargs: struct<lower: double, upper: double>
      child 0, lower: double
      child 1, upper: double
  child 2, sampler_cls: string
  child 3, sampler_kwargs: struct<base: double>
      child 0, base: double
to
{'k_filter': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'n_layers': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'enc_factor': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'batch_size': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'learning_rate': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'drop_p': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('int64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'weight_decay': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'latent_dim': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'beta_mi': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_tc': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('int64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_dimKL': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
                  return get_rows(
                      dataset=dataset,
                  ...<4 lines>...
                      column_names=column_names,
                  )
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
                  rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
                File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
                  yield from ds.decode(False) if ds.features else ds
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              epoch: struct<domain_cls: string, domain_kwargs: struct<lower: int64, upper: int64>, sampler_cls: string, s (... 24 chars omitted)
                child 0, domain_cls: string
                child 1, domain_kwargs: struct<lower: int64, upper: int64>
                    child 0, lower: int64
                    child 1, upper: int64
                child 2, sampler_cls: string
                child 3, sampler_kwargs: struct<>
              k_filter: null
              n_layers: null
              enc_factor: null
              batch_size: null
              drop_p: null
              latent_dim: null
              weight_decay: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
                child 0, domain_cls: string
                child 1, domain_kwargs: struct<lower: double, upper: double>
                    child 0, lower: double
                    child 1, upper: double
                child 2, sampler_cls: string
                child 3, sampler_kwargs: struct<base: double>
                    child 0, base: double
              beta_tc: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: int64>, sampler_cls: string,  (... 37 chars omitted)
                child 0, domain_cls: string
                child 1, domain_kwargs: struct<lower: double, upper: int64>
                    child 0, lower: double
                    child 1, upper: int64
                child 2, sampler_cls: string
                child 3, sampler_kwargs: struct<base: double>
                    child 0, base: double
              beta_dimKL: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
                child 0, domain_cls: string
                child 1, domain_kwargs: struct<lower: double, upper: double>
                    child 0, lower: double
                    child 1, upper: double
                child 2, sampler_cls: string
                child 3, sampler_kwargs: struct<base: double>
                    child 0, base: double
              learning_rate: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
                child 0, domain_cls: string
                child 1, domain_kwargs: struct<lower: double, upper: double>
                    child 0, lower: double
                    child 1, upper: double
                child 2, sampler_cls: string
                child 3, sampler_kwargs: struct<base: double>
                    child 0, base: double
              beta_mi: struct<domain_cls: string, domain_kwargs: struct<lower: double, upper: double>, sampler_cls: string, (... 38 chars omitted)
                child 0, domain_cls: string
                child 1, domain_kwargs: struct<lower: double, upper: double>
                    child 0, lower: double
                    child 1, upper: double
                child 2, sampler_cls: string
                child 3, sampler_kwargs: struct<base: double>
                    child 0, base: double
              to
              {'k_filter': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'n_layers': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'enc_factor': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'batch_size': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'learning_rate': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'drop_p': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('int64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'weight_decay': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'latent_dim': {'domain_cls': Value('string'), 'domain_kwargs': {'categories': List(Value('int64'))}, 'sampler_cls': Value('string'), 'sampler_kwargs': Json(decode=True)}, 'beta_mi': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_tc': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('int64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}, 'beta_dimKL': {'domain_cls': Value('string'), 'domain_kwargs': {'lower': Value('float64'), 'upper': Value('float64')}, 'sampler_cls': Value('string'), 'sampler_kwargs': {'base': Value('float64')}}}
              because column names don't match

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Check out the documentation for more information.

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
tabrepo: TabRepo: A Large Scale Repository of Tabular Model Evaluations and its AutoML Applications. Salinas D., Erickson N., 2024.
hpob: HPO-B: A Large-Scale Reproducible Benchmark for Black-Box HPO based on OpenML. Arango S., Jomaa H., Wistuba M., Grabocka J., 2021.
bbomix: BBOmix: A Tabular Benchmark for Hyperparameter Optimization of Unsupervised Biological Representation Learning. Luca Thale-Bombien, Jan Ewald, Ralf König and Aaron Klein., 2026

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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