tfds.builder

Fetches a tfds.core.DatasetBuilder by string name.

tfds.builder(
    name: str, *, try_gcs: bool = False, **builder_kwargs
) -> dataset_builder.DatasetBuilder

Used in the notebooks

Used in the tutorials
TFDS for Jax and PyTorch TensorFlow Datasets TFDS and determinism TensorFlow Constrained Optimization Example Using CelebA Dataset Exploring the TF-Hub CORD-19 Swivel Embeddings

Args
name	str, the registered name of the DatasetBuilder (the class name as camel or snake case: MyDataset or my_dataset). This can be either 'dataset_name' or 'dataset_name/config_name' for datasets with BuilderConfigs. As a convenience, this string may contain comma-separated keyword arguments for the builder. For example 'foo_bar/a=True,b=3' would use the FooBar dataset passing the keyword arguments a=True and b=3 (for builders with configs, it would be 'foo_bar/zoo/a=True,b=3' to use the 'zoo' config and pass to the builder keyword arguments a=True and b=3).
try_gcs	bool, if True, tfds.load will see if the dataset exists on the public GCS bucket before building it locally. This is equivalent to passing data_dir='gs://tfds-data/datasets'. Warning: try_gcs is different than builder_kwargs.download_config.try_download_gcs. try_gcs (default: False) overrides data_dir to be the public GCS bucket. try_download_gcs (default: True) allows downloading from GCS while keeping a different data_dir than the public GCS bucket. So, to fully bypass GCS, please use try_gcs=False and download_and_prepare_kwargs={'download_config': tfds.core.download.DownloadConfig(try_download_gcs=False)}).
**builder_kwargs	dict of keyword arguments passed to the tfds.core.DatasetBuilder.

Returns
A tfds.core.DatasetBuilder.

Raises
DatasetNotFoundError	if name is unrecognized.