TFDS now supports the Croissant 🥐 format! Read the documentation to know more.

tfds.builder

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

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 `BuilderConfig`s. 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.

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