Data input/output

Flyte being a data-aware orchestration platform, types play a vital role within it. This section provides an introduction to the wide range of data types that Flyte supports. These types serve a dual-purpose by not only validating the data but also enabling seamless transfer of data between local and cloud storage. They enable:

Data lineage
Memoization
Auto parallelization
Simplifying access to data
Auto generated CLI and launch UI

For a more comprehensive understanding of how Flyte manages data, refer to Understand How Flyte Handles Data.

Mapping Python to Flyte types

Flytekit automatically translates most Python types into Flyte types. Here’s a breakdown of these mappings:

Python Type	Flyte Type	Conversion	Comment
`int`	`Integer`	Automatic	Use Python 3 type hints.
`float`	`Float`	Automatic	Use Python 3 type hints.
`str`	`String`	Automatic	Use Python 3 type hints.
`bool`	`Boolean`	Automatic	Use Python 3 type hints.
`bytes`/`bytearray`	`Binary`	Not Supported	You have the option to employ your own custom typetransformer.
`complex`	NA	Not Supported	You have the option to employ your own custom type transformer.
`datetime.timedelta`	`Duration`	Automatic	Use Python 3 type hints.
`datetime.datetime`	`Datetime`	Automatic	Use Python 3 type hints.
`datetime.date`	`Datetime`	Automatic	Use Python 3 type hints.
`typing.List[T]` / `list[T]`	`Collection [T]`	Automatic	Use `typing.List[T]` or `list[T]`, where `T` canrepresent one of the other supported types listed in the table.
`typing.Iterator[T]`	`Collection [T]`	Automatic	Use `typing.Iterator[T]`, where `T` can represent one of the other supported types listed in the table.
File / file-like / `os.PathLike`	`FlyteFile`	Automatic	If you’re using `file` or `os.PathLike` objects,Flyte will default to the binary protocol for the file. When using `FlyteFile["protocol"]`, it is assumedthat the file is in the specified protocol, such as ‘jpg’, ‘png’, ‘hdf5’, etc.
Directory	`FlyteDirectory`	Automatic	When using `FlyteDirectory["protocol"]`, it is assumed that all thefiles belong to the specified protocol.
`typing.Dict[str, V]` / `dict[str, V]`	`Map[str, V]`	Automatic	Use `typing.Dict[str, V]` or `dict[str, V`, where `V` can be one of the other supported types in the table, including a nested dictionary.
`dict`	JSON (`struct.pb`)	Automatic	Use `dict`. It’s assumed that the untyped dictionary can beconverted to JSON. However, this may not always be possible and could result in a `RuntimeError`.
`@dataclass`	`Struct`	Automatic	The class should be a pure value class annotated with the `@dataclass`decorator.
`np.ndarray`	File	Automatic	Use `np.ndarray` as a type hint.
`pandas.DataFrame`	Structured Dataset	Automatic	Use `pandas.DataFrame` as a type hint. Pandas columntypes aren’t preserved.
`polars.DataFrame`	Structured Dataset	Automatic	Use `polars.DataFrame` as a type hint. Polars columntypes aren’t preserved.
`polars.LazyFrame`	Structured Dataset	Automatic	Use `polars.LazyFrame` as a type hint. Polars columntypes aren’t preserved.
`pyspark.DataFrame`	Structured Dataset	To utilize the type, install the `flytekitplugins-spark` plugin.	Use `pyspark.DataFrame` as a type hint.
`pydantic.BaseModel`	`Map`	To utilize the type, install the `pydantic` module.	Use `pydantic.BaseModel`as a type hint.
`torch.Tensor` / `torch.nn.Module`	File	To utilize the type, install the `torch` library.	Use `torchTensor` or `torch.nn.Module` as a type hint, and you can use their derived types.
`tf.keras.Model`	File	To utilize the type, install the `tensorflow` library.	Use `tf.keras.Model` andits derived types.
`sklearn.base.BaseEstimator`	File	To utilize the type, install the `scikit-learn` library.	Use `sklearnbase.BaseEstimator` and its derived types.
User defined types	Any	Custom transformers	The `FlytePickle` transformer is the default option, but youcan also define custom transformers. For instructions on building custom type transformers, please refer to this section.