Writing Custom Flyte Types

Flyte is a strongly typed framework for authoring tasks and workflows. But, there are situations when the existing set of types do not directly work. This is true with any programming language. This is when the languages support higher level concepts to describe User specific objects - like classes in python/java/c++, struct in C/golang, etc

Flytekit allows modeling user classes similarly. The idea is to make an interface that is more productive for the usecase, but write a transformer that transforms the user defined type to one of the generic constructs in Flyte’s Type system.

In this example, we will try to model an example user defined set and show how it can be integrated seamlessly with Flytekit’s typing engine.

The video below will walk you through the example.

import os
import tempfile
import typing

FlyteContext is used only to access a random local directory

from typing import Type

Defined type here represents a list of Files on the disk. We will refer to it as MyDataset

from flytekit import FlyteContext, task, workflow, BlobType, Blob, BlobMetadata, Literal, Scalar, LiteralType
from flytekit.extend import TypeEngine, TypeTransformer

class MyDataset(object):
    Dataset here is a set of files that exist together. In Flyte this maps to a Multi-part blob or a directory

    def __init__(self, base_dir: str = None):
        if base_dir is None:
            self._tmp_dir = tempfile.TemporaryDirectory()
            self._base_dir = self._tmp_dir.name
            self._files = []
            self._base_dir = base_dir
            files = os.listdir(base_dir)
            self._files = [os.path.join(base_dir, f) for f in files]

    def base_dir(self) -> str:
        return self._base_dir

    def files(self) -> typing.List[str]:
        return self._files

    def new_file(self, name: str) -> str:
        new_file = os.path.join(self._base_dir, name)
        return new_file

MyDataset represents a set of files locally, but, when a workflow consists of multiple steps, we want the data to flow between the different steps. To achieve this, it is necessary to explain how the data will be transformed to Flyte’s remote references. To do this, we create a new instance of flytekit.extend.TypeTransformer, for the type MyDataset as follows


The TypeTransformer is a Generic abstract base class. The Generic type argument here refers to the actual object that we want to work with. In this case, it is the MyDataset object

class MyDatasetTransformer(TypeTransformer[MyDataset]):
    _TYPE_INFO = BlobType(
        format="binary", dimensionality=BlobType.BlobDimensionality.MULTIPART

    def __init__(self):
        super(MyDatasetTransformer, self).__init__(
            name="mydataset-transform", t=MyDataset

    def get_literal_type(self, t: Type[MyDataset]) -> LiteralType:
        This is useful to tell the Flytekit type system that ``MyDataset`` actually refers to what corresponding type
        In this example, we say its of format binary (do not try to introspect) and there are more than one files in it
        return LiteralType(blob=self._TYPE_INFO)

    def to_literal(
        ctx: FlyteContext,
        python_val: MyDataset,
        python_type: Type[MyDataset],
        expected: LiteralType,
    ) -> Literal:
        This method is used to convert from given python type object ``MyDataset`` to the Literal representation
        # Step 1: lets upload all the data into a remote place recommended by Flyte
        remote_dir = ctx.file_access.get_random_remote_directory()
        ctx.file_access.upload_directory(python_val.base_dir, remote_dir)
        # Step 2: lets return a pointer to this remote_dir in the form of a literal
        return Literal(
                blob=Blob(uri=remote_dir, metadata=BlobMetadata(type=self._TYPE_INFO))

    def to_python_value(
        self, ctx: FlyteContext, lv: Literal, expected_python_type: Type[MyDataset]
    ) -> MyDataset:
        In this function we want to be able to re-hydrate the custom object from Flyte Literal value
        # Step 1: lets download remote data locally
        local_dir = ctx.file_access.get_random_local_directory()
        ctx.file_access.download_directory(lv.scalar.blob.uri, local_dir)
        # Step 2: create the MyDataset object
        return MyDataset(base_dir=local_dir)

Before we can use MyDataset in our tasks, we need to let flytekit know that MyDataset should be considered as a valid type. This is done using the flytekit.extend.TypeEngine.register() function.


Now the new type should be ready to use. Let us write an example generator and consumer for this new datatype

def generate() -> MyDataset:
    d = MyDataset()
    for i in range(3):
        fp = d.new_file(f"x{i}")
        with open(fp, "w") as f:
            f.write(f"Contents of file{i}")

    return d

def consume(d: MyDataset) -> str:
    s = ""
    for f in d.files:
        with open(f) as fp:
            s += fp.read()
            s += "\n"
    return s

def wf() -> str:
    return consume(d=generate())

We can run this workflow locally and test it. Remember even when you run it locally, flytekit will excercise the entire path

if __name__ == "__main__":


Contents of file0
Contents of file2
Contents of file1

Total running time of the script: ( 0 minutes 0.007 seconds)

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