Using Custom Python Objects#

Flyte supports passing JSON between tasks. But to simplify the usage for the users and introduce type-safety, Flytekit supports passing custom data objects between tasks.

Currently, data classes decorated with @dataclass_json are supported. One good use case of a data class would be when you want to wrap all input in a data class in the case of a map task which can only accept one input and produce one output.

This example shows how users can serialize custom JSON-compatible dataclasses between successive tasks using the excellent dataclasses_json library.

To get started, let’s import the necessary libraries.

import os
import tempfile
import typing
from dataclasses import dataclass

import pandas as pd
from dataclasses_json import dataclass_json
from flytekit import task, workflow
from import FlyteDirectory
from flytekit.types.file import FlyteFile
from flytekit.types.schema import FlyteSchema

We define a simple data class that can be sent between tasks.

class Datum(object):
    Example of a simple custom class that is modeled as a dataclass

    x: int
    y: str
    z: typing.Dict[int, str]

Datum is a user defined complex type that can be used to pass complex data between tasks. Interestingly, users can send this data between different tasks written in different languages and input it through the Flyte Console as raw JSON.


All variables in a data class should be annotated with their type. Failure to do should will result in an error.

Next, we define a data class that accepts FlyteSchema, FlyteFile, and FlyteDirectory.

class Result:
    schema: FlyteSchema
    file: FlyteFile
    directory: FlyteDirectory


A data class supports the usage of data associated with Python types, data classes, FlyteFile, FlyteDirectory, and FlyteSchema.

Once declared, dataclasses can be returned as outputs or accepted as inputs.

  1. Datum Data Class

def stringify(x: int) -> Datum:
    A dataclass return will be regarded as a complex single json return.
    return Datum(x=x, y=str(x), z={x: str(x)})

def add(x: Datum, y: Datum) -> Datum:
    Flytekit will automatically convert the passed in json into a DataClass. If the structures dont match, it will raise
    a runtime failure
    return Datum(x=x.x + y.x, y=x.y + y.y, z=x.z)

The stringify task outputs a data class, and the add task accepts data classes as inputs.

  1. Result Data Class

def upload_result() -> Result:
    Flytekit will upload FlyteFile, FlyteDirectory, and FlyteSchema to blob store (GCP, S3)
    df = pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]})
    temp_dir = tempfile.mkdtemp(prefix="flyte-")

    schema_path = temp_dir + "/schema.parquet"

    file_path = tempfile.NamedTemporaryFile(delete=False)
    file_path.write(b"Hello world!")
    fs = Result(
    return fs

def download_result(res: Result):
    Flytekit will lazily load FlyteSchema. We download the schema only when users invoke open().
    assert pd.DataFrame({"Name": ["Tom", "Joseph"], "Age": [20, 22]}).equals(
    f = open(res.file, "r")
    assert == "Hello world!"
    assert os.listdir( == ["schema.parquet"]

The upload_result task outputs a data class, and the download_result task accepts data classes as inputs.

Lastly, we create a workflow.

def wf(x: int, y: int) -> (Datum, Result):
    Dataclasses (JSON) can be returned from a workflow as well.
    res = upload_result()
    return add(x=stringify(x=x), y=stringify(x=y)), res

We can run the workflow locally.

if __name__ == "__main__":
    This workflow can be run locally. During local execution also, the dataclasses will be marshalled to and from json.
    wf(x=10, y=20)

Total running time of the script: ( 0 minutes 0.000 seconds)

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