@ancient-wolf-19325 does flyte use materializer?

It seems likely that Flyte uses a concept similar to materialization through its use of Flyte Decks and TypeTransformers. Flyte Decks allow for the visualization of task inputs and outputs by generating static HTML reports, which can be considered a form of materialization for visualization purposes. Additionally, Flyte's TypeTransformers facilitate the handling of complex data types by converting them into a format that can be easily stored and retrieved, which aligns with the concept of materialization in data processing. Would you like more detail on how Flyte handles data visualization or TypeTransformers? Sources: - Visualizing task input and output 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.

Write a code using the typetransformers?

To use TypeTransformers in Flyte, you can define a custom type transformer to handle specific data types. Here's an example of how to create a custom type transformer for a hypothetical

MyDataset

type:

from flytekit import task, workflow, TypeEngine, FlyteContext
from flytekit.models.literals import Literal, Scalar, Blob, BlobMetadata
from flytekit.models.types import LiteralType, SimpleType
from flytekit.extend import TypeTransformer

class MyDataset:
    def __init__(self, data):
        self.data = data

class MyDatasetTransformer(TypeTransformer[MyDataset]):
    def __init__(self):
        super().__init__(name="MyDataset", t=MyDataset)

    def get_literal_type(self, t: type) -> LiteralType:
        return LiteralType(simple=SimpleType.STRUCT)

    def to_literal(self, ctx: FlyteContext, python_val: MyDataset, python_type: type, expected: LiteralType) -> Literal:
        # Serialize the data to a file and return a Blob literal
        local_file = ctx.file_access.get_random_local_path() + ".txt"
        with open(local_file, "w") as f:
            f.write(python_val.data)
        remote_file = ctx.file_access.get_random_remote_path()
        ctx.file_access.upload(local_file, remote_file)
        return Literal(scalar=Scalar(blob=Blob(uri=remote_file, metadata=BlobMetadata(type=self.get_literal_type(python_type)))))

    def to_python_value(self, ctx: FlyteContext, lv: Literal, expected_python_type: type) -> MyDataset:
        # Deserialize the data from a file
        local_file = ctx.file_access.get_random_local_path() + ".txt"
        ctx.file_access.download(lv.scalar.blob.uri, local_file)
        with open(local_file, "r") as f:
            data = f.read()
        return MyDataset(data)

# Register the custom transformer
TypeEngine.register(MyDatasetTransformer())

@task
def process_data(data: MyDataset) -> str:
    return f"Processed data: {data.data}"

@workflow
def my_workflow(data: str) -> str:
    dataset = MyDataset(data)
    return process_data(data=dataset)

This code defines a custom type transformer for

MyDataset

and registers it with Flytekit. The

process_data

task and

my_workflow

workflow demonstrate how to use this transformer. Would you like more details on any specific part? Sources: - Custom types 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.