@ancient-wolf-19325 provide how to use pydantic instead of dataclass

To use Pydantic instead of dataclasses in Flyte, you can define your data models using Pydantic's

BaseModel

. Pydantic provides data validation and settings management using Python type annotations. Here's a basic example of how to use Pydantic models in Flyte tasks: 1. Define a Pydantic Model: Create a Pydantic model by subclassing

BaseModel

and defining your fields with type annotations. 2. Use the Model in Tasks: You can use the Pydantic model as input or output types in Flyte tasks. Flyte will handle the serialization and deserialization of these models. Here's a simple example:

from pydantic import BaseModel
from flytekit import task, workflow

# Define a Pydantic model
class User(BaseModel):
    id: int
    name: str

# Use the Pydantic model in a task
@task
def create_user(user_id: int, user_name: str) -> User:
    return User(id=user_id, name=user_name)

# Use the Pydantic model in a workflow
@workflow
def user_workflow(user_id: int, user_name: str) -> User:
    return create_user(user_id=user_id, user_name=user_name)

# Run the workflow
if __name__ == "__main__":
    result = user_workflow(user_id=1, user_name="John Doe")
    print(result)

This example demonstrates how to define a Pydantic model and use it within Flyte tasks and workflows. Flyte automatically handles the conversion of the Pydantic model to and from JSON, making it easy to work with structured data. Would you like more detail on this topic? Sources: - Pydantic BaseModel 👍 0 button 👎 0 button 💡 Hint: Mention @runllm in the thread for followups.