Specifically i've got a dataclass containing runtime configuration parameters for a particular asset. I'd like to wire these into the asset using the same schema of that dataclass. It would be nice to just reuse the dataclass for the config type hint, but this error is preventing that

The alternative right now is:

@dataclass
class Config():
   some: int
   params: str

class DagsterConfig(Config):
   some: int
   params: str

# some logic that populates this object
config: Config = load_config()

@asset
def some_asset(config: DagsterConfig):
    return something

materialize([asset], run_config={"ops": {"asset": config.asdict()}})

basically i would like to avoid having two separate definitions for Config and DagsterConfig

If there are any workarounds that might support something like this, please let me know!

(unfortunately im constrained to dagster 1.1.21 for now)

I wonder if theres a way for me to enable mutability on a dataclass such that it can be treated as a pydantic basemodel

I may be able to do something like the example here, where i nest the dataclass within the dagster config type https://docs.pydantic.dev/usage/dataclasses/#use-of-stdlib-dataclasses-with-basemodel

Let me give it a shot, will report back.

Extrapolating, this works (but probably ways to tidy it up)

from dataclasses import dataclass

from dagster import Config, RunConfig, asset, materialize
from pydantic import create_model
from pydantic.dataclasses import dataclass as pydantic_dataclass


@dataclass
class MyConfig:
    foo: str
    bar: int


MyDagsterConfig = create_model(
    "MyDagsterConfig", __base__=(pydantic_dataclass(MyConfig).__pydantic_model__, Config)
)


@asset
def some_asset(config: MyDagsterConfig):
    return config.foo


config = MyConfig(foo="foo", bar=2)

result = materialize(
    [some_asset], run_config=RunConfig(ops={"some_asset": config.__dict__})
)

print(result.asset_value("some_asset"))