@Scout How can I build a python class-based asset config from a .yaml file?

To build a Python class-based asset config from a

.yaml

file, you would typically follow these steps: 1. Define a Python class that extends from

Config

and includes type-annotated fields corresponding to the configuration schema you want to use for your asset. This class will represent the configuration schema in Pythonic form. 2. Load your

.yaml

file into a Python dictionary. You can use libraries like

PyYAML

or

ruamel.yaml

to parse the YAML content. 3. Instantiate your Python class with the loaded configuration data. Since the class is based on

Config

, it should be able to validate and handle the configuration data according to the defined schema. Here is an example based on the documentation:

python from dagster import Config, asset import yaml  # Step 1: Define a Python class for your asset config class MyAssetConfig(Config):     person_name: str  # Step 2: Load the YAML file into a Python dictionary with open('config.yaml', 'r') as file:     yaml_config = yaml.safe_load(file)  # Step 3: Instantiate the Python class with the YAML config data config = MyAssetConfig(**yaml_config)  # Define your asset using the class-based config @asset def greeting(config: MyAssetConfig) -> str:     return f"hello {config.person_name}"

In this example,

config.yaml

would be a YAML file containing the necessary configuration for the

MyAssetConfig

class, such as:

yaml person_name: "Alice"

Please note that the provided code example is a general illustration based on the documentation. You may need to adjust the code to fit your specific use case and ensure that the YAML file structure matches the schema defined in your Python class.

@Scout how can I instantiate a config if I have different configs (with the same schema) in different .yml files?

If you have different configurations with the same schema in different

.yml

files and you want to instantiate a config for each one, you can follow these steps: 1. Define a Python class that extends from

Config

with the desired schema. 2. Load each

.yaml

file into a separate Python dictionary. 3. Instantiate your Python class with each loaded configuration dictionary. Here's an example of how you might do this:

python from dagster import Config, asset import yaml  # Define a Python class for your asset config class MyAssetConfig(Config):     person_name: str  # Function to load a YAML file and instantiate the config class def load_config_from_yaml(file_path):     with open(file_path, 'r') as file:         yaml_config = yaml.safe_load(file)     return MyAssetConfig(**yaml_config)  # Load different configurations config1 = load_config_from_yaml('config1.yaml') config2 = load_config_from_yaml('config2.yaml')  # Define your asset using the class-based config @asset def greeting(config: MyAssetConfig) -> str:     return f"hello {config.person_name}"  # Now you can use config1 and config2 with your asset greeting1 = greeting(config=config1) greeting2 = greeting(config=config2)

In this example,

config1.yaml

and

config2.yaml

are two separate YAML files that contain configuration data following the schema defined in

MyAssetConfig

. The

load_config_from_yaml

function reads a YAML file, parses it into a dictionary, and then instantiates

MyAssetConfig

with that dictionary. This approach allows you to manage multiple configurations and use them as needed with your assets or other parts of your Dagster application.

@Scout Can I return a config inside an asset?

In Dagster, assets are typically used to represent a piece of data or a computation that produces data. The configuration for an asset is meant to parameterize how that asset is computed or materialized, rather than being a value that the asset itself returns. If you want to use configuration values within an asset's computation, you would access the configuration through the context object provided to the asset's compute function. However, the configuration itself is not something that you would return from the asset; instead, you would use the configuration to influence the output of the asset. Here's an example of how you might use configuration within an asset:

python from dagster import asset, Field  # Define your asset with a config schema @asset(config_schema={"greeting": Field(str, is_required=False, default_value="Hello")}) def my_asset(context):     # Access the configuration value     greeting = context.op_config["greeting"]     # Use the configuration value in the asset's computation     result = f"{greeting}, world!"     return result

In this example, the asset

my_asset

has a configuration schema that expects a "greeting" field. The asset uses the value of this configuration field to construct a string that it then returns. If you want to return configuration data as part of an asset's output, you would need to incorporate that configuration data into the asset's computation result. However, this is not a common pattern in Dagster, as configuration is generally used to control the behavior of computations rather than being part of the data that those computations produce. If you have a specific use case in mind where you think returning configuration from an asset would be necessary, it might be helpful to reconsider the design of your assets or how you're using configuration in your Dagster application. Configuration is typically static for a given run and is not meant to be manipulated or returned by the computations themselves.