🍡Selecting Output Variables

When working with configuration functions, not all variables defined within them are needed for the final execution engine.

Consider this configuration function:

from hypster import config, HP

@config
def llm_config(hp: HP):
    model_name = hp.select({"sonnet" : "claude-3-5-sonnet-20241022"
                            "haiku" : "claude-3-5-haiku-20241022"}, 
                            default="haiku")
    
    if model_type == "haiku":
        max_tokens = hp.int(256, max=2048)
    else:
        max_tokens = hp.int(126, max=1024)
        
    cache = Cache(folder=hp.text("./cache"))
    config_dct = {"temperature" : hp.number(0, min=0, max=1),
                  "max_tokens" : max_tokens}
              
    model = Model(model_name, cache)

Along with this execution function:

def run(input: str, model: Model, config_dict: Dict[str, Any]) -> str:
    return model.run(input, **config_dict)

This function only requires model and config_dict, but our configuration function creates additional variables like cache, model_type, and param. Passing unnecessary variables could:

• Cause function signature mismatches

• Lead to memory inefficiency

• Create potential naming conflicts

Variable Selection Methods

To ensure we pass only the required variables, we have two filtering approaches:

1. Include specific variables using final_vars:

config = my_config(final_vars=["model", "config_dict"], values={...})
run("Hello", **config)

Use final_vars when you need only a few specific variables

When final_vars is empty, all variables are returned (except those in exclude_vars)

1. Exclude unwanted variables using exclude_vars:

config = my_config(exclude_vars=["cache", "temp_data"], values={...})
run("Hello", **config)

Choose exclude_vars when you have many variables to keep and little to filter out.