https://github.com/ray-project/ray/pull/51234/files#r1988406888

tuner_port_for_PR.py

from time import sleep
import ray
from ray import tune
from ray.tune.tuner import Tuner
import time

def expensive_setup():
    print("EXPENSIVE SETUP")
    sleep(1)


class QuadraticTrainable(tune.Trainable):
    def setup(self, config):
        # Store the configuration containing hparam1 and hparam2
        self.config = config
        expensive_setup()
        self.max_steps = 5
        self.step_count = 0

    def step(self):
        # Extract hyperparameters from the config
        h1 = self.config["hparam1"]
        h2 = self.config["hparam2"]
        
        # Compute a simple quadratic objective where the optimum is at hparam1=3 and hparam2=5
        loss = (h1 - 3) ** 2 + (h2 - 5) ** 2

        metrics = {"loss": loss}

        self.step_count += 1
        if self.step_count > self.max_steps:
            metrics["done"] = True
        
        # Return the computed loss as the metric
        return metrics

    def reset_config(self, new_config):
        # Update the configuration for a new trial while reusing the actor
        self.config = new_config
        return True


if __name__ == "__main__":
    ray.init()
    
    search_space = {
        "hparam1": tune.uniform(-10, 10),
        "hparam2": tune.uniform(-10, 10)
    }
    
    # Common tuner parameters
    tuner_kwargs = {
        "trainable": QuadraticTrainable,
        "param_space": search_space,
        "run_config": ray.air.RunConfig(
            verbose=0,
        )
    }

    print("Running experiment with actor reuse")
    start_time = time.time()
    tuner_with_reuse = Tuner(
        **tuner_kwargs,
        tune_config=tune.TuneConfig(
            num_samples=1,
            max_concurrent_trials=1,
            reuse_actors=True,
        )
    )
    tuner_with_reuse.fit()
    elapsed_reuse = time.time() - start_time

    print("Running experiment without actor reuse")
    start_time = time.time()
    tuner_without_reuse = Tuner(
        **tuner_kwargs,
        tune_config=tune.TuneConfig(
            num_samples=1,
            max_concurrent_trials=1,
            reuse_actors=False,
        )
    )
    tuner_without_reuse.fit()
    elapsed_no_reuse = time.time() - start_time

    print("#" * 15)
    print("Timing results")
    print("-" * 15)
    print(f"Timing with reuse_actors=True: {elapsed_reuse:.2f} seconds")
    print(f"Timing without reuse_actors: {elapsed_no_reuse:.2f} seconds")
    print("#" * 15)