import random
from ray import tune
from swanlab.integration.ray import SwanLabLoggerCallback

def train_func(config):
    offset = random.random() / 5
    for epoch in range(2, config["epochs"]):
        acc = 1 - (2 + config["lr"]) ** -epoch - random.random() / epoch - offset
        loss = (2 + config["lr"]) ** -epoch + random.random() / epoch + offset
        tune.report({"acc": acc, "loss": loss})


tuner = tune.Tuner(
    train_func,
    param_space={
        "lr": tune.grid_search([0.001, 0.01, 0.1, 1.0]),
        "epochs": 10,
    },
    run_config=tune.RunConfig(
        callbacks=[SwanLabLoggerCallback(project="Ray_Project")],
    ),
)
results = tuner.fit()