b71c912da70e307ff1a9f07c7ca277a727306126,python/ray/tune/examples/mlflow_example.py,,easy_objective,#Any#,18

Before Change

def easy_objective(config):
    for i in range(20):
        result = dict(
            timesteps_total=i,
            mean_loss=(config["height"] - 14)**2 - abs(config["width"] - 3))
        tune.report(**result)
        time.sleep(0.02)


if __name__ == "__main__":
    client = MlflowClient()
    experiment_id = client.create_experiment("test")

After Change

    // Hyperparameters
    width, height = config["width"], config["height"]

    for step in range(config.get("steps", 100)):
        // Iterative training function - can be any arbitrary training procedure
        intermediate_score = evaluation_fn(step, width, height)
        // Feed the score back back to Tune.
        tune.report(iterations=step, mean_loss=intermediate_score)
        time.sleep(0.1)


if __name__ == "__main__":
    client = MlflowClient()
    experiment_id = client.create_experiment("test")

Instances