8b3f79f3079834f00c7ec62d5decd6bc026c1bc8,python/ray/tune/examples/ax_example.py,,,#,47

Before Change

    ray.init()

    tune_kwargs = {
        "num_samples": 10 if args.smoke_test else 50,
        "config": {
            "iterations": 100,
            "x1": tune.uniform(0.0, 1.0),
            "x2": tune.uniform(0.0, 1.0),
            "x3": tune.uniform(0.0, 1.0),
            "x4": tune.uniform(0.0, 1.0),
            "x5": tune.uniform(0.0, 1.0),
            "x6": tune.uniform(0.0, 1.0),
        },
        "stop": {
            "timesteps_total": 100
        }
    }
    algo = AxSearch(
        max_concurrent=4,
        metric="hartmann6",
        mode="min",
        parameter_constraints=["x1 + x2 <= 2.0"],  // Optional.
        outcome_constraints=["l2norm <= 1.25"],  // Optional.
    )
    scheduler = AsyncHyperBandScheduler(metric="hartmann6", mode="min")
    tune.run(
        easy_objective,
        name="ax",
        search_alg=algo,
        scheduler=scheduler,
        **tune_kwargs)

After Change

        parameter_constraints=["x1 + x2 <= 2.0"],  // Optional.
        outcome_constraints=["l2norm <= 1.25"],  // Optional.
    )
    scheduler = AsyncHyperBandScheduler()
    analysis = tune.run(
        easy_objective,
        name="ax",
        metric="hartmann6",  // provided in the "easy_objective" function
        mode="min",
        search_alg=algo,
        scheduler=scheduler,
        num_samples=10 if args.smoke_test else 50,
        config={
            "iterations": 100,
            "x1": tune.uniform(0.0, 1.0),
            "x2": tune.uniform(0.0, 1.0),
            "x3": tune.uniform(0.0, 1.0),
            "x4": tune.uniform(0.0, 1.0),
            "x5": tune.uniform(0.0, 1.0),
            "x6": tune.uniform(0.0, 1.0),
        },
        stop={"timesteps_total": 100})

    print("Best hyperparameters found were: ", analysis.best_config)

Instances