8b3f79f3079834f00c7ec62d5decd6bc026c1bc8,python/ray/tune/examples/zoopt_example.py,,,#,30

Before Change

    args, _ = parser.parse_known_args()
    ray.init()

    tune_kwargs = {
        "num_samples": 10 if args.smoke_test else 1000,
        "config": {
            "steps": 10,
            "height": tune.quniform(-10, 10, 1e-2),
            "width": tune.randint(0, 10)
        }
    }

    // Optional: Pass the parameter space yourself
    // space = {
    //     // for continuous dimensions: (continuous, search_range, precision)
    //     "height": (ValueType.CONTINUOUS, [-10, 10], 1e-2),
    //     // for discrete dimensions: (discrete, search_range, has_order)
    //     "width": (ValueType.DISCRETE, [0, 10], True)
    //     // for grid dimensions: (grid, grid_list)
    //     "layers": (ValueType.GRID, [4, 8, 16])
    // }

    zoopt_search_config = {
        "parallel_num": 8,
    }

    zoopt_search = ZOOptSearch(
        algo="Asracos",  // only support ASRacos currently
        budget=tune_kwargs["num_samples"],
        // dim_dict=space,  // If you want to set the space yourself
        metric="mean_loss",
        mode="min",
        **zoopt_search_config)

    scheduler = AsyncHyperBandScheduler(metric="mean_loss", mode="min")

    tune.run(
        easy_objective,
        search_alg=zoopt_search,
        name="zoopt_search",
        scheduler=scheduler,
        **tune_kwargs)

After Change

        // dim_dict=space,  // If you want to set the space yourself
        **zoopt_search_config)

    scheduler = AsyncHyperBandScheduler()

    analysis = tune.run(
        easy_objective,
        metric="mean_loss",
        mode="min",
        search_alg=zoopt_search,
        name="zoopt_search",
        scheduler=scheduler,
        num_samples=num_samples,
        config={
            "steps": 10,
            "height": tune.quniform(-10, 10, 1e-2),
            "width": tune.randint(0, 10)
        })
    print("Best config found: ", analysis.best_config)

Instances