Running Tune experiments with BOHB¶

In this tutorial we introduce BOHB, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with BOHB and, as a result, allow you to seamlessly scale up a BOHB optimization process - without sacrificing performance.

Bayesian Optimization HyperBand (BOHB) combines the benefits of Bayesian optimization together with Bandit-based methods (e.g. HyperBand). BOHB does not rely on the gradient of the objective function, but instead, learns from samples of the search space. It is suitable for optimizing functions that are non-differentiable, with many local minima, or even unknown but only testable. Therefore, this approach belongs to the domain of “derivative-free optimization” and “black-box optimization”.

In this example we minimize a simple objective to briefly demonstrate the usage of BOHB with Ray Tune via BOHBSearch. It’s useful to keep in mind that despite the emphasis on machine learning experiments, Ray Tune optimizes any implicit or explicit objective. Here we assume ConfigSpace==0.4.18 and hpbandster==0.7.4 libraries are installed. To learn more, please refer to the BOHB website.

Click below to see all the imports we need for this example. You can also launch directly into a Binder instance to run this notebook yourself. Just click on the rocket symbol at the top of the navigation.

import time

import ray
from ray import tune
from ray.air import session
from ray.tune.search import ConcurrencyLimiter
from ray.tune.schedulers.hb_bohb import HyperBandForBOHB
from ray.tune.search.bohb import TuneBOHB
import ConfigSpace as CS

Let’s start by defining a simple evaluation function. We artificially sleep for a bit (0.1 seconds) to simulate a long-running ML experiment. This setup assumes that we’re running multiple steps of an experiment and try to tune two hyperparameters, namely width and height, and activation.

def evaluate(step, width, height, activation):
    time.sleep(0.1)
    activation_boost = 10 if activation=="relu" else 1
    return (0.1 + width * step / 100) ** (-1) + height * 0.1 + activation_boost

Next, our objective function takes a Tune config, evaluates the score of your experiment in a training loop, and uses session.report to report the score back to Tune.

def objective(config):
    for step in range(config["steps"]):
        score = evaluate(step, config["width"], config["height"], config["activation"])
        session.report({"iterations": step, "mean_loss": score})

Next we define a search space. The critical assumption is that the optimal hyperparameters live within this space. Yet, if the space is very large, then those hyperparameters may be difficult to find in a short amount of time.

search_space = {
    "steps": 100,
    "width": tune.uniform(0, 20),
    "height": tune.uniform(-100, 100),
    "activation": tune.choice(["relu", "tanh"]),
}

Next we define the search algorithm built from TuneBOHB, constrained to a maximum of 4 concurrent trials with a ConcurrencyLimiter. Below algo will take care of the BO (Bayesian optimization) part of BOHB, while scheduler will take care the HB (HyperBand) part.

algo = TuneBOHB()
algo = tune.search.ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = HyperBandForBOHB(
    time_attr="training_iteration",
    max_t=100,
    reduction_factor=4,
    stop_last_trials=False,
)

The number of samples is the number of hyperparameter combinations that will be tried out. This Tune run is set to 1000 samples. (you can decrease this if it takes too long on your machine).

num_samples = 1000

Finally, we run the experiment to minimize the “mean_loss” of the objective by searching within "steps": 100 via algo, num_samples times. This previous sentence is fully characterizes the search problem we aim to solve. With this in mind, notice how efficient it is to execute tune.run().

analysis = tune.run(
    objective,
    search_alg=algo,
    scheduler=scheduler,
    metric="mean_loss",
    mode="min",
    name="bohb_exp",
    num_samples=num_samples,
    config=search_space
)

Here are the hyperparameters found to minimize the mean loss of the defined objective.

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

Optional: Passing the search space via the TuneBOHB algorithm¶

We can define the hyperparameter search space using ConfigSpace, which is the format accepted by BOHB.

config_space = CS.ConfigurationSpace()
config_space.add_hyperparameter(
    CS.UniformFloatHyperparameter("width", lower=0, upper=20)
)
config_space.add_hyperparameter(
    CS.UniformFloatHyperparameter("height", lower=-100, upper=100)
)
config_space.add_hyperparameter(
    CS.CategoricalHyperparameter(
        "activation", choices=["relu", "tanh"]
    )
)
algo = TuneBOHB(
    space=config_space,
    metric="episode_reward_mean",
    mode="max",
)
algo = tune.search.ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = HyperBandForBOHB(
    time_attr="training_iteration",
    max_t=100,
    reduction_factor=4,
    stop_last_trials=False,
)
analysis = tune.run(
    objective,
    config=config_space,
    scheduler=bohb_hyperband,
    search_alg=algo,
    num_samples=num_samples,
    name="bohb_exp_2",
    stop={"training_iteration": 100},
)

Here again are the hyperparameters found to minimize the mean loss of the defined objective.

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