In this tutorial we introduce HyperOpt, while running a simple Ray Tune experiment. Tune’s Search Algorithms integrate with HyperOpt and, as a result, allow you to seamlessly scale up a Hyperopt optimization process - without sacrificing performance.
HyperOpt provides gradient/derivative-free optimization able to handle noise over the objective landscape, including evolutionary, bandit, and Bayesian optimization algorithms. Nevergrad internally supports search spaces which are continuous, discrete or a mixture of thereof. It also provides a library of functions on which to test the optimization algorithms and compare with other benchmarks.
In this example we minimize a simple objective to briefly demonstrate the usage of HyperOpt with Ray Tune via HyperOptSearch. 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 hyperopt==0.2.5 library is installed. To learn more, please refer to HyperOpt website.
We include a important example on conditional search spaces (stringing together relationships among hyperparameters).
Background information:
Necessary requirements:
pip install ray[tune]pip install hyperopt==0.2.5# !pip install ray[tune]
!pip install hyperopt==0.2.5
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.tune.suggest import ConcurrencyLimiter
from ray.tune.suggest.hyperopt import HyperOptSearch
from hyperopt import hp
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.
def evaluate(step, width, height):
time.sleep(0.1)
return (0.1 + width * step / 100) ** (-1) + height * 0.1
Next, our objective function takes a Tune config, evaluates the score of your experiment in a training loop,
and uses tune.report to report the score back to Tune.
def objective(config):
for step in range(config["steps"]):
score = evaluate(step, config["width"], config["height"])
tune.report(iterations=step, mean_loss=score)
ray.init(configure_logging=False)
While defining the search algorithm, we may choose to provide an initial set of hyperparameters that we believe are especially promising or informative, and
pass this information as a helpful starting point for the HyperOptSearch object.
We also set the maximum concurrent trials to 4 with a ConcurrencyLimiter.
initial_params = [
{"width": 1, "height": 2},
{"width": 4, "height": 2},
]
algo = HyperOptSearch(points_to_evaluate=initial_params)
algo = ConcurrencyLimiter(algo, max_concurrent=4)
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
# If 1000 samples take too long, you can reduce this number.
# We override this number here for our smoke tests.
num_samples = 10
Next we define a search space. The critical assumption is that the optimal hyperparamters 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_config = {
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
"activation": tune.choice(["relu, tanh"])
}
Finally, we run the experiment to "min"imize the "mean_loss" of the objective by searching search_config 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,
metric="mean_loss",
mode="min",
name="hyperopt_exp",
num_samples=num_samples,
config=search_space,
)
Here are the hyperparamters found to minimize the mean loss of the defined objective.
print("Best hyperparameters found were: ", analysis.best_config)
Sometimes we may want to build a more complicated search space that has conditional dependencies on other hyperparameters. In this case, we pass a nested dictionary to objective_two, which has been slightly adjusted from objective to deal with the conditional search space.
def evaluation_fn(step, width, height, mult=1):
return (0.1 + width * step / 100) ** (-1) + height * 0.1 * mult
def objective_two(config):
width, height = config["width"], config["height"]
sub_dict = config["activation"]
mult = sub_dict.get("mult", 1)
for step in range(config["steps"]):
intermediate_score = evaluation_fn(step, width, height, mult)
tune.report(iterations=step, mean_loss=intermediate_score)
time.sleep(0.1)
conditional_space = {
"activation": hp.choice(
"activation",
[
{"activation": "relu", "mult": hp.uniform("mult", 1, 2)},
{"activation": "tanh"},
],
),
"width": hp.uniform("width", 0, 20),
"height": hp.uniform("height", -100, 100),
"steps": 100,
}
Now we the define the search algorithm built from HyperOptSearch constrained by ConcurrencyLimiter. When the hyperparameter search space is conditional, we pass it (conditional_space) into HyperOptSearch.
algo = HyperOptSearch(space=conditional_space, metric="mean_loss", mode="min")
algo = ConcurrencyLimiter(algo, max_concurrent=4)
Now we run the experiment, this time with an empty config because we instead provided space to the HyperOptSearch search_alg.
analysis = tune.run(
objective_two,
metric="mean_loss",
mode="min",
search_alg=algo,
num_samples=num_samples
)
Finally, we again show the hyperparameters that minimize the mean loss defined by the score of the objective function above.
print("Best hyperparameters found were: ", analysis.best_config)
ray.shutdown()