Efficient Distributed Hyperparameter Tuning with Ray and Optuna
In this snippet, I'll show you how to perform hyperparameter tuning using Ray and Optuna to scale up your experiments on a distributed environment.
from ray import tune
from ray.tune import OptunaSearch
from sklearn.datasets import make_classification
from sklearn.ensemble import RandomForestClassifier
# Define the hyperparameter space
space = {
"n_estimators": tune.randint(10, 100),
"max_depth": tune.quniform(2, 10, 2),
}
# Define the training loop with Optuna search
def train_fn(config):
X, y = make_classification(n_samples=1000, n_features=10)
model = RandomForestClassifier(**config)
return model.score(X, y)
# Perform hyperparameter tuning
search = OptunaSearch(n_evals=10)
tune.run(train_fn, config=space, search_alg=search)
This code snippet utilizes Ray and Optuna to efficiently perform hyperparameter tuning across a distributed environment. The train_fn function is wrapped with the Optuna search algorithm, which is then used to tune the hyperparameters within the predefined space. The result is a robust and scalable approach to model selection, perfect for complex machine learning projects.
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