Hyperparameter search
This example showcases a simple grid search in one dimension, where we try different parameters for a model and pick the one with the best results on a holdout set.
Defining the image
First, let’s build a custom image and install scikit-learn in it.
import modal
app = modal.App(
"example-basic-grid-search",
image=modal.Image.debian_slim().pip_install("scikit-learn~=1.5.0"),
)The Modal function
Next, define the function. Note that we use the custom image with scikit-learn in it.
We also take the hyperparameter k, which is how many nearest neighbors we use.
@app.function()
def fit_knn(k):
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X, y = load_digits(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
clf = KNeighborsClassifier(k)
clf.fit(X_train, y_train)
score = float(clf.score(X_test, y_test))
print("k = %3d, score = %.4f" % (k, score))
return score, kParallel search
To do a hyperparameter search, let’s map over this function with different values
for k, and then select for the best score on the holdout set:
@app.local_entrypoint()
def main():
# Do a basic hyperparameter search
best_score, best_k = max(fit_knn.map(range(1, 100)))
print("Best k = %3d, score = %.4f" % (best_k, best_score))