• API Reference - ModelInferenceTime

Model Inference Time

This notebook provides an overview for using and understanding the Model Inference Time check.

Structure:

• What is the Model Inference Time check?

• Generate data & model

• Run the check

• Define a condition

What is the Model Inference Time check?

The ModelInferenceTime check measures the model’s average inference time (in seconds) per sample. Inference time is an important metric for prediction models, especially in real-time applications and data streaming processes, where a fast runtime can affect the user’s experience or the overall system load.

Imports

from sklearn.datasets import load_iris
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split

from deepchecks.tabular import Dataset
from deepchecks.tabular.checks import ModelInferenceTime

Generate data & model

iris = load_iris(as_frame=True)
train, test = train_test_split(iris.frame, test_size=0.33, random_state=42)

train_ds = Dataset(train, features=iris.feature_names, label='target')
test_ds = Dataset(test, features=iris.feature_names, label='target')

clf = AdaBoostClassifier().fit(train_ds.data[train_ds.features], train_ds.data[train_ds.label_name])

Run the check

check = ModelInferenceTime()
check.run(test_ds, clf)

Define a condition

A condition for the average inference time per sample can be defined. Here, we define the threshold to be 0.00001 seconds.

check = ModelInferenceTime().add_condition_inference_time_less_than(value=0.00001)
check.run(test_ds, clf)

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