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?#

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#

Model Inference Time


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)
Model Inference Time


Total running time of the script: ( 0 minutes 0.128 seconds)

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