Introducing Skops

Introducing Skops

At Hugging Face, we are working on tackling various problems in open-source machine learning, including, hosting models securely and openly, enabling reproducibility, explainability and collaboration. We are thrilled to introduce you to our new library: Skops! With Skops, you can host your scikit-learn models on the Hugging Face Hub, create model cards for model documentation and collaborate with others.

Let's go through an end-to-end example: train a model first, and see step-by-step how to leverage Skops for sklearn in production.

# let's import the libraries first
import sklearn
from sklearn.datasets import load_breast_cancer
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split

# Load the data and split
X, y = load_breast_cancer(as_frame=True, return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

# Train the model
model = DecisionTreeClassifier().fit(X_train, y_train)

You can use any model filename and serialization method, like pickle or joblib. At the moment, our backend uses joblib to load the model. hub_utils.init creates a local folder containing the model in the given path, and the configuration file containing the specifications of the environment the model is trained in. The data and the task passed to the init will help Hugging Face Hub enable the inference widget on the model page as well as discoverability features to find the model.

from skops import hub_utils
import pickle

# let's save the model
model_path = "example.pkl"
local_repo = "my-awesome-model"
with open(model_path, mode="bw") as f:
    pickle.dump(model, file=f)

# we will now initialize a local repository
hub_utils.init(
    model=model_path, 
    requirements=[f"scikit-learn={sklearn.__version__}"], 
    dst=local_repo,
    task="tabular-classification",
    data=X_test,
)

We can also add any plot of our choice to the card using add_plot like below.