XGBoost

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XGBoost
Deploy a model built with the XGBoost framework.
​XGBoost is a gradient boosting framework. Baseten supports deploying XGBoost models out of the box.
All you need to do first is install the Baseten client and create an API key.
Baseten officially supports xgboost version 1.6.1 or higher. Especially if you're using an online notebook environment like Google Colab or a bundle of packages like Anaconda, ensure that the version you are using is supported. If it's not, use the --upgrade flag and pip will install the most recent version.
Deploying a XGBoost model
Deploying a XGBoost model is as simple as:
import baseten
baseten_model = baseten.deploy(
    xgboost_model,
    model_name='My XGBoost model',
)
If you have already saved your model, just load it back into memory, test it to ensure it works, and deploy as in the above.
Example deployment
This code sample deploys a XGBoost model.
import baseten
import xgboost as xgb
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
​
def create_data():
    X, y = make_classification(n_samples=100,
                           n_informative=2,
                           n_classes=2,
                           n_features=6)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
    train = xgb.DMatrix(X_train, y_train)
    test = xgb.DMatrix(X_test, y_test)
    return train, test
​
train, test = create_data()
params = {
    "learning_rate": 0.01,
    "max_depth": 3
}
# training, we set the early stopping rounds parameter
model = xgb.train(params,
        train, evals=[(train, "train"), (test, "validation")],
        num_boost_round=100, early_stopping_rounds=20)
​
baseten.login("*** INSERT API KEY ***")  # https://docs.baseten.co/settings/api-keys
​
baseten_model = baseten.deploy(
    model,
    model_name='lgb model',
)
scikit-learn
Custom models
Last modified 4d ago
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Outline
Deploying a XGBoost model
Example deployment