python - 如何将火车模型投入生产？

Question

我已经最终确定了一个模型，并且它的性能在可接受的范围内。我专门使用 python 和 scitkit-learn 。

下一步是将模型投入生产。

我可以请求帮助将这些模型投入生产吗？如何保存经过训练的模型以便将其转移到生产环境。

提前感谢您的帮助。

Answer 1

正如评论者所建议的，您应该使用pickle。特别是对于 ML，您正在寻找的是 Model persistence 。并使用 scikit-learn:

After training a scikit-learn model, it is desirable to have a way to persist the model for future use without having to retrain.

他们的例子:

>>> from sklearn import svm
>>> from sklearn import datasets
>>> clf = svm.SVC()
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> clf.fit(X, y)  
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',
    max_iter=-1, probability=False, random_state=None, shrinking=True,
    tol=0.001, verbose=False)

>>> import pickle
>>> s = pickle.dumps(clf)
>>> clf2 = pickle.loads(s)
>>> clf2.predict(X[0:1])
array([0])
>>> y[0]
0

In the specific case of the scikit, it may be more interesting to use joblib’s replacement of pickle (joblib.dump & joblib.load), which is more efficient on objects that carry large numpy arrays internally as is often the case for fitted scikit-learn estimators, but can only pickle to the disk and not to a string:

>>> from sklearn.externals import joblib
>>> joblib.dump(clf, 'filename.pkl')