# SGDClassifier partial_fit() for online learning - is one step of gradient descent enough?

I'm interested in incremental (online) learning for my logistic regression model trained with SGDClassifier. Basically updating the model as more labeled data comes in. I know I can use partial_fit() for this. The following seems to be a common example on StackOverflow:

clf = linear_model.SGDClassifier()
x1 = some_new_data
y1 = the_labels
clf.partial_fit(x1,y1)

The problem is, partial_fit() performs just one step of gradient descent:
Internally, this method uses max_iter = 1. Therefore, it is not guaranteed that a minimum of the cost function is reached after calling it once. Matters such as objective convergence and early stopping should be handled by the user.