Suppose you are performing a big hyper-parameter search, using scikit learn
GridSearchCV. Suppose you are running it on some shared platform, say Google Colab, that disconnects you after a while, so that it is impossible to perform the entire search all together.
It would be useful to be able to save the state of the search in the disk, in order to be able to resume the search from the same point some time later.
I know one can achieve this "manually", but not exploring all the parameter space at once, but dividing it in different regions and run the search on each region separately.
However, I am looking for something that does not require any manual intervention or any additional code to subdivide the parameter space.
I think such functionality may be available in other libraries like
Do you know if what I am asking for is possible in
GridSearchCV from sklearn?