How can I parallelize fitting a gradient boosting model with sklearn?

Question

Relatively new to model building with sklearn. I know cross validation can be parallelized via the n_jobs parameter, but if I'm not using CV, how can I utilize my available cores to speed up model fitting?

Answer 1

There are alternatives like XGboost or LigtGMB that are distributed (i.e., can run parallel). These are well documented and popular boosting algorithms.