问题
Is it possible to train in parallel multiple different sklearn models?
For example, I'd like to train one SVM, one RandomForest and one Linear Regression model at the same time. The desired output would be a list of objects returned by the .fit method.
回答1:
Is it possible to train in parallel multiple different sklearn models?
Training multiple models?
YES.
Training multiple models in true-[PARALLEL] scheduling fashion?
NO.
Training one particular model, using some sort of low-level, fine-grain ( if not directly silicon-wired ) sorts of vectorisation / ILP-parallelism and improved temporal-locality and effects of cache-coherence?
YES,
already deployed, if resources and low-level code permit, yet these levels are principally constrained by low ratio of work-package payload v/s overheads - ref. re-formulated Amdahl's Law so as to respect both the overheads, resources ( on lower end of the time-scale ) and indivisible atomicity of some sorts of processing-sprint(s) ( on the upper end of the time-scale .. exactly due to the indivisible implementation of the atomic processing-segments so common in the sklearn ML-processing pipelines ).
Training different models in a "just"-[CONCURRENT] scheduling fashion?
YES.
Using a smart distributed-system infrastructure, not a few SLOCs :o)
来源：https://stackoverflow.com/questions/47980936/train-multiple-different-sklearn-models-in-parallel