How mlxtend StackingRegressor with multiple cpu?











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I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.



For example:



In XGboost:



import xgboost as xgb
xgb_pars = {'nthread': -1}
xgb1=XGBRegressor(**xgb_pars)


Then I know I will use up all cpu core in this algorithm



But what if I try it with mlxtend StackingRegressor?



I guess this method will use cpu that I arranged to each algorithm.



Example:
XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1



So finally I am using 7 cores.










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    up vote
    0
    down vote

    favorite












    I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.



    For example:



    In XGboost:



    import xgboost as xgb
    xgb_pars = {'nthread': -1}
    xgb1=XGBRegressor(**xgb_pars)


    Then I know I will use up all cpu core in this algorithm



    But what if I try it with mlxtend StackingRegressor?



    I guess this method will use cpu that I arranged to each algorithm.



    Example:
    XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1



    So finally I am using 7 cores.










    share|improve this question
























      up vote
      0
      down vote

      favorite









      up vote
      0
      down vote

      favorite











      I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.



      For example:



      In XGboost:



      import xgboost as xgb
      xgb_pars = {'nthread': -1}
      xgb1=XGBRegressor(**xgb_pars)


      Then I know I will use up all cpu core in this algorithm



      But what if I try it with mlxtend StackingRegressor?



      I guess this method will use cpu that I arranged to each algorithm.



      Example:
      XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1



      So finally I am using 7 cores.










      share|improve this question













      I would like to use mlxtend StackingRegressor to ensemble XGBoost,LGBM and Catboost .But I am not sure how much cpu I will use in this method.



      For example:



      In XGboost:



      import xgboost as xgb
      xgb_pars = {'nthread': -1}
      xgb1=XGBRegressor(**xgb_pars)


      Then I know I will use up all cpu core in this algorithm



      But what if I try it with mlxtend StackingRegressor?



      I guess this method will use cpu that I arranged to each algorithm.



      Example:
      XGBoost:2 LGBM:2 CatBoost:2 Meta regressor:1



      So finally I am using 7 cores.







      machine-learning xgboost lightgbm catboost mlxtend






      share|improve this question













      share|improve this question











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      share|improve this question










      asked Nov 22 at 16:46









      Hq Li

      34




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          Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.



          BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples






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            up vote
            0
            down vote













            Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.



            BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples






            share|improve this answer

























              up vote
              0
              down vote













              Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.



              BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples






              share|improve this answer























                up vote
                0
                down vote










                up vote
                0
                down vote









                Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.



                BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples






                share|improve this answer












                Nope, the code seems to fit models one after another, see here. So first you will use 2 cores to train XGB, when it finishes- 2 cores for LGBM, and so on.



                BTW, thanks for sharing mlxtend- i was not aware of it. Seems to have many useful tools, that i had to develop myself and thus to re-invent the wheel :) The only unfortunate thing seems to be missing docs, but there are inlined docs and a very good set of examples







                share|improve this answer












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                share|improve this answer










                answered Dec 1 at 15:35









                Mykhailo Lisovyi

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