From 8e6722c2dcfed1a344cb4ee60ca94b5f24f36bd1 Mon Sep 17 00:00:00 2001 From: Jakob Richter Date: Tue, 27 Oct 2020 11:29:27 +0100 Subject: [PATCH] update links --- todo-files/outdated_rmds/parallelization.Rmd | 8 ++++---- vignettes/supplementary/parallelization.Rmd | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/todo-files/outdated_rmds/parallelization.Rmd b/todo-files/outdated_rmds/parallelization.Rmd index ca29521e2..8555262e6 100644 --- a/todo-files/outdated_rmds/parallelization.Rmd +++ b/todo-files/outdated_rmds/parallelization.Rmd @@ -3,7 +3,7 @@ # Parallelization It is possible to parallelize the evaluation of the target function to speed up the computation. Internally the -evaluation of the target function is realized with the R package `parallelMap`. This package offers simple parallelization with various different backends. For details on the usage see the [parallelMap github page](https://github.com/berndbischl/parallelMap#parallelmap), which offers a nice tutorial and describes all possible backends thorougly. For our usage we use a *multicore* backend. Note, that the multicore parallelization does _not_ work on windows machines. +evaluation of the target function is realized with the R package `parallelMap`. This package offers simple parallelization with various different backends. For details on the usage see the [parallelMap github page](https://github.com/mlr-org/parallelMap#parallelmap), which offers a nice tutorial and describes all possible backends thorougly. For our usage we use a *multicore* backend. Note, that the multicore parallelization does _not_ work on windows machines. ```{r eval=FALSE} library(mlrMBO) @@ -34,11 +34,11 @@ library(mlr) fn = function(x) { lrn = makeLearner("classif.ksvm", par.vals = x) - + rdesc = makeResampleDesc("CV", iters = 10L) - + res = resample(learner = lrn, iris.task, rdesc, show.info = FALSE) - + res$aggr } diff --git a/vignettes/supplementary/parallelization.Rmd b/vignettes/supplementary/parallelization.Rmd index 0ae5c53f3..3798e784b 100644 --- a/vignettes/supplementary/parallelization.Rmd +++ b/vignettes/supplementary/parallelization.Rmd @@ -28,7 +28,7 @@ This Vignette will give you a short introduction how **mlrMBO** can be configure The parallelization of multiple evaluations of the target function is realized internally with the R package `parallelMap`. This package offers simple parallelization with various different back-ends. -For details on the usage see the [parallelMap github page](https://github.com/berndbischl/parallelMap#readme), which offers a nice tutorial and describes all possible back-ends thoroughly. +For details on the usage see the [parallelMap github page](https://github.com/mlr-org/parallelMap#readme), which offers a nice tutorial and describes all possible back-ends thoroughly. In this example we use a *multicore* back-end, which is also the most common use-case. Note, that the multicore parallelization does _not_ work on windows machines.