Comments (13)
- if more than 1 point is proposes
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Jakob, please do only the above two things.
If you want to do it in more places, discuss it here.
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I added parallelMap in evalTargetFunction
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Jakob, is this done for all places where the target function is evaluated?
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After that I could think of 1-2 more places, that we can discuss, then we can close.
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I think it is done for all places in our mbo core function and all called functions.
Of course the exampleRun function also makes heavy use of function evaluations. What places are you thinking about?
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exampleRun: Good idea. If not parallel, do it now.
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In parallelLCB the different lcb function can be optimized in parallel. This is expensive. Do this, too.
If parallel is tested after that in 2-3 modes, pls close for now.
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Added parallelMap to exampleRun. Works fine with the testet modes local
and multicore
.
Where is 2) located? Can't find it :-)
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You should always test with "socket" too, because in this mode variable are potentially exported.
multicore uses forking = mem sharing with master. -
It is in multipoint lcb. Leave this to me for another reason. I will also close this.
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Before I forget: You also must document in all related R docs that the functions are now parallelized!
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You told me where to annotate, that parallelization is possible, but I unfortunately forgot it.
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Document it somewhere at the end of mbo.
Then either explain it in the tutorial, very BRIEFLY, or add a new isse for that.
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I added a @note to the documentation of mbo and referenced the parallelMap package and the mlrMBO tutorial.
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Related Issues (20)
- negative se values in opt.path might confuse users HOT 1
- invalid class “km” object: the number of experiments must be larger than the spatial dimension HOT 2
- invalid class “km” object: the number of experiments must be larger than the spatial dimension HOT 7
- pkgdown-site points to slack HOT 1
- human in the loop mbo fails if final.evals is != 0
- Undocumented ranges for settings
- Error in if (err < tol) break : missing value where TRUE/FALSE needed for 'classif.gausspr' HOT 6
- The 'configureMlr' can not work with the parallel computing HOT 4
- qLCB not implemented perfectly
- The solution to the died training-No return HOT 1
- Test failure on R-devel HOT 3
- MOIMBO with interleave.random.points causes error HOT 1
- Unable to install mlrMBO using install.packages dependencies=T. I am on IOS and using R v 1.4, someone else? HOT 4
- optimizing "multiple objective" functions with "constraints" HOT 3
- Error: unused arguments (forbidden = expression(x2 > x1)) HOT 1
- Final Answer from mlrMBO outside of the specified variable ranges (multi objective function) HOT 2
- Error: Error in as.data.frame.OptPathDF(opt.path, include.rest = FALSE) : No elements where selected (via 'dob' and 'eol')! HOT 8
- Error: Setting of final.method and final.evals for multi-objective optimization not supported at the moment.
- Possible lack of consistency in xgboost hyperparameters optimization?
- error with AEI again
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