parallelMap | R package to interface some popular parallelization backends | GPU library
kandi X-RAY | parallelMap Summary
kandi X-RAY | parallelMap Summary
parallelMap was written with users in mind who want a unified parallelization procedure in R that.
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parallelMap Examples and Code Snippets
Lines of Code : 29License : Non-SPDX (NOASSERTION)
Copy##### Example 2) #####
library(parallelMap)
parallelStartSocket(2)
parallelLibrary("MASS")
# subsample iris, fit an LDA model and return prediction error
f = function(i) {
n = nrow(iris)
train = sample(n, n/2)
test = setdiff(1:n, train)
mode options(
parallelMap.default.mode = "multicore",
parallelMap.default.cpus = 4,
parallelMap.default.show.info = FALSE
)
parallelStart()
f = function(i) i + 5
y = parallelMap(f, 1:2)
parallelStop()
parallelStart(cpus=2)
f = func .onAttach = function(libname, pkgname) {
# ...
parallelRegisterLevels(package = "mlr", levels = c("benchmark", "resample", "selectFeatures", "tuneParams"))
}
library(mlr)
parallelGetRegisteredLevels()
> mlr: mlr.benchmark, mlr.resample, mlr.s Community Discussions
Trending Discussions on parallelMap
QUESTION
I wrote a TF data pipeline that looks something like this (TF 2.6):
...ANSWER
Answered 2021-Nov-10 at 11:49I finally found the reason for such behaviour. It was caused by using XLA with GPU.
I suddenly found this, and decided to turn off the XLA, and oh god, after almost a week of investigations, GPU was fully utilized and training times became waaay more sane (before that they were equal to CPU training times!!). As it's written in the article: 1) GPU support in XLA is experimental; 2) tensors need to have inferrable shapes; 3) all operations in the graph must be supported in XLA. Signs of such problems are poor CPU and GPU utilization, as well as bouncing training steps, i.e. one step takes 150 seconds, and the next 8-10 steps take one second each, and then this pattern is repeated. The article talks about TF 1.x, but it seems that not much has changed regarding this topic up till now (again, I'm using TF 2.6).
Main takeaways:
- Don't use XLA with GPU blindly, it may degrade your GPU training times down to CPU level (if used incorrectly).
- If you use XLA with GPU, make sure that you meet the requirements described above.
I will update this answer if I manage to meet these XLA requirements in my computations and turn on the XLA with the performance boost, not degradation.
QUESTION
I'm using AMP ParallelFunctions and AMP Promise wait to create an async execution in PHP. The idea is to call multiple HTTP endpoints simultaneously and wait until all of them are resolved.
The code looks something like this:
...ANSWER
Answered 2021-Sep-17 at 17:13This issue happens because PHP Thread Workers don't have access to the global scope where constants were defined.
I ended up passing creating a local variable, assigning the global variable to it, and then pass it to the anonymous function, as Sammitch suggested.
Something like this:
QUESTION
I have a simple recipe to train a model. My categorical variables are changing over time and sometimes I want a numerical to be treated as categorical (postal code) , so I define a list prior to recipe containing them. (just for the sake of the argument, the list is much longer)
recipe worked ok, and then trained my model (3 folds) but an error is raised.
...ANSWER
Answered 2021-Jul-05 at 15:56You definitely were passing the vector of variables correctly to the recipe -- no problem there!
You were running into other problems with your model fitting. An xgboost model requires all predictors to be numeric, so if you convert something like zip code to factors, you need to then use step_dummy(). If you have something of high cardinality like zip codes, you probably will need to handle new levels or unknown levels as well.
QUESTION
I am new to the cats-effect library, and I am running into an issue with parallel execution. I have an application that I think would benefit, but when I test the idea on a toy construct, I can't seem to see a difference in execution time. I feel like I must be missing something obvious to others, so I thought I'd try my luck. In the code below, I have two implementations of summation across sequences of numbers (addInSequence and addInParallel), both executed in the the run() function. When I do run the program, I note that they have virtually identical run times. Am I missing something obvious?
ANSWER
Answered 2020-May-26 at 08:14Two things:
Parallel operations are not guaranteed to be faster always. If your sequential operation is short, then overhead from the dispatch to multiple threads and later gathering all results might be greater than the speedup.
Take a look at what you are measuring. You have one sequential operation that does X amount of work, or 3 operations that do X/3 amount of work. You measure them all and then you compare: time of running X sequentially vs total time of running X/3 amount of work in 3 tasks. If sequential run took about 3 seconds, and each parallel run took about 1 second, by that logic both versions take 3 seconds. Which might be true of we measure CPU usage time, but not quite if we measure time from beginning of all that work to the finish.
If I run your code I get
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