Memory is not released after forked process using furrr

Hi guys,
I´m experiencing an issue in my job trying to run a Random Forest Model.
My data is around 2.0M rows by around 90 columns after running the recipe.

The process is highly imbalanced, so I need to run a downsample and an upsample using CV. Downsample works great because It reduces to a tiny small dataset but after the upsample I would say I have around 2M rows by fold, 1.5M in analysis, and 0.5M in assessment.

What I have noticed is everytime I run a new object the RAM memory is not returned back to the OS, and when I get to the point of running RF I have memory to start but not to finish, I mean, sometimes it says some cores couldn´t retrieve info because they died, or sometimes all of the process go to sleep and this can be run forever. The longer this thing ran was 3 and a half hours just to notice all the process were slept. But normally after 1 minute, all the processes go to sleep.

I recreated a small reproducible example. Of course In this case I have plenty of memory remaining but just wanted to show that Memory is not released.

#> Registered S3 method overwritten by 'xts':
#>   method     from
#>   as.zoo.xts zoo
#> ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────── tidymodels 0.0.3 ──
#> ✓ broom     0.5.3          ✓ recipes
#> ✓ dials     0.0.4          ✓ rsample   0.0.5     
#> ✓ infer     0.5.1          ✓ yardstick 0.0.4     
#> ✓ parsnip
#> ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────── tidymodels_conflicts() ──
#> x scales::discard()   masks purrr::discard()
#> x dplyr::filter()     masks stats::filter()
#> x recipes::fixed()    masks stringr::fixed()
#> x dplyr::lag()        masks stats::lag()
#> x dials::margin()     masks ggplot2::margin()
#> x yardstick::spec()   masks readr::spec()
#> x recipes::step()     masks stats::step()
#> x recipes::yj_trans() masks scales::yj_trans()

#Data used for the example only

#not all of these steps apply for this but I tried to replicate as much as I could from my real case
up_recipe <- recipe(Class ~ . , data = Ionosphere) %>%
  step_nzv(all_predictors()) %>% 
  step_corr(all_numeric(), threshold = 0.7) %>% 
  step_YeoJohnson(all_numeric()) %>%
  step_dummy(all_nominal(), -all_outcomes()) %>% 
  step_center(all_numeric()) %>% 
  step_scale(all_numeric()) %>% 
  step_spatialsign(all_numeric()) %>%
  step_upsample(Class, over_ratio = 0.5)
#> 0.019 sec elapsed


data_CV <- vfold_cv(Ionosphere, v = 10, repeats = 1, strata = Class)
# Activating forked processes
#> Loading required package: future
options(future.fork.enable = TRUE)
cl <- availableCores() - 1
plan(multicore, workers = cl)

# This recipes was stored in a different object, becuase initially i was using mutate to append the model 
# and several recipes and the object was huge and I ran out of memory
recipes_up <-  data_CV$splits  %>%
  future_map(prepper, recipe = up_recipe)
#> 0.457 sec elapsed

model <-  future_map(
  ~ rand_forest(mode = "classification", trees = 200, mtry = 10) %>%
    set_engine("ranger", num.threads = 1) %>%
    fit(Class ~ ., data = juice(.x, everything()))
#> 1.031 sec elapsed

Created on 2019-12-30 by the reprex package (v0.3.0)

I´m running a Centos 7 Machine with Rstudio Server, 24 cores and 234 GB of RAM.

Initially these are my machine stats:


After running the recipes, you´ll notice that more RAM is set as used.


This screenshot is when running the RF model and you´ll see multiprocesses and
that RAM is higher.

Could you guys help out understand why RAM is not being released and how can I manage it efficiently. Some people have told me I would need some docker + kubernetes solution, but sincerely I dont know about devOps and why I´d need those.

@Max and @davis I would really appreciate your help with this. If more details are needed I would be glad to provide more details.

Aditionally, sometimes I´m running instead of ranger H2o models, I´m still suing tidymodels infrastructure because I think recipes is way more powerful that built-in process in h2o. And sometimes i just can´t convert to h2o frame because of memory issues too.

Also, I will be testing {tune} as @Max suggested to see if this is an issue with the parallel backend or something else.

Thanks in advance,


Was this taken after the models are done? If not, wouldn't this be the additional usage by ranger?

Also, I don't think that it is accurate to say that memory isn't being released. The worker processes are still open and running.

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A SVM isn't the most efficient choice when you have 2M rows.

I would try a simple multicore approach. This starts/stops as needed and, in my experience, is the lowest maintenance parallel backend.

I've fit these models to data sets of this size before without major issue like the ones that you describe.

Additionally, I've worked on HPC systems that were not... optimized. Those were the only occasions where I had dropped processes or workers that just never died. I'm not saying that your system is bad. I'm saying that on other systems, I haven't had these issues for problems like those that you describe.

The bottom line is that you should use whatever tool work for you; you need to get things done.

Well, there are two cases. If the process finishes the RAM memory is not released, meaning it is not available again to be used. this happens for example after running the recipes chunk, you can see that nothing is running and you have less memory available. The second case is that the process doesn´t finish and it goes to sleep. So in that case the process runs forever but not changing anything, because all of the process went to sleep. In that case the only solution I have is to restart R manually using Ctrl+Shift+F10 or rebooting my VM from command line.

I tried other models, for example SVM from kernlab using the tune interface, and I have to say the process never goes to sleep, but it ran at least 5 hours and suddenly interrupted, Interruption could have happened because of my machine configuration, but correct me if I´m wrong, I don´t think this is such a huge process to kill this machine.

I´m thinking moving to python to check how it behaves, but I just can´t get over the tidyverse and tidymodels. From an API standpoint tidy apporach is way superior to python's but now i just can't convince my boss that R is capable to deal with these kind of problems.