Quick examples

library(drake)
load_basic_example() # Also (over)writes report.Rmd. `example_drake("basic")`, `vignette("quickstart")`.
plot_graph(my_plan) # Hover, click, drag, zoom, pan. Try file = "graph.html" and targets_only = TRUE.
make(my_plan) # Run the workflow.
make(my_plan) # Check that everything is already up to date.

Dive deeper into the built-in examples.

example_drake("basic") # Write the code files.
examples_drake() # List the other examples.
vignette("quickstart") # Same as https://cran.r-project.org/package=drake/vignettes/quickstart.html

Useful functions

Besides make(), here are some useful functions to learn about drake,

load_basic_example()
drake_tip()
examples_drake()
example_drake()

set up your workflow plan,

plan()
analyses()
summaries()
evaluate()
expand()
gather()
wildcard() # from the wildcard package

explore the dependency network,

outdated()
missed()
plot_graph()
dataframes_graph()
render_graph()
read_graph()
deps()
tracked()

interact with the cache,

clean()
cached()
imported()
built()
build_times()
readd()
loadd()
find_project()
find_cache()

debug your work,

check()
session()
in_progress()
progress()
config()
read_config()

and speed up your project with parallel computing.

make() # with jobs > 2
max_useful_jobs()
parallelism_choices()
shell_file()

Setting up the basic example

Let’s establish the building blocks of a data analysis workflow.

library(knitr)
library(drake)

First, we will generate a few datasets.

simulate = function(n){
  data.frame(
    x = stats::rnorm(n), # Drake tracks calls like `pkg::fn()` (namespaced functions).
    y = rpois(n, 1)
  )
}

Then, we will analyze each dataset with multiple analysis methods.

reg1 = function(d){
  lm(y ~ + x, data = d)
}

reg2 = function(d){
  d$x2 = d$x^2
  lm(y ~ x2, data = d)
}

Finally, we will generate a dynamic report to display results.

my_knit = function(file, ...){
  knit(file)
}

We need the source file report.Rmd.

lines = c(
  "---",
  "title: Example Report",
  "author: You",
  "output: html_document",
  "---",
  "",
  "Look how I read outputs from the drake cache.",
  "",
  "```{r example_chunk}",
  "library(drake)",
  "readd(small)",
  "readd(coef_regression2_small)", # Return an object from the drake cache.
  "loadd(large)", # Load an object from the drake cache into your workspace.
  "head(large)",
  "```")
writeLines(lines, "report.Rmd")

Workflow plan

The workflow plan lists the intermediate steps of your project.

load_basic_example()
my_plan

##                    target                                      command
## 1             'report.md'   my_knit('report.Rmd', report_dependencies)
## 2                   small                                  simulate(5)
## 3                   large                                 simulate(50)
## 4     report_dependencies      c(small, large, coef_regression2_small)
## 5       regression1_small                                  reg1(small)
## 6       regression1_large                                  reg1(large)
## 7       regression2_small                                  reg2(small)
## 8       regression2_large                                  reg2(large)
## 9  summ_regression1_small suppressWarnings(summary(regression1_small))
## 10 summ_regression1_large suppressWarnings(summary(regression1_large))
## 11 summ_regression2_small suppressWarnings(summary(regression2_small))
## 12 summ_regression2_large suppressWarnings(summary(regression2_large))
## 13 coef_regression1_small                      coef(regression1_small)
## 14 coef_regression1_large                      coef(regression1_large)
## 15 coef_regression2_small                      coef(regression2_small)
## 16 coef_regression2_large                      coef(regression2_large)

Each row is an intermediate step, and each command generates a target. A target is an output R object (cached when generated) or output file (specified with single quotes), and a command just an ordinary piece of R code (not necessarily a single function call). As input, commands may take objects imported from your workspace, targets generated by other commands, or initial input files. These dependencies give your project an underlying network.

# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px") 
# See also dataframes_graph(), render_graph().
# Speed up regraphing with config().

You can also check the dependencies of individual targets.

deps(reg2)

## [1] "lm"

deps(my_plan$command[1]) # report.Rmd is single-quoted because it is a file dependency.

## [1] "'report.Rmd'"        "my_knit"             "report_dependencies"

deps(my_plan$command[16])

## [1] "coef"              "regression2_large"

List all the reproducibly-tracked objects and files, including imports and targets.

tracked(my_plan, targets = "small")

## Unloading targets from environment:
##   report_dependencies

## [1] "small"        "simulate"     "data.frame"   "rpois"       
## [5] "stats::rnorm"

tracked(my_plan)

##  [1] "'report.md'"            "small"                 
##  [3] "large"                  "report_dependencies"   
##  [5] "regression1_small"      "regression1_large"     
##  [7] "regression2_small"      "regression2_large"     
##  [9] "summ_regression1_small" "summ_regression1_large"
## [11] "summ_regression2_small" "summ_regression2_large"
## [13] "coef_regression1_small" "coef_regression1_large"
## [15] "coef_regression2_small" "coef_regression2_large"
## [17] "reg1"                   "reg2"                  
## [19] "simulate"               "my_knit"               
## [21] "'report.Rmd'"           "c"                     
## [23] "summary"                "suppressWarnings"      
## [25] "coef"                   "lm"                    
## [27] "data.frame"             "rpois"                 
## [29] "stats::rnorm"           "knit"

Check for cycles, missing input files, and other pitfalls.

check(my_plan)

Generate the workflow plan

The data frame my_plan would be a pain to write by hand, so drake has functions to help you.

my_datasets = plan(
  small = simulate(5),
  large = simulate(50))
my_datasets

##   target      command
## 1  small  simulate(5)
## 2  large simulate(50)

expand(my_datasets, values = c("rep1", "rep2"))

##       target      command
## 1 small_rep1  simulate(5)
## 2 small_rep2  simulate(5)
## 3 large_rep1 simulate(50)
## 4 large_rep2 simulate(50)

methods = plan(
  regression1 = reg1(..dataset..),
  regression2 = reg2(..dataset..))
methods

##        target           command
## 1 regression1 reg1(..dataset..)
## 2 regression2 reg2(..dataset..)

my_analyses = analyses(methods, data = my_datasets)
my_analyses

##              target     command
## 1 regression1_small reg1(small)
## 2 regression1_large reg1(large)
## 3 regression2_small reg2(small)
## 4 regression2_large reg2(large)

summary_types = plan(
  summ = suppressWarnings(summary(..analysis..)), # Occasionally there is a perfect regression fit.
  coef = coef(..analysis..))
summary_types

##   target                                 command
## 1   summ suppressWarnings(summary(..analysis..))
## 2   coef                      coef(..analysis..)

results = summaries(summary_types, analyses = my_analyses, 
  datasets = my_datasets, gather = NULL)
results

##                   target                                      command
## 1 summ_regression1_small suppressWarnings(summary(regression1_small))
## 2 summ_regression1_large suppressWarnings(summary(regression1_large))
## 3 summ_regression2_small suppressWarnings(summary(regression2_small))
## 4 summ_regression2_large suppressWarnings(summary(regression2_large))
## 5 coef_regression1_small                      coef(regression1_small)
## 6 coef_regression1_large                      coef(regression1_large)
## 7 coef_regression2_small                      coef(regression2_small)
## 8 coef_regression2_large                      coef(regression2_large)

load_in_report = plan(
  report_dependencies = c(small, large, coef_regression2_small))
load_in_report

##                target                                 command
## 1 report_dependencies c(small, large, coef_regression2_small)

report = plan(
  report.md = my_knit('report.Rmd', report_dependencies),
  file_targets = TRUE, strings_in_dots = "filenames")
report

##        target                                    command
## 1 'report.md' my_knit('report.Rmd', report_dependencies)

my_plan = rbind(report, my_datasets, load_in_report, my_analyses, results)
my_plan

##                    target                                      command
## 1             'report.md'   my_knit('report.Rmd', report_dependencies)
## 2                   small                                  simulate(5)
## 3                   large                                 simulate(50)
## 4     report_dependencies      c(small, large, coef_regression2_small)
## 5       regression1_small                                  reg1(small)
## 6       regression1_large                                  reg1(large)
## 7       regression2_small                                  reg2(small)
## 8       regression2_large                                  reg2(large)
## 9  summ_regression1_small suppressWarnings(summary(regression1_small))
## 10 summ_regression1_large suppressWarnings(summary(regression1_large))
## 11 summ_regression2_small suppressWarnings(summary(regression2_small))
## 12 summ_regression2_large suppressWarnings(summary(regression2_large))
## 13 coef_regression1_small                      coef(regression1_small)
## 14 coef_regression1_large                      coef(regression1_large)
## 15 coef_regression2_small                      coef(regression2_small)
## 16 coef_regression2_large                      coef(regression2_large)

df = plan(data = simulate(center = MU, scale = SIGMA))
df

##   target                              command
## 1   data simulate(center = MU, scale = SIGMA)

df = expand(df, values = c("rep1", "rep2"))
df

##      target                              command
## 1 data_rep1 simulate(center = MU, scale = SIGMA)
## 2 data_rep2 simulate(center = MU, scale = SIGMA)

evaluate(df, wildcard = "MU", values = 1:2)

##        target                             command
## 1 data_rep1_1 simulate(center = 1, scale = SIGMA)
## 2 data_rep1_2 simulate(center = 2, scale = SIGMA)
## 3 data_rep2_1 simulate(center = 1, scale = SIGMA)
## 4 data_rep2_2 simulate(center = 2, scale = SIGMA)

evaluate(df, wildcard = "MU", values = 1:2, expand = FALSE)

##      target                             command
## 1 data_rep1 simulate(center = 1, scale = SIGMA)
## 2 data_rep2 simulate(center = 2, scale = SIGMA)

evaluate(df, rules = list(MU = 1:2, SIGMA = c(0.1, 1)), expand = FALSE)

##      target                           command
## 1 data_rep1 simulate(center = 1, scale = 0.1)
## 2 data_rep2   simulate(center = 2, scale = 1)

evaluate(df, rules = list(MU = 1:2, SIGMA = c(0.1, 1, 10)))

##             target                           command
## 1  data_rep1_1_0.1 simulate(center = 1, scale = 0.1)
## 2    data_rep1_1_1   simulate(center = 1, scale = 1)
## 3   data_rep1_1_10  simulate(center = 1, scale = 10)
## 4  data_rep1_2_0.1 simulate(center = 2, scale = 0.1)
## 5    data_rep1_2_1   simulate(center = 2, scale = 1)
## 6   data_rep1_2_10  simulate(center = 2, scale = 10)
## 7  data_rep2_1_0.1 simulate(center = 1, scale = 0.1)
## 8    data_rep2_1_1   simulate(center = 1, scale = 1)
## 9   data_rep2_1_10  simulate(center = 1, scale = 10)
## 10 data_rep2_2_0.1 simulate(center = 2, scale = 0.1)
## 11   data_rep2_2_1   simulate(center = 2, scale = 1)
## 12  data_rep2_2_10  simulate(center = 2, scale = 10)

gather(df)

##   target                                            command
## 1 target list(data_rep1 = data_rep1, data_rep2 = data_rep2)

gather(df, target = "my_summaries", gather = "rbind")

##         target                                             command
## 1 my_summaries rbind(data_rep1 = data_rep1, data_rep2 = data_rep2)

Run the workflow

You may want to check for outdated or missing targets/imports first.

outdated(my_plan, verbose = FALSE) # These are the targets that need to be (re)built.

##  [1] "'report.md'"            "coef_regression1_large"
##  [3] "coef_regression1_small" "coef_regression2_large"
##  [5] "coef_regression2_small" "large"                 
##  [7] "regression1_large"      "regression1_small"     
##  [9] "regression2_large"      "regression2_small"     
## [11] "report_dependencies"    "small"                 
## [13] "summ_regression1_large" "summ_regression1_small"
## [15] "summ_regression2_large" "summ_regression2_small"

missed(my_plan, verbose = FALSE) # Make sure nothing is missing from your workspace.

Then just make(my_plan).

make(my_plan)

## check 10 items
## import 'report.Rmd'
## import c
## import summary
## import suppressWarnings
## import coef
## import lm
## import data.frame
## import rpois
## import stats::rnorm
## import knit
## check 4 items
## import reg1
## import reg2
## import simulate
## import my_knit
## check 2 items
## target small
## target large
## check 4 items
## target regression1_small
## target regression1_large
## target regression2_small
## target regression2_large
## check 8 items
## target summ_regression1_small
## target summ_regression1_large
## target summ_regression2_small
## target summ_regression2_large
## target coef_regression1_small
## target coef_regression1_large
## target coef_regression2_small
## target coef_regression2_large
## check 1 item
## target report_dependencies
## check 1 item
## target 'report.md'

The non-file dependencies of your last target are already loaded in your workspace.

"report_dependencies" %in% ls() # Should be TRUE.

## [1] TRUE

outdated(my_plan, verbose = FALSE) # Everything is up to date.
build_times(digits = 4) # How long did it take to make each target?

##                    target   user system elapsed
## 1             'report.md' 0.018s 0.002s   0.02s
## 2  coef_regression1_large 0.001s     0s  0.001s
## 3  coef_regression1_small     0s     0s      0s
## 4  coef_regression2_large 0.001s     0s      0s
## 5  coef_regression2_small 0.001s     0s  0.001s
## 6                   large 0.001s     0s  0.001s
## 7       regression1_large 0.001s     0s  0.002s
## 8       regression1_small 0.003s     0s  0.003s
## 9       regression2_large 0.002s     0s  0.001s
## 10      regression2_small 0.001s     0s  0.001s
## 11    report_dependencies 0.001s     0s  0.001s
## 12                  small 0.002s     0s  0.002s
## 13 summ_regression1_large 0.001s     0s  0.001s
## 14 summ_regression1_small 0.002s     0s  0.002s
## 15 summ_regression2_large 0.001s     0s  0.001s
## 16 summ_regression2_small 0.001s     0s  0.001s

# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px") # The red nodes from before turned green.
# dataframes_graph(my_plan) # Get visNetwork nodes and edges so you can
                            # make your own plot with visNetwork or render_graph().

Use readd() and loadd() to load more targets. (They are cached in the hidden .drake/ folder using storr). Other functions interact and view the cache.

readd(coef_regression2_large)

## (Intercept)          x2 
##   0.5682171   0.2275959

loadd(small)
head(small)

##            x y
## 1  0.3461223 2
## 2  1.5181933 1
## 3  0.2383665 0
## 4  1.5992608 2
## 5 -0.1831176 2

rm(small)
cached(small, large)

## small large 
##  TRUE  TRUE

cached()

##  [1] "'report.Rmd'"           "'report.md'"           
##  [3] "c"                      "coef"                  
##  [5] "coef_regression1_large" "coef_regression1_small"
##  [7] "coef_regression2_large" "coef_regression2_small"
##  [9] "data.frame"             "knit"                  
## [11] "large"                  "lm"                    
## [13] "my_knit"                "reg1"                  
## [15] "reg2"                   "regression1_large"     
## [17] "regression1_small"      "regression2_large"     
## [19] "regression2_small"      "report_dependencies"   
## [21] "rpois"                  "simulate"              
## [23] "small"                  "stats::rnorm"          
## [25] "summ_regression1_large" "summ_regression1_small"
## [27] "summ_regression2_large" "summ_regression2_small"
## [29] "summary"                "suppressWarnings"

built()

##  [1] "'report.md'"            "coef_regression1_large"
##  [3] "coef_regression1_small" "coef_regression2_large"
##  [5] "coef_regression2_small" "large"                 
##  [7] "regression1_large"      "regression1_small"     
##  [9] "regression2_large"      "regression2_small"     
## [11] "report_dependencies"    "small"                 
## [13] "summ_regression1_large" "summ_regression1_small"
## [15] "summ_regression2_large" "summ_regression2_small"

imported()

##  [1] "'report.Rmd'"     "c"                "coef"            
##  [4] "data.frame"       "knit"             "lm"              
##  [7] "my_knit"          "reg1"             "reg2"            
## [10] "rpois"            "simulate"         "stats::rnorm"    
## [13] "summary"          "suppressWarnings"

head(read_plan())

##                target                                    command
## 1         'report.md' my_knit('report.Rmd', report_dependencies)
## 2               small                                simulate(5)
## 3               large                               simulate(50)
## 4 report_dependencies    c(small, large, coef_regression2_small)
## 5   regression1_small                                reg1(small)
## 6   regression1_large                                reg1(large)

# read_graph() # Plots the graph of the workflow you just ran.
head(progress()) # See also in_progress()

##           'report.Rmd'            'report.md'                      c 
##             "finished"             "finished"             "finished" 
##                   coef coef_regression1_large coef_regression1_small 
##             "finished"             "finished"             "finished"

# session(): sessionInfo() of the last call to make()
progress(large)

##      large 
## "finished"

The next time you run make(my_plan), nothing will be built because drake knows everything is up to date.

make(my_plan)

## check 10 items
## import 'report.Rmd'
## import c
## import summary
## import suppressWarnings
## import coef
## import data.frame
## import rpois
## import stats::rnorm
## import lm
## import knit
## check 4 items
## import simulate
## import reg1
## import reg2
## import my_knit
## check 2 items
## check 4 items
## check 8 items
## check 1 item
## check 1 item

But if you change one of your functions, commands, or other dependencies, drake will update the affected parts of the workflow. Let’s say we want to change the quadratic term to a cubic term in our reg2() function.

reg2 = function(d){
  d$x3 = d$x^3
  lm(y ~ x3, data = d)
}

The targets depending on reg2() need to be rebuilt and everything else is left alone.

outdated(my_plan, verbose = FALSE)

## [1] "'report.md'"            "coef_regression2_large"
## [3] "coef_regression2_small" "regression2_large"     
## [5] "regression2_small"      "report_dependencies"   
## [7] "summ_regression2_large" "summ_regression2_small"

# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px")

make(my_plan)

## check 10 items
## import 'report.Rmd'
## import c
## import summary
## import suppressWarnings
## import coef
## import data.frame
## import rpois
## import stats::rnorm
## import lm
## import knit
## check 4 items
## import simulate
## import reg1
## import reg2
## import my_knit
## check 2 items
## check 4 items
## target regression2_small
## target regression2_large
## check 8 items
## target summ_regression2_small
## target summ_regression2_large
## target coef_regression2_small
## target coef_regression2_large
## check 1 item
## target report_dependencies
## check 1 item
## target 'report.md'

But trivial changes to whitespace and comments are totally ignored in your functions and in my_plan$command.

reg2 = function(d){
  d$x3 = d$x^3
    lm(y ~ x3, data = d) # I indented here.
}
outdated(my_plan, verbose = FALSE) # Everything is up to date.

Need to add new work on the fly? Just append rows to the workflow plan. If the rest of your workflow is up to date, only the new work is run.

new_simulation = function(n){
  data.frame(x = rnorm(n), y = rnorm(n))
}

additions = plan(
  new_data = new_simulation(36) + sqrt(10))  
additions

##     target                       command
## 1 new_data new_simulation(36) + sqrt(10)

my_plan = rbind(my_plan, additions)
my_plan

##                    target                                      command
## 1             'report.md'   my_knit('report.Rmd', report_dependencies)
## 2                   small                                  simulate(5)
## 3                   large                                 simulate(50)
## 4     report_dependencies      c(small, large, coef_regression2_small)
## 5       regression1_small                                  reg1(small)
## 6       regression1_large                                  reg1(large)
## 7       regression2_small                                  reg2(small)
## 8       regression2_large                                  reg2(large)
## 9  summ_regression1_small suppressWarnings(summary(regression1_small))
## 10 summ_regression1_large suppressWarnings(summary(regression1_large))
## 11 summ_regression2_small suppressWarnings(summary(regression2_small))
## 12 summ_regression2_large suppressWarnings(summary(regression2_large))
## 13 coef_regression1_small                      coef(regression1_small)
## 14 coef_regression1_large                      coef(regression1_large)
## 15 coef_regression2_small                      coef(regression2_small)
## 16 coef_regression2_large                      coef(regression2_large)
## 17               new_data                new_simulation(36) + sqrt(10)

make(my_plan)

## check 12 items
## import 'report.Rmd'
## import c
## import summary
## import suppressWarnings
## import coef
## import sqrt
## import data.frame
## import rnorm
## import rpois
## import stats::rnorm
## import lm
## import knit
## check 5 items
## import new_simulation
## import simulate
## import reg1
## import reg2
## import my_knit
## check 3 items
## target new_data
## check 4 items
## check 8 items
## check 1 item
## check 1 item

If you ever need to erase your work, use clean(). Any targets removed from the cache will have to be rebuilt on the next call to make(), so be careful.

clean(small, reg1) # uncaches individual targets and imported objects
clean() # cleans all targets out of the cache
clean(destroy = TRUE) # removes the cache entirely

High-performance computing

The network graph is the key to drake’s parallel computing.

clean()
load_basic_example()
make(my_plan, jobs = 2, verbose = FALSE) # Parallelize over 2 jobs.
reg2 = function(d){ # Change a dependency.
  d$x3 = d$x^3
  lm(y ~ x3, data = d)
}

# Hover, click, drag, zoom, and pan.
plot_graph(my_plan, width = "100%", height = "500px")

When you call make(my_plan, jobs = 4), the work proceeds in chronological order from left to right. The items are built or imported column by column in sequence, and up-to-date targets are skipped. Within each column, the targets/objects are all independent of each other conditional on the previous steps, so they are distributed over the 4 available parallel jobs/workers. Assuming the targets are rate-limiting (as opposed to imported objects), the next make(..., jobs = 4) should be faster than make(..., jobs = 1), but it would be superfluous to use more than 4 jobs.

See function max_useful_jobs() to suggest the number of jobs, taking into account which targets are already up to date. Try out the following in a fresh R session.

library(drake)
load_basic_example()
plot_graph(my_plan) # Set targets_only to TRUE for smaller graphs.
max_useful_jobs(my_plan) # 8
max_useful_jobs(my_plan, imports = "files") # 8
max_useful_jobs(my_plan, imports = "all") # 10
max_useful_jobs(my_plan, imports = "none") # 8
make(my_plan, jobs = 4)
plot_graph(my_plan)
# Ignore the targets already built.
max_useful_jobs(my_plan) # 1
max_useful_jobs(my_plan, imports = "files") # 1
max_useful_jobs(my_plan, imports = "all") # 10
max_useful_jobs(my_plan, imports = "none") # 0
# Change a function so some targets are now out of date.
reg2 = function(d){
  d$x3 = d$x^3
  lm(y ~ x3, data = d)
}
plot_graph(my_plan)
max_useful_jobs(my_plan) # 4
max_useful_jobs(my_plan, imports = "files") # 4
max_useful_jobs(my_plan, imports = "all") # 10
max_useful_jobs(my_plan, imports = "none") # 4

As for how the parallelism is implemented, you can choose from multiple built-in backends.

1. mclapply: low-overhead, light-weight. drake::make(my_plan, parallelism = "mclapply", jobs = 2) invokes parallel::mclapply() under the hood, distributing the work over at most two independent processes (set with jobs). Mclapply is an ideal choice for low-overhead single-node parallelism, but it does not work on Windows.
2. parLapply: medium-overhead, light-weight. make(my_plan, parallelism = "parLapply", jobs = 2) invokes parallel::mclapply() under the hood. This option is similar to mclapply except that it works on Windows and costs a little extra time up front.
3. Makefile: high-overhead, heavy-duty. For this one, Windows users need to download and install Rtools. For everyone else, just make sure Make is installed. The build order may be different for Makefile parallelism because all the imports are imported before any of the targets are built with the Makefile. That means plot_graph(), dataframes_graph(), and max_useful_jobs() behave differently for .

• make(my_plan, parallelism = "Makefile", jobs = 2) creates a proper Makefile to distribute the work over multiple independent R sessions.
• make(my_plan, parallelism = "Makefile", command = "make", args = "--jobs=4 --silent") gives you finer control over the arguments and the program for the Makefile. For example, you could use lsmake instead of Make itself.
• make(my_plan, parallelism = "Makefile", jobs = 2, prepend = "SHELL=./shell.sh") is similar, but it uses a helper file shell.sh to distribute the R sessions over different jobs/nodes on a cluster. Your shell.sh file should look something like the following, and you can use shell_file() to write an example.

#!/bin/bash
shift
echo "module load R; $*" | qsub -sync y -cwd -j y

You may need to replace module load R with a command to load a specific version of R. SLURM users can just point to srun and dispense with shell.sh altogether.

make(my_plan, parallelism = "Makefile", jobs = 4,
  prepend = "SHELL=srun")

For long projects, put your call to make() in an R script (say, script.R) and run it from the Linux terminal.

nohup nice -19 R CMD BATCH script.R &

Even after you log out, a background process will keep running on the login node and submit new jobs at the appropriate time. Jobs are only submitted if the targets need to be (re)built.

make(..., parallelism = "Makefile", jobs = 2, args = "--jobs=4")