Package 'clustermq'

Queue function calls on the cluster

Description

Queue function calls on the cluster

Usage

Q(
  fun,
  ...,
  const = list(),
  export = list(),
  pkgs = c(),
  seed = 128965,
  memory = NULL,
  template = list(),
  n_jobs = NULL,
  job_size = NULL,
  rettype = "list",
  fail_on_error = TRUE,
  workers = NULL,
  log_worker = FALSE,
  chunk_size = NA,
  timeout = Inf,
  max_calls_worker = Inf,
  verbose = TRUE
)

Arguments

fun	A function to call
...	Objects to be iterated in each function call
const	A list of constant arguments passed to each function call
export	List of objects to be exported to the worker
pkgs	Character vector of packages to load on the worker
seed	A seed to set for each function call
memory	Short for 'template=list(memory=value)'
template	A named list of values to fill in the scheduler template
n_jobs	The number of jobs to submit; upper limit of jobs if job_size is given as well
job_size	The number of function calls per job
rettype	Return type of function call (vector type or 'list')
fail_on_error	If an error occurs on the workers, continue or fail?
workers	Optional instance of QSys representing a worker pool
log_worker	Write a log file for each worker
chunk_size	Number of function calls to chunk together defaults to 100 chunks per worker or max. 10 kb per chunk
timeout	Maximum time in seconds to wait for worker (default: Inf)
max_calls_worker	Maxmimum number of chunks that will be sent to one worker
verbose	Print status messages and progress bar (default: TRUE)

Value

A list of whatever 'fun' returned

Examples

## Not run: 
# Run a simple multiplication for numbers 1 to 3 on a worker node
fx = function(x) x * 2
Q(fx, x=1:3, n_jobs=1)
# list(2,4,6)

# Run a mutate() call in dplyr on a worker node
iris %>%
    mutate(area = Q(`*`, e1=Sepal.Length, e2=Sepal.Width, n_jobs=1))
# iris with an additional column 'area'

## End(Not run)

---

Queue function calls defined by rows in a data.frame

Description

Queue function calls defined by rows in a data.frame

Usage

Q_rows(
  df,
  fun,
  const = list(),
  export = list(),
  pkgs = c(),
  seed = 128965,
  memory = NULL,
  template = list(),
  n_jobs = NULL,
  job_size = NULL,
  rettype = "list",
  fail_on_error = TRUE,
  workers = NULL,
  log_worker = FALSE,
  chunk_size = NA,
  timeout = Inf,
  max_calls_worker = Inf,
  verbose = TRUE
)

Arguments

df	data.frame with iterated arguments
fun	A function to call
const	A list of constant arguments passed to each function call
export	List of objects to be exported to the worker
pkgs	Character vector of packages to load on the worker
seed	A seed to set for each function call
memory	Short for 'template=list(memory=value)'
template	A named list of values to fill in the scheduler template
n_jobs	The number of jobs to submit; upper limit of jobs if job_size is given as well
job_size	The number of function calls per job
rettype	Return type of function call (vector type or 'list')
fail_on_error	If an error occurs on the workers, continue or fail?
workers	Optional instance of QSys representing a worker pool
log_worker	Write a log file for each worker
chunk_size	Number of function calls to chunk together defaults to 100 chunks per worker or max. 10 kb per chunk
timeout	Maximum time in seconds to wait for worker (default: Inf)
max_calls_worker	Maxmimum number of chunks that will be sent to one worker
verbose	Print status messages and progress bar (default: TRUE)

Examples

## Not run: 
# Run a simple multiplication for data frame columns x and y on a worker node
fx = function (x, y) x * y
df = data.frame(x = 5, y = 10)
Q_rows(df, fx, job_size = 1)
# [1] 50

# Q_rows also matches the names of a data frame with the function arguments
fx = function (x, y) x - y
df = data.frame(y = 5, x = 10)
Q_rows(df, fx, job_size = 1)
# [1] 5

## End(Not run)

---

Register clustermq as 'foreach' parallel handler

Description

Register clustermq as 'foreach' parallel handler

Usage

register_dopar_cmq(...)

Arguments

...	List of arguments passed to the 'Q' function, e.g. n_jobs

---

Creates a pool of workers

Description

Creates a pool of workers

Usage

workers(
  n_jobs,
  data = NULL,
  reuse = TRUE,
  template = list(),
  log_worker = FALSE,
  qsys_id = getOption("clustermq.scheduler", qsys_default),
  verbose = FALSE,
  ...
)

Arguments

n_jobs	Number of jobs to submit (0 implies local processing)
data	Set common data (function, constant args, seed)
reuse	Whether workers are reusable or get shut down after call
template	A named list of values to fill in template
log_worker	Write a log file for each worker
qsys_id	Character string of QSys class to use
verbose	Print message about worker startup
...	Additional arguments passed to the qsys constructor

Value

An instance of the QSys class

---