batchtools: Tools for Computation on Batch Systems

Description

For bug reports and feature requests please use the tracker: https://github.com/mlr-org/batchtools.

Package options

batchtools.verbose

Verbosity. Set to FALSE to suppress info messages and progress bars.

batchtools.progress

Progress bars. Set to FALSE to disable them.

batchtools.timestamps

Add time stamps to log output. Set to FALSE to disable them.

Furthermore, you may enable a debug mode using the debugme package by setting the environment variable “DEBUGME” to “batchtools” before loading batchtools.

Author(s)

Maintainer: Michel Lang michellang@gmail.com (ORCID)

Authors:

• Bernd Bischl bernd_bischl@gmx.net

Other contributors:

• Dirk Surmann surmann@statistik.tu-dortmund.de (ORCID) [contributor]

See Also

Useful links:

• https://github.com/mlr-org/batchtools

• https://batchtools.mlr-org.com

• Report bugs at https://github.com/mlr-org/batchtools/issues

Set and Retrieve Job Names

Description

Set custom names for jobs. These are passed to the template as ‘job.name’. If no custom name is set (or any of the job names of the chunk is missing), the job hash is used as job name. Individual job names can be accessed via jobs$job.name.

Usage

setJobNames(ids = NULL, names, reg = getDefaultRegistry())
getJobNames(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

setJobNames returns NULL invisibly, getJobTable returns a data.table with columns job.id and job.name.

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
ids = batchMap(identity, 1:10, reg = tmp)
setJobNames(ids, letters[1:nrow(ids)], reg = tmp)
getJobNames(reg = tmp)

Inner, Left, Right, Outer, Semi and Anti Join for Data Tables

Description

These helper functions perform join operations on data tables. Most of them are basically one-liners. See https://rpubs.com/ronasta/join_data_tables for a overview of join operations in data table or alternatively dplyr's vignette on two table verbs.

Usage

ijoin(x, y, by = NULL)
ljoin(x, y, by = NULL)
rjoin(x, y, by = NULL)
ojoin(x, y, by = NULL)
sjoin(x, y, by = NULL)
ajoin(x, y, by = NULL)
ujoin(x, y, all.y = FALSE, by = NULL)

Arguments

Value

[data.table ] with key identical to by.

Examples

# Create two tables for demonstration
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(identity, x = 1:6, reg = tmp)
x = getJobPars(reg = tmp)
y = findJobs(x >= 2 & x <= 5, reg = tmp)
y$extra.col = head(letters, nrow(y))
# Inner join: similar to intersect(): keep all columns of x and y with common matches
ijoin(x, y)
# Left join: use all ids from x, keep all columns of x and y
ljoin(x, y)
# Right join: use all ids from y, keep all columns of x and y
rjoin(x, y)
# Outer join: similar to union(): keep all columns of x and y with matches in x or y
ojoin(x, y)
# Semi join: filter x with matches in y
sjoin(x, y)
# Anti join: filter x with matches not in y
ajoin(x, y)
# Updating join: Replace values in x with values in y
ujoin(x, y)

Add or Remove Job Tags

Description

Add and remove arbitrary tags to jobs.

Usage

addJobTags(ids = NULL, tags, reg = getDefaultRegistry())
removeJobTags(ids = NULL, tags, reg = getDefaultRegistry())
getUsedJobTags(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] with job ids affected (invisible).

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
ids = batchMap(sqrt, x = -3:3, reg = tmp)
# Add new tag to all ids
addJobTags(ids, "needs.computation", reg = tmp)
getJobTags(reg = tmp)
# Add more tags
addJobTags(findJobs(x < 0, reg = tmp), "x.neg", reg = tmp)
addJobTags(findJobs(x > 0, reg = tmp), "x.pos", reg = tmp)
getJobTags(reg = tmp)
# Submit first 5 jobs and remove tag if successful
ids = submitJobs(1:5, reg = tmp)
if (waitForJobs(reg = tmp))
 removeJobTags(ids, "needs.computation", reg = tmp)
getJobTags(reg = tmp)
# Grep for warning message and add a tag
addJobTags(grepLogs(pattern = "NaNs produced", reg = tmp), "div.zero", reg = tmp)
getJobTags(reg = tmp)
# All tags where tag x.neg is set:
ids = findTagged("x.neg", reg = tmp)
getUsedJobTags(ids, reg = tmp)

Create a Linux-Worker

Description

R6Class to create local and remote linux workers.

Format

An R6Class generator object

Value

[Worker ].

Fields

nodename

Host name. Set via constructor.

ncpus

Number of CPUs. Set via constructor and defaults to a heuristic which tries to detect the number of CPUs of the machine.

max.load

Maximum load average (of the last 5 min). Set via constructor and defaults to the number of CPUs of the machine.

status

Status of the worker; one of “unknown”, “available”, “max.cpus” and “max.load”.

Methods

new(nodename, ncpus, max.load)

Constructor.

update(reg)

Update the worker status.

list(reg)

List running jobs.

start(reg, fn, outfile)

Start job collection in file “fn” and output to “outfile”.

kill(reg, batch.id)

Kill job matching the “batch.id”.

Examples

## Not run: 
# create a worker for the local machine and use 4 CPUs.
Worker$new("localhost", ncpus = 4)
## End(Not run)

Define Algorithms for Experiments

Description

Algorithms are functions which get the data part as well as the problem instance (the return value of the function defined in Problem ) and return an arbitrary R object.

This function serializes all components to the file system and registers the algorithm in the ExperimentRegistry .

removeAlgorithm removes all jobs from the registry which depend on the specific algorithm. reg$algorithms holds the IDs of already defined algorithms.

Usage

addAlgorithm(name, fun = NULL, reg = getDefaultRegistry())
removeAlgorithms(name, reg = getDefaultRegistry())

Arguments

Value

[Algorithm]. Object of class “Algorithm”.

See Also

Problem , addExperiments

Add Experiments to the Registry

Description

Adds experiments (parametrized combinations of problems with algorithms) to the registry and thereby defines batch jobs.

If multiple problem designs or algorithm designs are provided, they are combined via the Cartesian product. E.g., if you have two problems p1 and p2 and three algorithms a1, a2 and a3, addExperiments creates experiments for all parameters for the combinations (p1, a1), (p1, a2), (p1, a3), (p2, a1), (p2, a2) and (p2, a3).

Usage

addExperiments(
 prob.designs = NULL,
 algo.designs = NULL,
 repls = 1L,
 combine = "crossprod",
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] with ids of added jobs stored in column “job.id”.

Note

R's data.frame converts character vectors to factors by default in R versions prior to 4.0.0 which frequently resulted in problems using addExperiments. Therefore, this function will warn about factor variables if the following conditions hold:

1. R version is < 4.0.0

2. The design is passed as a data.frame, not a data.table or tibble .

3. The option “stringsAsFactors” is not set or set to TRUE.

See Also

Other Experiment: removeExperiments(), summarizeExperiments()

Examples

tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
# add first problem
fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd)
addProblem("rnorm", fun = fun, reg = tmp)
# add second problem
fun = function(job, data, n, lambda, ...) rexp(n, rate = lambda)
addProblem("rexp", fun = fun, reg = tmp)
# add first algorithm
fun = function(instance, method, ...) if (method == "mean") mean(instance) else median(instance)
addAlgorithm("average", fun = fun, reg = tmp)
# add second algorithm
fun = function(instance, ...) sd(instance)
addAlgorithm("deviation", fun = fun, reg = tmp)
# define problem and algorithm designs
library(data.table)
prob.designs = algo.designs = list()
prob.designs$rnorm = CJ(n = 100, mean = -1:1, sd = 1:5)
prob.designs$rexp = data.table(n = 100, lambda = 1:5)
algo.designs$average = data.table(method = c("mean", "median"))
algo.designs$deviation = data.table()
# add experiments and submit
addExperiments(prob.designs, algo.designs, reg = tmp)
# check what has been created
summarizeExperiments(reg = tmp)
unwrap(getJobPars(reg = tmp))

Define Problems for Experiments

Description

Problems may consist of up to two parts: A static, immutable part (data in addProblem) and a dynamic, stochastic part (fun in addProblem). For example, for statistical learning problems a data frame would be the static problem part while a resampling function would be the stochastic part which creates problem instance. This instance is then typically passed to a learning algorithm like a wrapper around a statistical model (fun in addAlgorithm ).

This function serialize all components to the file system and registers the problem in the ExperimentRegistry .

removeProblem removes all jobs from the registry which depend on the specific problem. reg$problems holds the IDs of already defined problems.

Usage

addProblem(
 name,
 data = NULL,
 fun = NULL,
 seed = NULL,
 cache = FALSE,
 reg = getDefaultRegistry()
)
removeProblems(name, reg = getDefaultRegistry())

Arguments

Value

[Problem]. Object of class “Problem” (invisibly).

See Also

Algorithm , addExperiments

Examples

tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
addProblem("p1", fun = function(job, data) data, reg = tmp)
addProblem("p2", fun = function(job, data) job, reg = tmp)
addAlgorithm("a1", fun = function(job, data, instance) instance, reg = tmp)
addExperiments(repls = 2, reg = tmp)
# List problems, algorithms and job parameters:
tmp$problems
tmp$algorithms
getJobPars(reg = tmp)
# Remove one problem
removeProblems("p1", reg = tmp)
# List problems and algorithms:
tmp$problems
tmp$algorithms
getJobPars(reg = tmp)

assertRegistry

Description

Assert that a given object is a batchtools registry. Additionally can sync the registry, check if it is writeable, or check if jobs are running. If any check fails, throws an error indicting the reason for the failure.

Usage

assertRegistry(
 reg,
 class = NULL,
 writeable = FALSE,
 sync = FALSE,
 running.ok = TRUE
)

Arguments

Value

TRUE invisibly.

Export Objects to the Slaves

Description

Objects are saved in subdirectory “exports” of the “file.dir” of reg. They are automatically loaded and placed in the global environment each time the registry is loaded or a job collection is executed.

Usage

batchExport(
 export = list(),
 unexport = character(0L),
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table] with name and uri to the exported objects.

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
# list exports
exports = batchExport(reg = tmp)
print(exports)
# add a job and required exports
batchMap(function(x) x^2 + y + z, x = 1:3, reg = tmp)
exports = batchExport(export = list(y = 99, z = 1), reg = tmp)
print(exports)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
stopifnot(loadResult(1, reg = tmp) == 101)
# Un-export z
exports = batchExport(unexport = "z", reg = tmp)
print(exports)

Map Operation for Batch Systems

Description

A parallel and asynchronous Map /mapply for batch systems. Note that this function only defines the computational jobs. The actual computation is started with submitJobs . Results and partial results can be collected with reduceResultsList , reduceResults or loadResult .

For a synchronous Map -like execution, see btmapply .

Usage

batchMap(
 fun,
 ...,
 args = list(),
 more.args = list(),
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] with ids of added jobs stored in column “job.id”.

See Also

batchReduce

Examples

# example using "..." and more.args
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
f = function(x, y) x^2 + y
ids = batchMap(f, x = 1:10, more.args = list(y = 100), reg = tmp)
getJobPars(reg = tmp)
testJob(6, reg = tmp) # 100 + 6^2 = 136
# vector recycling
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
f = function(...) list(...)
ids = batchMap(f, x = 1:3, y = 1:6, reg = tmp)
getJobPars(reg = tmp)
# example for an expand.grid()-like operation on parameters
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
ids = batchMap(paste, args = data.table::CJ(x = letters[1:3], y = 1:3), reg = tmp)
getJobPars(reg = tmp)
testJob(6, reg = tmp)

Map Over Results to Create New Jobs

Description

This function allows you to create new computational jobs (just like batchMap based on the results of a Registry .

Usage

batchMapResults(
 fun,
 ids = NULL,
 ...,
 more.args = list(),
 target,
 source = getDefaultRegistry()
)

Arguments

Value

[data.table ] with ids of jobs added to target.

Note

The URI to the result files in registry source is hard coded as parameter in the target registry. This means that target is currently not portable between systems for computation.

See Also

Other Results: loadResult(), reduceResults(), reduceResultsList()

Examples

# Source registry: calculate square of some numbers
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(function(x) list(square = x^2), x = 1:10, reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
# Target registry: calculate the square root on results of first registry
target = makeRegistry(file.dir = NA, make.default = FALSE)
batchMapResults(fun = function(x, y) list(sqrt = sqrt(x$square)), ids = 4:8,
 target = target, source = tmp)
submitJobs(reg = target)
waitForJobs(reg = target)
# Map old to new ids. First, get a table with results and parameters
results = unwrap(rjoin(getJobPars(reg = target), reduceResultsDataTable(reg = target)))
print(results)
# Parameter '.id' points to job.id in 'source'. Use a inner join to combine:
ijoin(results, unwrap(reduceResultsDataTable(reg = tmp)), by = c(".id" = "job.id"))

Reduce Operation for Batch Systems

Description

A parallel and asynchronous Reduce for batch systems. Note that this function only defines the computational jobs. Each job reduces a certain number of elements on one slave. The actual computation is started with submitJobs . Results and partial results can be collected with reduceResultsList , reduceResults or loadResult .

Usage

batchReduce(
 fun,
 xs,
 init = NULL,
 chunks = seq_along(xs),
 more.args = list(),
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] with ids of added jobs stored in column “job.id”.

See Also

batchMap

Examples

# define function to reduce on slave, we want to sum a vector
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
xs = 1:100
f = function(aggr, x) aggr + x
# sum 20 numbers on each slave process, i.e. 5 jobs
chunks = chunk(xs, chunk.size = 5)
batchReduce(fun = f, 1:100, init = 0, chunks = chunks, reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
# now reduce one final time on master
reduceResults(fun = function(aggr, job, res) f(aggr, res), reg = tmp)

Synchronous Apply Functions

Description

This is a set of functions acting as counterparts to the sequential popular apply functions in base R: btlapply for lapply and btmapply for mapply .

Internally, jobs are created using batchMap on the provided registry. If no registry is provided, a temporary registry (see argument file.dir of makeRegistry ) and batchMap will be used. After all jobs are terminated (see waitForJobs ), the results are collected and returned as a list.

Note that these functions are only suitable for short and fail-safe operations on batch system. If some jobs fail, you have to retrieve partial results from the registry directory yourself.

Usage

btlapply(
 X,
 fun,
 ...,
 resources = list(),
 n.chunks = NULL,
 chunk.size = NULL,
 reg = makeRegistry(file.dir = NA)
)
btmapply(
 fun,
 ...,
 more.args = list(),
 simplify = FALSE,
 use.names = TRUE,
 resources = list(),
 n.chunks = NULL,
 chunk.size = NULL,
 reg = makeRegistry(file.dir = NA)
)

Arguments

Value

[list] List with the results of the function call.

Examples

btlapply(1:3, function(x) x^2)
btmapply(function(x, y, z) x + y + z, x = 1:3, y = 1:3, more.args = list(z = 1), simplify = TRUE)

Cluster Functions Helper to Write Job Description Files

Description

This function is only intended for use in your own cluster functions implementation.

Calls brew silently on your template, any error will lead to an exception. The file is stored at the same place as the corresponding job file in the “jobs”-subdir of your files directory.

Usage

cfBrewTemplate(reg, text, jc)

Arguments

Value

[character(1)]. File path to brewed template file.

See Also

Other ClusterFunctionsHelper: cfHandleUnknownSubmitError(), cfKillJob(), cfReadBrewTemplate(), makeClusterFunctions(), makeSubmitJobResult(), runOSCommand()

Cluster Functions Helper to Handle Unknown Errors

Description

This function is only intended for use in your own cluster functions implementation.

Simply constructs a SubmitJobResult object with status code 101, NA as batch id and an informative error message containing the output of the OS command in output.

Usage

cfHandleUnknownSubmitError(cmd, exit.code, output)

Arguments

Value

[SubmitJobResult ].

See Also

Other ClusterFunctionsHelper: cfBrewTemplate(), cfKillJob(), cfReadBrewTemplate(), makeClusterFunctions(), makeSubmitJobResult(), runOSCommand()

Cluster Functions Helper to Kill Batch Jobs

Description

This function is only intended for use in your own cluster functions implementation.

Calls the OS command to kill a job via system like this: “cmd batch.job.id”. If the command returns an exit code > 0, the command is repeated after a 1 second sleep max.tries-1 times. If the command failed in all tries, an error is generated.

Usage

cfKillJob(
 reg,
 cmd,
 args = character(0L),
 max.tries = 3L,
 nodename = "localhost"
)

Arguments

Value

TRUE on success. An exception is raised otherwise.

See Also

Other ClusterFunctionsHelper: cfBrewTemplate(), cfHandleUnknownSubmitError(), cfReadBrewTemplate(), makeClusterFunctions(), makeSubmitJobResult(), runOSCommand()

Cluster Functions Helper to Parse a Brew Template

Description

This function is only intended for use in your own cluster functions implementation.

This function is only intended for use in your own cluster functions implementation. Simply reads your template file and returns it as a character vector.

Usage

cfReadBrewTemplate(template, comment.string = NA_character_)

Arguments

Value

[character].

See Also

Other ClusterFunctionsHelper: cfBrewTemplate(), cfHandleUnknownSubmitError(), cfKillJob(), makeClusterFunctions(), makeSubmitJobResult(), runOSCommand()

Chunk Jobs for Sequential Execution

Description

Jobs can be partitioned into “chunks” to be executed sequentially on the computational nodes. Chunks are defined by providing a data frame with columns “job.id” and “chunk” (integer) to submitJobs . All jobs with the same chunk number will be grouped together on one node to form a single computational job.

The function chunk simply splits x into either a fixed number of groups, or into a variable number of groups with a fixed number of maximum elements.

The function lpt also groups x into a fixed number of chunks, but uses the actual values of x in a greedy “Longest Processing Time” algorithm. As a result, the maximum sum of elements in minimized.

binpack splits x into a variable number of groups whose sum of elements do not exceed the upper limit provided by chunk.size.

See examples of estimateRuntimes for an application of binpack and lpt.

Usage

chunk(x, n.chunks = NULL, chunk.size = NULL, shuffle = TRUE)
lpt(x, n.chunks = 1L)
binpack(x, chunk.size = max(x))

Arguments

Value

[integer] giving the chunk number for each element of x.

See Also

estimateRuntimes

Examples

ch = chunk(1:10, n.chunks = 2)
table(ch)
ch = chunk(rep(1, 10), chunk.size = 2)
table(ch)
set.seed(1)
x = runif(10)
ch = lpt(x, n.chunks = 2)
sapply(split(x, ch), sum)
set.seed(1)
x = runif(10)
ch = binpack(x, 1)
sapply(split(x, ch), sum)
# Job chunking
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
ids = batchMap(identity, 1:25, reg = tmp)
### Group into chunks with 10 jobs each
library(data.table)
ids[, chunk := chunk(job.id, chunk.size = 10)]
print(ids[, .N, by = chunk])
### Group into 4 chunks
ids[, chunk := chunk(job.id, n.chunks = 4)]
print(ids[, .N, by = chunk])
### Submit to batch system
submitJobs(ids = ids, reg = tmp)
# Grouped chunking
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
prob = addProblem(reg = tmp, "prob1", data = iris, fun = function(job, data) nrow(data))
prob = addProblem(reg = tmp, "prob2", data = Titanic, fun = function(job, data) nrow(data))
algo = addAlgorithm(reg = tmp, "algo", fun = function(job, data, instance, i, ...) problem)
prob.designs = list(prob1 = data.table(), prob2 = data.table(x = 1:2))
algo.designs = list(algo = data.table(i = 1:3))
addExperiments(prob.designs, algo.designs, repls = 3, reg = tmp)
### Group into chunks of 5 jobs, but do not put multiple problems into the same chunk
# -> only one problem has to be loaded per chunk, and only once because it is cached
ids = getJobTable(reg = tmp)[, .(job.id, problem, algorithm)]
ids[, chunk := chunk(job.id, chunk.size = 5), by = "problem"]
ids[, chunk := .GRP, by = c("problem", "chunk")]
dcast(ids, chunk ~ problem)

Remove All Jobs

Description

Removes all jobs from a registry and calls sweepRegistry .

Usage

clearRegistry(reg = getDefaultRegistry())

Arguments

See Also

Other Registry: getDefaultRegistry(), loadRegistry(), makeRegistry(), removeRegistry(), saveRegistry(), sweepRegistry(), syncRegistry()

Execute Jobs of a JobCollection

Description

Executes every job in a JobCollection . This function is intended to be called on the slave.

Usage

doJobCollection(jc, output = NULL)

Arguments

Value

[character(1)]: Hash of the JobCollection executed.

See Also

Other JobCollection: makeJobCollection()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(identity, 1:2, reg = tmp)
jc = makeJobCollection(1:2, reg = tmp)
doJobCollection(jc)

Estimate Remaining Runtimes

Description

Estimates the runtimes of jobs using the random forest implemented in ranger. Observed runtimes are retrieved from the Registry and runtimes are predicted for unfinished jobs.

The estimated remaining time is calculated in the print method. You may also pass n here to determine the number of parallel jobs which is then used in a simple Longest Processing Time (LPT) algorithm to give an estimate for the parallel runtime.

Usage

estimateRuntimes(tab, ..., reg = getDefaultRegistry())
## S3 method for class 'RuntimeEstimate'
print(x, n = 1L, ...)

Arguments

Value

[RuntimeEstimate] which is a list with two named elements: “runtimes” is a data.table with columns “job.id”, “runtime” (in seconds) and “type” (“estimated” if runtime is estimated, “observed” if runtime was observed). The other element of the list named “model”] contains the fitted random forest object.

See Also

binpack and lpt to chunk jobs according to their estimated runtimes.

Examples

# Create a simple toy registry
set.seed(1)
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE, seed = 1)
addProblem(name = "iris", data = iris, fun = function(data, ...) nrow(data), reg = tmp)
addAlgorithm(name = "nrow", function(instance, ...) nrow(instance), reg = tmp)
addAlgorithm(name = "ncol", function(instance, ...) ncol(instance), reg = tmp)
addExperiments(algo.designs = list(nrow = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
addExperiments(algo.designs = list(ncol = data.table::CJ(x = 1:50, y = letters[1:5])), reg = tmp)
# We use the job parameters to predict runtimes
tab = unwrap(getJobPars(reg = tmp))
# First we need to submit some jobs so that the forest can train on some data.
# Thus, we just sample some jobs from the registry while grouping by factor variables.
library(data.table)
ids = tab[, .SD[sample(nrow(.SD), 5)], by = c("problem", "algorithm", "y")]
setkeyv(ids, "job.id")
submitJobs(ids, reg = tmp)
waitForJobs(reg = tmp)
# We "simulate" some more realistic runtimes here to demonstrate the functionality:
# - Algorithm "ncol" is 5 times more expensive than "nrow"
# - x has no effect on the runtime
# - If y is "a" or "b", the runtimes are really high
runtime = function(algorithm, x, y) {
 ifelse(algorithm == "nrow", 100L, 500L) + 1000L * (y %in% letters[1:2])
}
tmp$status[ids, done := done + tab[ids, runtime(algorithm, x, y)]]
rjoin(sjoin(tab, ids), getJobStatus(ids, reg = tmp)[, c("job.id", "time.running")])
# Estimate runtimes:
est = estimateRuntimes(tab, reg = tmp)
print(est)
rjoin(tab, est$runtimes)
print(est, n = 10)
# Submit jobs with longest runtime first:
ids = est$runtimes[type == "estimated"][order(runtime, decreasing = TRUE)]
print(ids)
## Not run: 
submitJobs(ids, reg = tmp)
## End(Not run)
# Group jobs into chunks with runtime < 1h
ids = est$runtimes[type == "estimated"]
ids[, chunk := binpack(runtime, 3600)]
print(ids)
print(ids[, list(runtime = sum(runtime)), by = chunk])
## Not run: 
submitJobs(ids, reg = tmp)
## End(Not run)
# Group jobs into 10 chunks with similar runtime
ids = est$runtimes[type == "estimated"]
ids[, chunk := lpt(runtime, 10)]
print(ids[, list(runtime = sum(runtime)), by = chunk])

Execute a Single Jobs

Description

Executes a single job (as created by makeJob ) and returns its result. Also works for Experiments.

Usage

execJob(job)

Arguments

Value

Result of the job.

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(identity, 1:2, reg = tmp)
job = makeJob(1, reg = tmp)
execJob(job)

Find a batchtools Configuration File

Description

This functions returns the path to the first configuration file found in the following locations:

1. File “batchtools.conf.R” in the path specified by the environment variable “R_BATCHTOOLS_SEARCH_PATH”.

2. File “batchtools.conf.R” in the current working directory.

3. File “config.R” in the user configuration directory as reported by rappdirs::user_config_dir("batchtools", expand = FALSE) (depending on OS, e.g., on linux this usually resolves to “~/.config/batchtools/config.R”).

4. “.batchtools.conf.R” in the home directory (“~”).

5. “config.R” in the site config directory as reported by rappdirs::site_config_dir("batchtools") (depending on OS). This file can be used for admins to set sane defaults for a computation site.

Usage

findConfFile()

Value

[character(1)] Path to the configuration file or NA if no configuration file was found.

Find and Filter Jobs

Description

These functions are used to find and filter jobs, depending on either their parameters (findJobs and findExperiments), their tags (findTagged), or their computational status (all other functions, see getStatus for an overview).

Note that findQueued, findRunning, findOnSystem and findExpired are somewhat heuristic and may report misleading results, depending on the state of the system and the ClusterFunctions implementation.

See JoinTables for convenient set operations (unions, intersects, differences) on tables with job ids.

Usage

findJobs(expr, ids = NULL, reg = getDefaultRegistry())
findExperiments(
 ids = NULL,
 prob.name = NA_character_,
 prob.pattern = NA_character_,
 algo.name = NA_character_,
 algo.pattern = NA_character_,
 prob.pars,
 algo.pars,
 repls = NULL,
 reg = getDefaultRegistry()
)
findSubmitted(ids = NULL, reg = getDefaultRegistry())
findNotSubmitted(ids = NULL, reg = getDefaultRegistry())
findStarted(ids = NULL, reg = getDefaultRegistry())
findNotStarted(ids = NULL, reg = getDefaultRegistry())
findDone(ids = NULL, reg = getDefaultRegistry())
findNotDone(ids = NULL, reg = getDefaultRegistry())
findErrors(ids = NULL, reg = getDefaultRegistry())
findOnSystem(ids = NULL, reg = getDefaultRegistry())
findRunning(ids = NULL, reg = getDefaultRegistry())
findQueued(ids = NULL, reg = getDefaultRegistry())
findExpired(ids = NULL, reg = getDefaultRegistry())
findTagged(tags = character(0L), ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] with column “job.id” containing matched jobs.

See Also

getStatus JoinTables

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(identity, i = 1:3, reg = tmp)
ids = findNotSubmitted(reg = tmp)
# get all jobs:
findJobs(reg = tmp)
# filter for jobs with parameter i >= 2
findJobs(i >= 2, reg = tmp)
# filter on the computational status
findSubmitted(reg = tmp)
findNotDone(reg = tmp)
# filter on tags
addJobTags(2:3, "my_tag", reg = tmp)
findTagged(tags = "my_tag", reg = tmp)
# combine filter functions using joins
# -> jobs which are not done and not tagged (using an anti-join):
ajoin(findNotDone(reg = tmp), findTagged("my_tag", reg = tmp))

Find a batchtools Template File

Description

This functions returns the path to a template file on the file system.

Usage

findTemplateFile(template)

Arguments

Value

[character] Path to the file or NA if no template template file was found.

Get and Set the Default Registry

Description

getDefaultRegistry returns the registry currently set as default (or stops with an exception if none is set). setDefaultRegistry sets a registry as default.

Usage

getDefaultRegistry()
setDefaultRegistry(reg)

Arguments

See Also

Other Registry: clearRegistry(), loadRegistry(), makeRegistry(), removeRegistry(), saveRegistry(), sweepRegistry(), syncRegistry()

Retrieve Error Messages

Description

Extracts error messages from the internal data base and returns them in a table.

Usage

getErrorMessages(
 ids = NULL,
 missing.as.error = FALSE,
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] with columns “job.id”, “terminated” (logical), “error” (logical) and “message” (string).

See Also

Other debug: getStatus(), grepLogs(), killJobs(), resetJobs(), showLog(), testJob()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
fun = function(i) if (i == 3) stop(i) else i
ids = batchMap(fun, i = 1:5, reg = tmp)
submitJobs(1:4, reg = tmp)
waitForJobs(1:4, reg = tmp)
getErrorMessages(ids, reg = tmp)
getErrorMessages(ids, missing.as.error = TRUE, reg = tmp)

Query Job Information

Description

getJobStatus returns the internal table which stores information about the computational status of jobs, getJobPars a table with the job parameters, getJobResources a table with the resources which were set to submit the jobs, and getJobTags the tags of the jobs (see Tags).

getJobTable returns all these tables joined.

Usage

getJobTable(ids = NULL, reg = getDefaultRegistry())
getJobStatus(ids = NULL, reg = getDefaultRegistry())
getJobResources(ids = NULL, reg = getDefaultRegistry())
getJobPars(ids = NULL, reg = getDefaultRegistry())
getJobTags(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] with the following columns (not necessarily in this order):

job.id

Unique Job ID as integer.

submitted

Time the job was submitted to the batch system as POSIXct .

started

Time the job was started on the batch system as POSIXct .

done

Time the job terminated (successfully or with an error) as POSIXct .

error

Either NA if the job terminated successfully or the error message.

mem.used

Estimate of the memory usage.

batch.id

Batch ID as reported by the scheduler.

log.file

Log file. If missing, defaults to [job.hash].log.

job.hash

Unique string identifying the job or chunk.

time.queued

Time in seconds (as difftime ) the job was queued.

time.running

Time in seconds (as difftime ) the job was running.

pars

List of parameters/arguments for this job.

resources

List of computational resources set for this job.

tags

Tags as joined string, delimited by “,”.

problem

Only for ExperimentRegistry : the problem identifier.

algorithm

Only for ExperimentRegistry : the algorithm identifier.

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
f = function(x) if (x < 0) stop("x must be > 0") else sqrt(x)
batchMap(f, x = c(-1, 0, 1), reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
addJobTags(1:2, "tag1", reg = tmp)
addJobTags(2, "tag2", reg = tmp)
# Complete table:
getJobTable(reg = tmp)
# Job parameters:
getJobPars(reg = tmp)
# Set and retrieve tags:
getJobTags(reg = tmp)
# Job parameters with tags right-joined:
rjoin(getJobPars(reg = tmp), getJobTags(reg = tmp))

Summarize the Computational Status

Description

This function gives an encompassing overview over the computational status on your system. The status can be one or many of the following:

• “defined”: Jobs which are defined via batchMap or addExperiments , but are not yet submitted.

• “submitted”: Jobs which are submitted to the batch system via submitJobs , scheduled for execution.

• “started”: Jobs which have been started.

• “done”: Jobs which terminated successfully.

• “error”: Jobs which terminated with an exception.

• “running”: Jobs which are listed by the cluster functions to be running on the live system. Not supported for all cluster functions.

• “queued”: Jobs which are listed by the cluster functions to be queued on the live system. Not supported for all cluster functions.

• “system”: Jobs which are listed by the cluster functions to be queued or running. Not supported for all cluster functions.

• “expired”: Jobs which have been submitted, but vanished from the live system. Note that this is determined heuristically and may include some false positives.

Here, a job which terminated successfully counts towards the jobs which are submitted, started and done. To retrieve the corresponding job ids, see findJobs .

Usage

getStatus(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] (with class “Status” for printing).

See Also

findJobs

Other debug: getErrorMessages(), grepLogs(), killJobs(), resetJobs(), showLog(), testJob()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
fun = function(i) if (i == 3) stop(i) else i
ids = batchMap(fun, i = 1:5, reg = tmp)
submitJobs(ids = 1:4, reg = tmp)
waitForJobs(reg = tmp)
tab = getStatus(reg = tmp)
print(tab)
str(tab)

Grep Log Files for a Pattern

Description

Crawls through log files and reports jobs with lines matching the pattern. See showLog for an example.

Usage

grepLogs(
 ids = NULL,
 pattern,
 ignore.case = FALSE,
 fixed = FALSE,
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] with columns “job.id” and “message”.

See Also

Other debug: getErrorMessages(), getStatus(), killJobs(), resetJobs(), showLog(), testJob()

Kill Jobs

Description

Kill jobs which are currently running on the batch system.

In case of an error when killing, the function tries - after a short sleep - to kill the remaining batch jobs again. If this fails three times for some jobs, the function gives up. Jobs that could be successfully killed are reset in the Registry.

Usage

killJobs(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] with columns “job.id”, the corresponding “batch.id” and the logical flag “killed” indicating success.

See Also

Other debug: getErrorMessages(), getStatus(), grepLogs(), resetJobs(), showLog(), testJob()

Load a Registry from the File System

Description

Loads a registry from its file.dir.

Multiple R sessions accessing the same registry simultaneously can lead to database inconsistencies. This is especially dangerous if the same file.dir is accessed from multiple machines, e.g. via a mount.

If you just need to check on the status or peek into some preliminary results while another process is still submitting or waiting for pending results, you can load the registry in a read-only mode. All operations that need to change the registry will raise an exception in this mode. Files communicated back by the computational nodes are parsed to update the registry in memory while the registry on the file system remains unchanged.

A heuristic tries to detect if the registry has been altered in the background by an other process and in this case automatically restricts the current registry to read-only mode. However, you should rely on this heuristic to work flawlessly. Thus, set to writeable to TRUE if and only if you are absolutely sure that other state-changing processes are terminated.

If you need write access, load the registry with writeable set to TRUE.

Usage

loadRegistry(
 file.dir,
 work.dir = NULL,
 conf.file = findConfFile(),
 make.default = TRUE,
 writeable = FALSE
)

Arguments

Value

[Registry ].

See Also

Other Registry: clearRegistry(), getDefaultRegistry(), makeRegistry(), removeRegistry(), saveRegistry(), sweepRegistry(), syncRegistry()

Load the Result of a Single Job

Description

Loads the result of a single job.

Usage

loadResult(id, reg = getDefaultRegistry())

Arguments

Value

[ANY]. The stored result.

See Also

Other Results: batchMapResults(), reduceResults(), reduceResultsList()

ClusterFunctions Constructor

Description

This is the constructor used to create custom cluster functions. Note that some standard implementations for TORQUE, Slurm, LSF, SGE, etc. ship with the package.

Usage

makeClusterFunctions(
 name,
 submitJob,
 killJob = NULL,
 listJobsQueued = NULL,
 listJobsRunning = NULL,
 array.var = NA_character_,
 store.job.collection = FALSE,
 store.job.files = FALSE,
 scheduler.latency = 0,
 fs.latency = 0,
 hooks = list()
)

Arguments

See Also

Other ClusterFunctions: makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

Other ClusterFunctionsHelper: cfBrewTemplate(), cfHandleUnknownSubmitError(), cfKillJob(), cfReadBrewTemplate(), makeSubmitJobResult(), runOSCommand()

ClusterFunctions for Docker

Description

Cluster functions for Docker/Docker Swarm (https://docs.docker.com/engine/swarm/).

The submitJob function executes docker [docker.args] run --detach=true [image.args] [resources] [image] [cmd]. Arguments docker.args, image.args and image can be set on construction. The resources part takes the named resources ncpus and memory from submitJobs and maps them to the arguments --cpu-shares and --memory (in Megabytes). The resource threads is mapped to the environment variables “OMP_NUM_THREADS” and “OPENBLAS_NUM_THREADS”. To reliably identify jobs in the swarm, jobs are labeled with “batchtools=[job.hash]” and named using the current login name (label “user”) and the job hash (label “batchtools”).

listJobsRunning uses docker [docker.args] ps --format={{.ID}} to filter for running jobs.

killJobs uses docker [docker.args] kill [batch.id] to filter for running jobs.

These cluster functions use a Hook to remove finished jobs before a new submit and every time the Registry is synchronized (using syncRegistry ). This is currently required because docker does not remove terminated containers automatically. Use docker ps -a --filter 'label=batchtools' --filter 'status=exited' to identify and remove terminated containers manually (or usa a cron job).

Usage

makeClusterFunctionsDocker(
 image,
 docker.args = character(0L),
 image.args = character(0L),
 scheduler.latency = 1,
 fs.latency = 65
)

Arguments

Value

[ClusterFunctions ].

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for Sequential Execution in the Running R Session

Description

All jobs are executed sequentially using the current R process in which submitJobs is called. Thus, submitJob blocks the session until the job has finished. The main use of this ClusterFunctions implementation is to test and debug programs on a local computer.

Listing jobs returns an empty vector (as no jobs can be running when you call this) and killJob is not implemented for the same reasons.

Usage

makeClusterFunctionsInteractive(
 external = FALSE,
 write.logs = TRUE,
 fs.latency = 0
)

Arguments

Value

[ClusterFunctions ].

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for LSF Systems

Description

Cluster functions for LSF (https://www.ibm.com/products/hpc-workload-management).

Job files are created based on the brew template template.file. This file is processed with brew and then submitted to the queue using the bsub command. Jobs are killed using the bkill command and the list of running jobs is retrieved using bjobs -u $USER -w. The user must have the appropriate privileges to submit, delete and list jobs on the cluster (this is usually the case).

The template file can access all resources passed to submitJobs as well as all variables stored in the JobCollection . It is the template file's job to choose a queue for the job and handle the desired resource allocations.

Usage

makeClusterFunctionsLSF(
 template = "lsf",
 scheduler.latency = 1,
 fs.latency = 65
)

Arguments

Value

[ClusterFunctions ].

Note

Array jobs are currently not supported.

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for Parallel Multicore Execution

Description

Jobs are spawned asynchronously using the functions mcparallel and mccollect (both in parallel). Does not work on Windows, use makeClusterFunctionsSocket instead.

Usage

makeClusterFunctionsMulticore(ncpus = NA_integer_, fs.latency = 0)

Arguments

Value

[ClusterFunctions ].

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for OpenLava

Description

Cluster functions for OpenLava.

Job files are created based on the brew template template. This file is processed with brew and then submitted to the queue using the bsub command. Jobs are killed using the bkill command and the list of running jobs is retrieved using bjobs -u $USER -w. The user must have the appropriate privileges to submit, delete and list jobs on the cluster (this is usually the case).

The template file can access all resources passed to submitJobs as well as all variables stored in the JobCollection . It is the template file's job to choose a queue for the job and handle the desired resource allocations.

Usage

makeClusterFunctionsOpenLava(
 template = "openlava",
 scheduler.latency = 1,
 fs.latency = 65
)

Arguments

Value

[ClusterFunctions ].

Note

Array jobs are currently not supported.

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for SGE Systems

Description

Cluster functions for Univa Grid Engine / Oracle Grid Engine / Sun Grid Engine (https://altair.com/hpc-cloud-applications/).

Job files are created based on the brew template template. This file is processed with brew and then submitted to the queue using the qsub command. Jobs are killed using the qdel command and the list of running jobs is retrieved using qselect. The user must have the appropriate privileges to submit, delete and list jobs on the cluster (this is usually the case).

The template file can access all resources passed to submitJobs as well as all variables stored in the JobCollection . It is the template file's job to choose a queue for the job and handle the desired resource allocations.

Usage

makeClusterFunctionsSGE(
 template = "sge",
 nodename = "localhost",
 scheduler.latency = 1,
 fs.latency = 65
)

Arguments

Value

[ClusterFunctions ].

Note

Array jobs are currently not supported.

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for Remote SSH Execution

Description

Jobs are spawned by starting multiple R sessions via Rscript over SSH. If the hostname of the Worker equals “localhost”, Rscript is called directly so that you do not need to have an SSH client installed.

Usage

makeClusterFunctionsSSH(workers, fs.latency = 65)

Arguments

Value

[ClusterFunctions ].

Note

If you use a custom “.ssh/config” file, make sure your ProxyCommand passes ‘-q’ to ssh, otherwise each output will end with the message “Killed by signal 1” and this will break the communication with the nodes.

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

Examples

## Not run: 
# cluster functions for multicore execution on the local machine
makeClusterFunctionsSSH(list(Worker$new("localhost", ncpus = 2)))
## End(Not run)

ClusterFunctions for Slurm Systems

Description

Cluster functions for Slurm (https://slurm.schedmd.com/).

Job files are created based on the brew template template.file. This file is processed with brew and then submitted to the queue using the sbatch command. Jobs are killed using the scancel command and the list of running jobs is retrieved using squeue. The user must have the appropriate privileges to submit, delete and list jobs on the cluster (this is usually the case).

The template file can access all resources passed to submitJobs as well as all variables stored in the JobCollection . It is the template file's job to choose a queue for the job and handle the desired resource allocations.

Note that you might have to specify the cluster name here if you do not want to use the default, otherwise the commands for listing and killing jobs will not work.

Usage

makeClusterFunctionsSlurm(
 template = "slurm",
 array.jobs = TRUE,
 nodename = "localhost",
 scheduler.latency = 1,
 fs.latency = 65
)

Arguments

Value

[ClusterFunctions ].

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSocket(), makeClusterFunctionsTORQUE()

ClusterFunctions for Parallel Socket Execution

Description

Jobs are spawned asynchronously using the package snow.

Usage

makeClusterFunctionsSocket(ncpus = NA_integer_, fs.latency = 65)

Arguments

Value

[ClusterFunctions ].

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsTORQUE()

ClusterFunctions for OpenPBS/TORQUE Systems

Description

Cluster functions for TORQUE/PBS (https://adaptivecomputing.com/cherry-services/torque-resource-manager/).

Job files are created based on the brew template template.file. This file is processed with brew and then submitted to the queue using the qsub command. Jobs are killed using the qdel command and the list of running jobs is retrieved using qselect. The user must have the appropriate privileges to submit, delete and list jobs on the cluster (this is usually the case).

The template file can access all resources passed to submitJobs as well as all variables stored in the JobCollection . It is the template file's job to choose a queue for the job and handle the desired resource allocations.

Usage

makeClusterFunctionsTORQUE(
 template = "torque",
 scheduler.latency = 1,
 fs.latency = 65
)

Arguments

Value

[ClusterFunctions ].

See Also

Other ClusterFunctions: makeClusterFunctions(), makeClusterFunctionsDocker(), makeClusterFunctionsInteractive(), makeClusterFunctionsLSF(), makeClusterFunctionsMulticore(), makeClusterFunctionsOpenLava(), makeClusterFunctionsSGE(), makeClusterFunctionsSSH(), makeClusterFunctionsSlurm(), makeClusterFunctionsSocket()

ExperimentRegistry Constructor

Description

makeExperimentRegistry constructs a special Registry which is suitable for the definition of large scale computer experiments.

Each experiments consists of a Problem and an Algorithm . These can be parametrized with addExperiments to actually define computational jobs.

Usage

makeExperimentRegistry(
 file.dir = "registry",
 work.dir = getwd(),
 conf.file = findConfFile(),
 packages = character(0L),
 namespaces = character(0L),
 source = character(0L),
 load = character(0L),
 seed = NULL,
 make.default = TRUE
)

Arguments

Value

[ExperimentRegistry].

Examples

tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
# Definde one problem, two algorithms and add them with some parameters:
addProblem(reg = tmp, "p1",
 fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd))
addAlgorithm(reg = tmp, "a1", fun = function(job, data, instance, ...) mean(instance))
addAlgorithm(reg = tmp, "a2", fun = function(job, data, instance, ...) median(instance))
ids = addExperiments(reg = tmp, list(p1 = data.table::CJ(n = c(50, 100), mean = -2:2, sd = 1:4)))
# Overview over defined experiments:
tmp$problems
tmp$algorithms
summarizeExperiments(reg = tmp)
summarizeExperiments(reg = tmp, by = c("problem", "algorithm", "n"))
ids = findExperiments(prob.pars = (n == 50), reg = tmp)
print(unwrap(getJobPars(ids, reg = tmp)))
# Submit jobs
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
# Reduce the results of algorithm a1
ids.mean = findExperiments(algo.name = "a1", reg = tmp)
reduceResults(ids.mean, fun = function(aggr, res, ...) c(aggr, res), reg = tmp)
# Join info table with all results and calculate mean of results
# grouped by n and algorithm
ids = findDone(reg = tmp)
pars = unwrap(getJobPars(ids, reg = tmp))
results = unwrap(reduceResultsDataTable(ids, fun = function(res) list(res = res), reg = tmp))
tab = ljoin(pars, results)
tab[, list(mres = mean(res)), by = c("n", "algorithm")]

Jobs and Experiments

Description

Jobs and Experiments are abstract objects which hold all information necessary to execute a single computational job for a Registry or ExperimentRegistry , respectively.

They can be created using the constructor makeJob which takes a single job id. Jobs and Experiments are passed to reduce functions like reduceResults . Furthermore, Experiments can be used in the functions of the Problem and Algorithm . Jobs and Experiments hold these information:

job.id

Job ID as integer.

pars

Job parameters as named list. For ExperimentRegistry , the parameters are divided into the sublists “prob.pars” and “algo.pars”.

seed

Seed which is set via doJobCollection as scalar integer.

resources

Computational resources which were set for this job as named list.

external.dir

Path to a directory which is created exclusively for this job. You can store external files here. Directory is persistent between multiple restarts of the job and can be cleaned by calling resetJobs .

fun

Job only: User function passed to batchMap .

prob.name

Experiments only: Problem id.

algo.name

Experiments only: Algorithm id.

problem

Experiments only: Problem .

instance

Experiments only: Problem instance.

algorithm

Experiments only: Algorithm .

repl

Experiments only: Replication number.

Note that the slots “pars”, “fun”, “algorithm” and “problem” lazy-load required files from the file system and construct the object on the first access. The realizations are cached for all slots except “instance” (which might be stochastic).

Jobs and Experiments can be executed manually with execJob .

Usage

makeJob(id, reader = NULL, reg = getDefaultRegistry())

Arguments

Value

[Job | Experiment].

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(function(x, y) x + y, x = 1:2, more.args = list(y = 99), reg = tmp)
submitJobs(resources = list(foo = "bar"), reg = tmp)
job = makeJob(1, reg = tmp)
print(job)
# Get the parameters:
job$pars
# Get the job resources:
job$resources
# Execute the job locally:
execJob(job)

JobCollection Constructor

Description

makeJobCollection takes multiple job ids and creates an object of class “JobCollection” which holds all necessary information for the calculation with doJobCollection . It is implemented as an environment with the following variables:

file.dir

file.dir of the Registry.

work.dir:

work.dir of the Registry.

job.hash

Unique identifier of the job. Used to create names on the file system.

jobs

data.table holding individual job information. See examples.

log.file

Location of the designated log file for this job.

resources:

Named list of of specified computational resources.

uri

Location of the job description file (saved with link[base]{saveRDS} on the file system.

seed

integer(1) Seed of the Registry.

packages

character with required packages to load via require .

namespaces

character with required packages to load via requireNamespace .

source

character with list of files to source before execution.

load

character with list of files to load before execution.

array.var

character(1) of the array environment variable specified by the cluster functions.

array.jobs

logical(1) signaling if jobs were submitted using chunks.as.arrayjobs.

If your ClusterFunctions uses a template, brew will be executed in the environment of such a collection. Thus all variables available inside the job can be used in the template.

Usage

makeJobCollection(ids = NULL, resources = list(), reg = getDefaultRegistry())

Arguments

Value

[JobCollection].

See Also

Other JobCollection: doJobCollection()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE, packages = "methods")
batchMap(identity, 1:5, reg = tmp)
# resources are usually set in submitJobs()
jc = makeJobCollection(1:3, resources = list(foo = "bar"), reg = tmp)
ls(jc)
jc$resources

Registry Constructor

Description

makeRegistry constructs the inter-communication object for all functions in batchtools. All communication transactions are processed via the file system: All information required to run a job is stored as JobCollection in a file in the a subdirectory of the file.dir directory. Each jobs stores its results as well as computational status information (start time, end time, error message, ...) also on the file system which is regular merged parsed by the master using syncRegistry . After integrating the new information into the Registry, the Registry is serialized to the file system via saveRegistry . Both syncRegistry and saveRegistry are called whenever required internally. Therefore it should be safe to quit the R session at any time. Work can later be resumed by calling loadRegistry which de-serializes the registry from the file system.

The registry created last is saved in the package namespace (unless make.default is set to FALSE) and can be retrieved via getDefaultRegistry .

Canceled jobs and jobs submitted multiple times may leave stray files behind. These can be swept using sweepRegistry . clearRegistry completely erases all jobs from a registry, including log files and results, and thus allows you to start over.

Usage

makeRegistry(
 file.dir = "registry",
 work.dir = getwd(),
 conf.file = findConfFile(),
 packages = character(0L),
 namespaces = character(0L),
 source = character(0L),
 load = character(0L),
 seed = NULL,
 make.default = TRUE
)

Arguments

Details

Currently batchtools understands the following options set via the configuration file:

cluster.functions:

As returned by a constructor, e.g. makeClusterFunctionsSlurm .

default.resources:

List of resources to use. Will be overruled by resources specified via submitJobs .

temp.dir:

Path to directory to use for temporary registries.

sleep:

Custom sleep function. See waitForJobs .

expire.after:

Number of iterations before treating jobs as expired in waitForJobs .

compress:

Compression algorithm to use via saveRDS .

Value

[environment] of class “Registry” with the following slots:

file.dir [path]:

File directory.

work.dir [path]:

Working directory.

temp.dir [path]:

Temporary directory. Used if file.dir is NA to create temporary registries.

packages [character()]:

Packages to load on the slaves.

namespaces [character()]:

Namespaces to load on the slaves.

seed [integer(1)]:

Registry seed. Before each job is executed, the seed seed + job.id is set.

cluster.functions [cluster.functions]:

Usually set in your conf.file. Set via a call to makeClusterFunctions . See example.

default.resources [named list()]:

Usually set in your conf.file. Named list of default resources.

max.concurrent.jobs [integer(1)]:

Usually set in your conf.file. Maximum number of concurrent jobs for a single user and current registry on the system. submitJobs will try to respect this setting. The resource “max.concurrent.jobs” has higher precedence.

defs [data.table]:

Table with job definitions (i.e. parameters).

status [data.table]:

Table holding information about the computational status. Also see getJobStatus .

resources [data.table]:

Table holding information about the computational resources used for the job. Also see getJobResources .

tags [data.table]:

Table holding information about tags. See Tags.

hash [character(1)]:

Unique hash which changes each time the registry gets saved to the file system. Can be utilized to invalidate the cache of knitr.

See Also

Other Registry: clearRegistry(), getDefaultRegistry(), loadRegistry(), removeRegistry(), saveRegistry(), sweepRegistry(), syncRegistry()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
print(tmp)
# Set cluster functions to interactive mode and start jobs in external R sessions
tmp$cluster.functions = makeClusterFunctionsInteractive(external = TRUE)
# Change packages to load
tmp$packages = c("MASS")
saveRegistry(reg = tmp)

Create a SubmitJobResult

Description

This function is only intended for use in your own cluster functions implementation.

Use this function in your implementation of makeClusterFunctions to create a return value for the submitJob function.

Usage

makeSubmitJobResult(
 status,
 batch.id,
 log.file = NA_character_,
 msg = NA_character_
)

Arguments

Value

[SubmitJobResult ]. A list, containing status, batch.id and msg.

See Also

Other ClusterFunctionsHelper: cfBrewTemplate(), cfHandleUnknownSubmitError(), cfKillJob(), cfReadBrewTemplate(), makeClusterFunctions(), runOSCommand()

Reduce Results

Description

A version of Reduce for Registry objects which iterates over finished jobs and aggregates them. All jobs must have terminated, an error is raised otherwise.

Usage

reduceResults(fun, ids = NULL, init, ..., reg = getDefaultRegistry())

Arguments

Value

Aggregated results in the same order as provided ids. Return type depends on the user function. If ids is empty, reduceResults returns init (if available) or NULL otherwise.

Note

If you have thousands of jobs, disabling the progress bar (options(batchtools.progress = FALSE)) can significantly increase the performance.

See Also

Other Results: batchMapResults(), loadResult(), reduceResultsList()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(function(a, b) list(sum = a+b, prod = a*b), a = 1:3, b = 1:3, reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
# Extract element sum from each result
reduceResults(function(aggr, res) c(aggr, res$sum), init = list(), reg = tmp)
# Aggregate element sum via '+'
reduceResults(function(aggr, res) aggr + res$sum, init = 0, reg = tmp)
# Aggregate element prod via '*' where parameter b < 3
reduce = function(aggr, res, job) {
 if (job$pars$b >= 3)
 return(aggr)
 aggr * res$prod
}
reduceResults(reduce, init = 1, reg = tmp)
# Reduce to data.frame() (inefficient, use reduceResultsDataTable() instead)
reduceResults(rbind, init = data.frame(), reg = tmp)
# Reduce to data.frame by collecting results first, then utilize vectorization of rbind:
res = reduceResultsList(fun = as.data.frame, reg = tmp)
do.call(rbind, res)
# Reduce with custom combine function:
comb = function(x, y) list(sum = x$sum + y$sum, prod = x$prod * y$prod)
reduceResults(comb, reg = tmp)
# The same with neutral element NULL
comb = function(x, y) if (is.null(x)) y else list(sum = x$sum + y$sum, prod = x$prod * y$prod)
reduceResults(comb, init = NULL, reg = tmp)
# Alternative: Reduce in list, reduce manually in a 2nd step
res = reduceResultsList(reg = tmp)
Reduce(comb, res)

Apply Functions on Results

Description

Applies a function on the results of your finished jobs and thereby collects them in a list or data.table . The later requires the provided function to return a list (or data.frame) of scalar values. See rbindlist for features and limitations of the aggregation.

If not all jobs are terminated, the respective result will be NULL.

Usage

reduceResultsList(
 ids = NULL,
 fun = NULL,
 ...,
 missing.val,
 reg = getDefaultRegistry()
)
reduceResultsDataTable(
 ids = NULL,
 fun = NULL,
 ...,
 missing.val,
 reg = getDefaultRegistry()
)

Arguments

Value

reduceResultsList returns a list of the results in the same order as the provided ids. reduceResultsDataTable returns a data.table with columns “job.id” and additional result columns created via rbindlist , sorted by “job.id”.

Note

If you have thousands of jobs, disabling the progress bar (options(batchtools.progress = FALSE)) can significantly increase the performance.

See Also

reduceResults

Other Results: batchMapResults(), loadResult(), reduceResults()

Examples

### Example 1 - reduceResultsList
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(function(x) x^2, x = 1:10, reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
reduceResultsList(fun = sqrt, reg = tmp)
### Example 2 - reduceResultsDataTable
tmp = makeExperimentRegistry(file.dir = NA, make.default = FALSE)
# add first problem
fun = function(job, data, n, mean, sd, ...) rnorm(n, mean = mean, sd = sd)
addProblem("rnorm", fun = fun, reg = tmp)
# add second problem
fun = function(job, data, n, lambda, ...) rexp(n, rate = lambda)
addProblem("rexp", fun = fun, reg = tmp)
# add first algorithm
fun = function(instance, method, ...) if (method == "mean") mean(instance) else median(instance)
addAlgorithm("average", fun = fun, reg = tmp)
# add second algorithm
fun = function(instance, ...) sd(instance)
addAlgorithm("deviation", fun = fun, reg = tmp)
# define problem and algorithm designs
library(data.table)
prob.designs = algo.designs = list()
prob.designs$rnorm = CJ(n = 100, mean = -1:1, sd = 1:5)
prob.designs$rexp = data.table(n = 100, lambda = 1:5)
algo.designs$average = data.table(method = c("mean", "median"))
algo.designs$deviation = data.table()
# add experiments and submit
addExperiments(prob.designs, algo.designs, reg = tmp)
submitJobs(reg = tmp)
# collect results and join them with problem and algorithm paramters
res = ijoin(
 getJobPars(reg = tmp),
 reduceResultsDataTable(reg = tmp, fun = function(x) list(res = x))
)
unwrap(res, sep = ".")

Remove Experiments

Description

Remove Experiments from an ExperimentRegistry . This function automatically checks if any of the jobs to reset is either pending or running. However, if the implemented heuristic fails, this can lead to inconsistencies in the data base. Use with care while jobs are running.

Usage

removeExperiments(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] of removed job ids, invisibly.

See Also

Other Experiment: addExperiments(), summarizeExperiments()

Remove a Registry from the File System

Description

All files will be erased from the file system, including all results. If you wish to remove only intermediate files, use sweepRegistry .

Usage

removeRegistry(wait = 5, reg = getDefaultRegistry())

Arguments

Value

[character(1)]: Path of the deleted file directory.

See Also

Other Registry: clearRegistry(), getDefaultRegistry(), loadRegistry(), makeRegistry(), saveRegistry(), sweepRegistry(), syncRegistry()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
removeRegistry(0, tmp)

Reset the Computational State of Jobs

Description

Resets the computational state of jobs in the Registry . This function automatically checks if any of the jobs to reset is either pending or running. However, if the implemented heuristic fails, this can lead to inconsistencies in the data base. Use with care while jobs are running.

Usage

resetJobs(ids = NULL, reg = getDefaultRegistry())

Arguments

Value

[data.table ] of job ids which have been reset. See JoinTables for examples on working with job tables.

See Also

Other debug: getErrorMessages(), getStatus(), grepLogs(), killJobs(), showLog(), testJob()

Trigger Evaluation of Custom Function

Description

Hooks allow to trigger functions calls on specific events. They can be specified via the ClusterFunctions and are triggered on the following events:

pre.sync

function(reg, fns, ...): Run before synchronizing the registry on the master. fn is the character vector of paths to the update files.

post.sync

function(reg, updates, ...): Run after synchronizing the registry on the master. updates is the data.table of processed updates.

pre.submit.job

function(reg, ...): Run before a job is successfully submitted to the scheduler on the master.

post.submit.job

function(reg, ...): Run after a job is successfully submitted to the scheduler on the master.

pre.submit

function(reg, ...): Run before any job is submitted to the scheduler.

post.submit

function(reg, ...): Run after a jobs are submitted to the schedule.

pre.do.collection

function(reg, reader, ...): Run before starting the job collection on the slave. reader is an internal cache object.

post.do.collection

function(reg, updates, reader, ...): Run after all jobs in the chunk are terminated on the slave. updates is a data.table of updates which will be merged with the Registry by the master. reader is an internal cache object.

pre.kill

function(reg, ids, ...): Run before any job is killed.

post.kill

function(reg, ids, ...): Run after jobs are killed. ids is the return value of killJobs .

Usage

runHook(obj, hook, ...)

Arguments

Value

Return value of the called function, or NULL if there is no hook with the specified ID.

Run OS Commands on Local or Remote Machines

Description

This is a helper function to run arbitrary OS commands on local or remote machines. The interface is similar to system2 , but it always returns the exit status and the output.

Usage

runOSCommand(
 sys.cmd,
 sys.args = character(0L),
 stdin = "",
 nodename = "localhost"
)

Arguments

Value

[named list] with “sys.cmd”, “sys.args”, “exit.code” (integer), “output” (character).

See Also

Other ClusterFunctionsHelper: cfBrewTemplate(), cfHandleUnknownSubmitError(), cfKillJob(), cfReadBrewTemplate(), makeClusterFunctions(), makeSubmitJobResult()

Examples

## Not run: 
runOSCommand("ls")
runOSCommand("ls", "-al")
runOSCommand("notfound")
## End(Not run)

Store the Registy to the File System

Description

Stores the registry on the file system in its “file.dir” (specified for construction in makeRegistry , can be accessed via reg$file.dir). This function is usually called internally whenever needed.

Usage

saveRegistry(reg = getDefaultRegistry())

Arguments

Value

[logical(1)]: TRUE if the registry was saved, FALSE otherwise (if the registry is read-only).

See Also

Other Registry: clearRegistry(), getDefaultRegistry(), loadRegistry(), makeRegistry(), removeRegistry(), sweepRegistry(), syncRegistry()

Inspect Log Files

Description

showLog opens the log in the pager. For customization, see file.show . getLog returns the log as character vector.

Usage

showLog(id, reg = getDefaultRegistry())
getLog(id, reg = getDefaultRegistry())

Arguments

Value

Nothing.

See Also

Other debug: getErrorMessages(), getStatus(), grepLogs(), killJobs(), resetJobs(), testJob()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
# Create some dummy jobs
fun = function(i) {
 if (i == 3) stop(i)
 if (i %% 2 == 1) warning("That's odd.")
}
ids = batchMap(fun, i = 1:5, reg = tmp)
submitJobs(reg = tmp)
waitForJobs(reg = tmp)
getStatus(reg = tmp)
writeLines(getLog(ids[1], reg = tmp))
## Not run: 
showLog(ids[1], reg = tmp)
## End(Not run)
grepLogs(pattern = "warning", ignore.case = TRUE, reg = tmp)

Submit Jobs to the Batch Systems

Description

Submits defined jobs to the batch system.

After submitting the jobs, you can use waitForJobs to wait for the termination of jobs or call reduceResultsList /reduceResults to collect partial results. The progress can be monitored with getStatus .

Usage

submitJobs(
 ids = NULL,
 resources = list(),
 sleep = NULL,
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] with columns “job.id” and “chunk”.

Resources

You can pass arbitrary resources to submitJobs() which then are available in the cluster function template. Some resources' names are standardized and it is good practice to stick to the following nomenclature to avoid confusion:

walltime:

Upper time limit in seconds for jobs before they get killed by the scheduler. Can be passed as additional column as part of ids to set per-job resources.

memory:

Memory limit in Mb. If jobs exceed this limit, they are usually killed by the scheduler. Can be passed as additional column as part of ids to set per-job resources.

ncpus:

Number of (physical) CPUs to use on the slave. Can be passed as additional column as part of ids to set per-job resources.

omp.threads:

Number of threads to use via OpenMP. Used to set environment variable “OMP_NUM_THREADS”. Can be passed as additional column as part of ids to set per-job resources.

pp.size:

Maximum size of the pointer protection stack, see Memory .

blas.threads:

Number of threads to use for the BLAS backend. Used to set environment variables “MKL_NUM_THREADS” and “OPENBLAS_NUM_THREADS”. Can be passed as additional column as part of ids to set per-job resources.

measure.memory:

Enable memory measurement for jobs. Comes with a small runtime overhead.

chunks.as.arrayjobs:

Execute chunks as array jobs.

pm.backend:

Start a parallelMap backend on the slave.

foreach.backend:

Start a foreach backend on the slave.

clusters:

Resource used for Slurm to select the set of clusters to run sbatch/squeue/scancel on.

Chunking of Jobs

Multiple jobs can be grouped (chunked) together to be executed sequentially on the batch system as a single batch job. This is especially useful to avoid overburding the scheduler by submitting thousands of jobs simultaneously. To chunk jobs together, job ids must be provided as data.frame with columns “job.id” and “chunk” (integer). All jobs with the same chunk number will be executed sequentially inside the same batch job. The utility functions chunk , binpack and lpt can assist in grouping jobs.

Array Jobs

If your cluster supports array jobs, you can set the resource chunks.as.arrayjobs to TRUE in order to execute chunks as job arrays on the cluster. For each chunk of size n, batchtools creates a JobCollection of (possibly heterogeneous) jobs which is submitted to the scheduler as a single array job with n repetitions. For each repetition, the JobCollection is first read from the file system, then subsetted to the i-th job using the environment variable reg$cluster.functions$array.var (depending on the cluster backend, defined automatically) and finally executed.

Order of Submission

Jobs are submitted in the order of chunks, i.e. jobs which have chunk number sort(unique(ids$chunk))[1] first, then jobs with chunk number sort(unique(ids$chunk))[2] and so on. If no chunks are provided, jobs are submitted in the order of ids$job.id.

Limiting the Number of Jobs

If requested, submitJobs tries to limit the number of concurrent jobs of the user by waiting until jobs terminate before submitting new ones. This can be controlled by setting “max.concurrent.jobs” in the configuration file (see Registry ) or by setting the resource “max.concurrent.jobs” to the maximum number of jobs to run simultaneously. If both are set, the setting via the resource takes precedence over the setting in the configuration.

Measuring Memory

Setting the resource measure.memory to TRUE turns on memory measurement: gc is called directly before and after the job and the difference is stored in the internal database. Note that this is just a rough estimate and does neither work reliably for external code like C/C++ nor in combination with threading.

Inner Parallelization

Inner parallelization is typically done via threading, sockets or MPI. Two backends are supported to assist in setting up inner parallelization.

The first package is parallelMap. If you set the resource “pm.backend” to “multicore”, “socket” or “mpi”, parallelStart is called on the slave before the first job in the chunk is started and parallelStop is called after the last job terminated. This way, the resources for inner parallelization can be set and get automatically stored just like other computational resources. The function provided by the user just has to call parallelMap to start parallelization using the preconfigured backend.

To control the number of CPUs, you have to set the resource ncpus. Otherwise ncpus defaults to the number of available CPUs (as reported by (see detectCores )) on the executing machine for multicore and socket mode and defaults to the return value of mpi.universe.size-1 for MPI. Your template must be set up to handle the parallelization, e.g. request the right number of CPUs or start R with mpirun. You may pass further options like level to parallelStart via the named list “pm.opts”.

The second supported parallelization backend is foreach. If you set the resource “foreach.backend” to “seq” (sequential mode), “parallel” (doParallel) or “mpi” (doMPI), the requested foreach backend is automatically registered on the slave. Again, the resource ncpus is used to determine the number of CPUs.

Neither the namespace of parallelMap nor the namespace foreach are attached. You have to do this manually via library or let the registry load the packages for you.

Note

If you a large number of jobs, disabling the progress bar (options(batchtools.progress = FALSE)) can significantly increase the performance of submitJobs.

Examples

### Example 1: Submit subsets of jobs
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
# toy function which fails if x is even and an input file does not exists
fun = function(x, fn) if (x %% 2 == 0 && !file.exists(fn)) stop("file not found") else x
# define jobs via batchMap
fn = tempfile()
ids = batchMap(fun, 1:20, reg = tmp, fn = fn)
# submit some jobs
ids = 1:10
submitJobs(ids, reg = tmp)
waitForJobs(ids, reg = tmp)
getStatus(reg = tmp)
# create the required file and re-submit failed jobs
file.create(fn)
submitJobs(findErrors(ids, reg = tmp), reg = tmp)
getStatus(reg = tmp)
# submit remaining jobs which have not yet been submitted
ids = findNotSubmitted(reg = tmp)
submitJobs(ids, reg = tmp)
getStatus(reg = tmp)
# collect results
reduceResultsList(reg = tmp)
### Example 2: Using memory measurement
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
# Toy function which creates a large matrix and returns the column sums
fun = function(n, p) colMeans(matrix(runif(n*p), n, p))
# Arguments to fun:
args = data.table::CJ(n = c(1e4, 1e5), p = c(10, 50)) # like expand.grid()
print(args)
# Map function to create jobs
ids = batchMap(fun, args = args, reg = tmp)
# Set resources: enable memory measurement
res = list(measure.memory = TRUE)
# Submit jobs using the currently configured cluster functions
submitJobs(ids, resources = res, reg = tmp)
# Retrive information about memory, combine with parameters
info = ijoin(getJobStatus(reg = tmp)[, .(job.id, mem.used)], getJobPars(reg = tmp))
print(unwrap(info))
# Combine job info with results -> each job is aggregated using mean()
unwrap(ijoin(info, reduceResultsDataTable(fun = function(res) list(res = mean(res)), reg = tmp)))
### Example 3: Multicore execution on the slave
tmp = makeRegistry(file.dir = NA, make.default = FALSE)
# Function which sleeps 10 seconds, i-times
f = function(i) {
 parallelMap::parallelMap(Sys.sleep, rep(10, i))
}
# Create one job with parameter i=4
ids = batchMap(f, i = 4, reg = tmp)
# Set resources: Use parallelMap in multicore mode with 4 CPUs
# batchtools internally loads the namespace of parallelMap and then
# calls parallelStart() before the job and parallelStop() right
# after the job last job in the chunk terminated.
res = list(pm.backend = "multicore", ncpus = 4)
## Not run: 
# Submit both jobs and wait for them
submitJobs(resources = res, reg = tmp)
waitForJobs(reg = tmp)
# If successfull, the running time should be ~10s
getJobTable(reg = tmp)[, .(job.id, time.running)]
# There should also be a note in the log:
grepLogs(pattern = "parallelMap", reg = tmp)
## End(Not run)

Quick Summary over Experiments

Description

Returns a frequency table of defined experiments. See ExperimentRegistry for an example.

Usage

summarizeExperiments(
 ids = NULL,
 by = c("problem", "algorithm"),
 reg = getDefaultRegistry()
)

Arguments

Value

[data.table ] of frequencies.

See Also

Other Experiment: addExperiments(), removeExperiments()

Check Consistency and Remove Obsolete Information

Description

Canceled jobs and jobs submitted multiple times may leave stray files behind. This function checks the registry for consistency and removes obsolete files and redundant data base entries.

Usage

sweepRegistry(reg = getDefaultRegistry())

Arguments

See Also

Other Registry: clearRegistry(), getDefaultRegistry(), loadRegistry(), makeRegistry(), removeRegistry(), saveRegistry(), syncRegistry()

Synchronize the Registry

Description

Parses update files written by the slaves to the file system and updates the internal data base.

Usage

syncRegistry(reg = getDefaultRegistry())

Arguments

Value

[logical(1)]: TRUE if the state has changed, FALSE otherwise.

See Also

Other Registry: clearRegistry(), getDefaultRegistry(), loadRegistry(), makeRegistry(), removeRegistry(), saveRegistry(), sweepRegistry()

Run Jobs Interactively

Description

Starts a single job on the local machine.

Usage

testJob(id, external = FALSE, reg = getDefaultRegistry())

Arguments

Value

Returns the result of the job if successful.

See Also

Other debug: getErrorMessages(), getStatus(), grepLogs(), killJobs(), resetJobs(), showLog()

Examples

tmp = makeRegistry(file.dir = NA, make.default = FALSE)
batchMap(function(x) if (x == 2) xxx else x, 1:2, reg = tmp)
testJob(1, reg = tmp)
## Not run: 
testJob(2, reg = tmp)
## End(Not run)

Unwrap Nested Data Frames

Description

Some functions (e.g., getJobPars , getJobResources or reduceResultsDataTable return a data.table with columns of type list. These columns can be unnested/unwrapped with this function. The contents of these columns will be transformed to a data.table and cbind -ed to the input data.frame x, replacing the original nested column.

Usage

unwrap(x, cols = NULL, sep = NULL)
flatten(x, cols = NULL, sep = NULL)

Arguments

Value

[data.table ].

Note

There is a name clash with function flatten in package purrr. The function flatten is discouraged to use for this reason in favor of unwrap.

Examples

x = data.table::data.table(
 id = 1:3,
 values = list(list(a = 1, b = 3), list(a = 2, b = 2), list(a = 3))
)
unwrap(x)
unwrap(x, sep = ".")

Wait for Termination of Jobs

Description

This function simply waits until all jobs are terminated.

Usage

waitForJobs(
 ids = NULL,
 sleep = NULL,
 timeout = 604800,
 expire.after = NULL,
 stop.on.error = FALSE,
 stop.on.expire = FALSE,
 reg = getDefaultRegistry()
)

Arguments

Value

[logical(1)]. Returns TRUE if all jobs terminated successfully and FALSE if either the timeout is reached or at least one job terminated with an exception or expired.