R/availableCores.R, R/makeClusterPSOCK.R, R/makeNodePSOCK.R, and 1 more
makeClusterPSOCK.RdThe makeClusterPSOCK() function creates a cluster of R workers
for parallel processing. These R workers may be background R sessions
on the current machine, R sessions on external machines (local or remote),
or a mix of such. For external workers, the default is to use SSH to
connect to those external machines. This function works similarly to
makePSOCKcluster() of the
parallel package, but provides additional and more flexibility
options for controlling the setup of the system calls that launch the
background R workers, and how to connect to external machines.
checkNumberOfLocalWorkers(workers)
makeClusterPSOCK(
workers,
makeNode = makeNodePSOCK,
port = c("auto", "random"),
...,
autoStop = FALSE,
tries = getOption2("parallelly.makeNodePSOCK.tries", 3L),
delay = getOption2("parallelly.makeNodePSOCK.tries.delay", 15),
validate = getOption2("parallelly.makeNodePSOCK.validate", TRUE),
verbose = getOption2("parallelly.debug", FALSE)
)
makeNodePSOCK(
worker = getOption2("parallelly.localhost.hostname", "localhost"),
master = NULL,
port,
connectTimeout = getOption2("parallelly.makeNodePSOCK.connectTimeout", 2 * 60),
timeout = getOption2("parallelly.makeNodePSOCK.timeout", 30 * 24 * 60 * 60),
rscript = NULL,
homogeneous = NULL,
rscript_args = NULL,
rscript_envs = NULL,
rscript_libs = NULL,
rscript_startup = NULL,
rscript_sh = c("auto", "cmd", "sh", "none"),
default_packages = c("datasets", "utils", "grDevices", "graphics", "stats", if
(methods) "methods"),
methods = TRUE,
socketOptions = getOption2("parallelly.makeNodePSOCK.socketOptions", "no-delay"),
useXDR = getOption2("parallelly.makeNodePSOCK.useXDR", FALSE),
outfile = "/dev/null",
renice = NA_integer_,
rshcmd = getOption2("parallelly.makeNodePSOCK.rshcmd", NULL),
user = NULL,
revtunnel = NA,
rshlogfile = NULL,
rshopts = getOption2("parallelly.makeNodePSOCK.rshopts", NULL),
rank = 1L,
manual = FALSE,
dryrun = FALSE,
quiet = FALSE,
setup_strategy = getOption2("parallelly.makeNodePSOCK.setup_strategy", "parallel"),
action = c("launch", "options"),
verbose = FALSE
)The hostnames of workers (as a character vector) or the number of localhost workers (as a positive integer).
A function that creates a "SOCKnode" or
"SOCK0node" object, which represents a connection to a worker.
The port number of the master used for communicating with all
the workers (via socket connections). If an integer vector of ports, then
a random one among those is chosen. If "random", then a random port in
is chosen from 11000:11999, or from the range specified by
environment variable R_PARALLELLY_RANDOM_PORTS.
If "auto" (default), then the default (single) port is taken from
environment variable R_PARALLEL_PORT, otherwise "random" is
used.
Note, do not use this argument to specify the port number used by
rshcmd, which typically is an SSH client. Instead, if the SSH daemon
runs on a different port than the default 22, specify the SSH port by
appending it to the hostname, e.g. "remote.server.org:2200" or via
SSH options -p, e.g. rshopts = c("-p", "2200").
Optional arguments passed to
makeNode(workers[i], ..., rank = i) where i = seq_along(workers).
If TRUE, the cluster will be automatically stopped
using stopCluster() when it is
garbage collected, unless already stopped. See also autoStopCluster().
Maximum number of attempts done to launch each node
with makeNode() and the delay (in seconds) in-between attempts.
If argument port specifies more than one port, e.g. port = "random"
then a random port will be drawn and validated at most tries times.
Arguments tries and delay are used only when
setup_strategy == "sequential".
If TRUE (default), after the nodes have been created,
they are all validated that they work by inquiring about their session
information, which is saved in attribute session_info of each node.
If TRUE, informative messages are outputted.
The hostname or IP number of the machine where the worker
should run.
Attribute localhost can be set to TRUE or FALSE to manually indicate
whether worker is the same as the local host.
The hostname or IP number of the master / calling machine, as
known to the workers. If NULL (default), then the default is
Sys.info()[["nodename"]] unless worker is localhost or
revtunnel = TRUE in case it is "localhost".
The maximum time (in seconds) allowed for each socket connection between the master and a worker to be established (defaults to 2 minutes). See note below on current lack of support on Linux and macOS systems.
The maximum time (in seconds) allowed to pass without the master and a worker communicate with each other (defaults to 30 days).
The system command for launching Rscript
on the worker and whether it is installed in the same path as the calling
machine or not. For more details, see below.
Additional arguments to Rscript (as a character
vector). This argument can be used to customize the R environment of the
workers before they launches.
For instance, use rscript_args = c("-e", shQuote('setwd("/path/to")'))
to set the working directory to /path/to on all workers.
A named character vector environment variables to
set or unset on worker at startup, e.g.
rscript_envs = c(FOO = "3.14", "HOME", "UNKNOWN", UNSETME = NA_character_).
If an element is not named, then the value of that variable will be used as
the name and the value will be the value of Sys.getenv() for that
variable. Non-existing environment variables will be dropped.
These variables are set using Sys.setenv().
An named element with value NA_character_ will cause that variable to be
unset, which is done via Sys.unsetenv().
A character vector of R library paths that will be
used for the library search path of the R workers. An asterisk
("*") will be resolved to the default .libPaths() on the
worker. That is, to prepend a folder, instead of replacing the
existing ones, use rscript_libs = c("new_folder", "*").
To pass down a non-default library path currently set on the main R
session to the workers, use rscript_libs = .libPaths().
An R expression or a character vector of R code,
or a list with a mix of these, that will be evaluated on the R worker
prior to launching the worker's event loop.
For instance, use rscript_startup = 'setwd("/path/to")'
to set the working directory to /path/to on all workers.
The type of shell used where rscript is launched,
which should be "sh" is launched via a POSIX shell and "cmd" if
launched via an MS Windows shell. This controls how shell command-line
options are quoted, via
shQuote(..., type = rscript_sh).
If "none", then no quoting is done.
If "auto" (default), and the cluster node is launched locally, then it
is set to "sh" or "cmd" according to the current platform.
If launched remotely, then it is set to "sh" based on the assumption
remote machines typically launch commands via SSH in a POSIX shell.
If the remote machines run MS Windows, use rscript_sh = "cmd".
If length(rscript_sh) is two, then rscript_sh[1] is for the inner and
rscript_sh[2] is for the outer shell quoting of the Rscript call.
More precisely, rscript_sh[1] is for Rscript arguments that need shell
quoting (e.g. Rscript -e "<expr>"), and rscript_sh[2] is for the whole
Rscript ... call.
If length(rscript_sh) is one, then it is used for both the inner and the
outer shell quoting.
A character vector or NULL that controls which R
packages are attached on each cluster node during startup. An asterisk
("*") resolves to getOption("defaultPackages") on the current machine.
If NULL, then the default set of packages R are attached.
If TRUE (default), then the methods package is also
loaded. This is argument exists for legacy reasons due to how
Rscript worked in R (< 3.5.0).
A character string that sets R option
socketOptions on the worker.
If FALSE (default), the communication between master and workers, which is binary, will use small-endian (faster), otherwise big-endian ("XDR"; slower).
Where to direct the stdout and stderr connection output from the workers. If NULL, then no redirection of output is done, which means that the output is relayed in the terminal on the local computer. On Windows, the output is only relayed when running R from a terminal but not from a GUI.
A numerical 'niceness' (priority) to set for the worker processes.
The command (character vector) to be run on the master
to launch a process on another host and any additional arguments (character
vector). These arguments are only applied if machine is not
localhost. For more details, see below.
(optional) The user name to be used when communicating with another host.
If TRUE, a reverse SSH tunnel is set up for each worker such
that the worker R process sets up a socket connection to its local port
(port + rank - 1) which then reaches the master on port port.
If FALSE, then the worker will try to connect directly to port port on
master.
If NA, then TRUE or FALSE is inferred from inspection of rshcmd[1].
For more details, see below.
(optional) If a filename, the output produced by the
rshcmd call is logged to this file, of if TRUE, then it is logged
to a temporary file. The log file name is available as an attribute
as part of the return node object.
Warning: This only works with SSH clients that support command-line
option -E out.log`. For example, PuTTY's plink does
not support this option, and any attempts to specify rshlogfile will
cause the SSH connection to fail.
A unique one-based index for each worker (automatically set).
If TRUE the workers will need to be run manually. The command to run will be displayed.
If TRUE, nothing is set up, but a message suggesting how to launch the worker from the terminal is outputted. This is useful for troubleshooting.
If TRUE, then no output will be produced other than that from
using verbose = TRUE.
If "parallel" (default), the workers are set up
concurrently, one after the other. If "sequential", they are set up
sequentially.
This is an internal argument.
An object of class c("RichSOCKcluster", "SOCKcluster", "cluster")
consisting of a list of "SOCKnode" or "SOCK0node" workers (that also
inherit from RichSOCKnode).
makeNodePSOCK() returns a "SOCKnode" or
"SOCK0node" object representing an established connection to a worker.
Using too many parallel workers on the same machine may result in overusing the CPU. For example, if an R script hard codes the number of parallel workers to 32, as in
cl <- makeClusterPSOCK(32)it will use more than 100% of the CPU cores when running on machine with fewer than 32 CPU cores. For example, on a eight-core machine, this may run the CPU at 400% of its capacity, which has a significant negative effect on the current R process, but also on all other processes running on the same machine. This also a problem on systems where R gets allotted a specific number of CPU cores, which is the case on high-performance compute (HPC) clusters, but also on other shared systems that limits user processes via Linux Control Groups (cgroups). For example, a free account on Posit Cloud is limited to a single CPU core. Parallelizing with 32 workers when only having access to a single core, will result in 3200% overuse and 32 concurrent R processes competing for this single CPU core.
To protect against CPU overuse by mistake, makeClusterPSOCK() will
warn when parallelizing above 100%;
cl <- parallelly:::makeClusterPSOCK(12, dryrun = TRUE)
Warning message:
In checkNumberOfLocalWorkers(workers) :
Careful, you are setting up 12 localhost parallel workers with
only 8 CPU cores available for this R process, which could result
in a 150% load. The maximum is set to 100%. Overusing the CPUs has
negative impact on the current R process, but also on all other
processes of yours and others running on the same machine. See
help("parallelly.options", package = "parallelly") for how to
override this threshold
Any attempts resulting in more than 300% overuse will be refused;
> cl <- parallelly:::makeClusterPSOCK(25, dryrun = TRUE)
Error in checkNumberOfLocalWorkers(workers) :
Attempting to set up 25 localhost parallel workers with only
8 CPU cores available for this R process, which could result in
a 312% load. The maximum is set to 300%. Overusing the CPUs has
negative impact on the current R process, but also on all other
processes of yours and others running on the same machine. See
help("parallelly.options", package = "parallelly") for how to
override this threshold
See parallelly.options for how to change the default thresholds.
In R (>= 4.4.0), an alternatively to using
cl <- parallelly::makeClusterPSOCK(workers) is:
A hostname is considered to be localhost if it equals:
"localhost",
"127.0.0.1", or
Sys.info()[["nodename"]].
It is also considered localhost if it appears on the same line
as the value of Sys.info()[["nodename"]] in file /etc/hosts.
rshcmd and rshopts)Arguments rshcmd and rshopts are only used when connecting
to an external host.
The default method for connecting to an external host is via SSH and the
system executable for this is given by argument rshcmd. The default
is given by option
parallelly.makeNodePSOCK.rshcmd.
If that is not
set, then the default is to use ssh on Unix-like systems,
including macOS as well as Windows 10. On older MS Windows versions, which
does not have a built-in ssh client, the default is to use
(i) plink from the PuTTY
project, and then (ii) the ssh client that is distributed with
RStudio.
PuTTY puts itself on Windows' system PATH when installed, meaning this
function will find PuTTY automatically if installed. If not, to manually
set specify PuTTY as the SSH client, specify the absolute pathname of
plink.exe in the first element and option -ssh in the
second as in rshcmd = c("C:/Path/PuTTY/plink.exe", "-ssh").
This is because all elements of rshcmd are individually "shell"
quoted and element rshcmd[1] must be on the system PATH.
Furthermore, when running R from RStudio on Windows, the ssh
client that is distributed with RStudio will also be considered.
This client, which is from MinGW
MSYS, is searched for in the folder given by the RSTUDIO_MSYS_SSH
environment variable—a variable that is (only) set when running RStudio.
To use this SSH client outside of RStudio, set RSTUDIO_MSYS_SSH
accordingly.
You can override the default set of SSH clients that are searched for
by specifying them in argument rshcmd or via option
parallelly.makeNodePSOCK.rshcmd
using the format <...>, e.g.
rshcmd = c("<rstudio-ssh>", "<putty-plink>", "<ssh>"). See
below for examples.
If no SSH-client is found, an informative error message is produced.
Additional SSH command-line options may be specified via argument rshopts,
which defaults to option parallelly.makeNodePSOCK.rshopts. For
instance, a private SSH key can be provided as
rshopts = c("-i", "~/.ssh/my_private_key"). PuTTY users should
specify a PuTTY PPK file, e.g.
rshopts = c("-i", "C:/Users/joe/.ssh/my_keys.ppk").
Contrary to rshcmd, elements of rshopts are not quoted.
IMPORTANT: With one exception, it is not possible to for these
functions to log in and launch R workers on external machines that requires
a password to be entered manually for authentication.
The only known exception is the PuTTY client on Windows for which one can
pass the password via command-line option -pw, e.g.
rshopts = c("-pw", "MySecretPassword").
Note, depending on whether you run R in a terminal or via a GUI, you might not even see the password prompt. It is also likely that you cannot enter a password, because the connection is set up via a background system call.
The poor man's workaround for setup that requires a password is to manually
log into the each of the external machines and launch the R workers by hand.
For this approach, use manual = TRUE and follow the instructions
which include cut'n'pasteable commands on how to launch the worker from the
external machine.
However, a much more convenient and less tedious method is to set up key-based SSH authentication between your local machine and the external machine(s), as explain below.
The best approach to automatically launch R workers on external machines over SSH is to set up key-based SSH authentication. This will allow you to log into the external machine without have to enter a password.
Key-based SSH authentication is taken care of by the SSH client and not R. To configure this, see the manuals of your SSH client or search the web for "ssh key authentication".
If SSH is used, which is inferred from rshcmd[1], then the default is
to use reverse SSH tunneling (revtunnel = TRUE), otherwise not
(revtunnel = FALSE). Using reverse SSH tunneling, avoids complications
from otherwise having to configure port forwarding in firewalls, which
often requires static IP address as well as privileges to edit the
firewall on your outgoing router, something most users don't have.
It also has the advantage of not having to know the internal and / or the
public IP address / hostname of the master.
Yet another advantage is that there will be no need for a DNS lookup by the
worker machines to the master, which may not be configured or is disabled
on some systems, e.g. compute clusters.
rscriptIf homogeneous is FALSE, the rscript defaults to "Rscript", i.e. it
is assumed that the Rscript executable is available on the
PATH of the worker.
If homogeneous is TRUE, the rscript defaults to
file.path(R.home("bin"), "Rscript"), i.e. it is basically assumed that
the worker and the caller share the same file system and R installation.
When specified, argument rscript should be a character vector with one or
more elements. Any asterisk ("*") will be resolved to the above default
homogeneous-dependent Rscript path.
All elements are automatically shell quoted using base::shQuote(), except
those that are of format <ENVVAR>=<VALUE>, that is, the ones matching the
regular expression '^[[:alpha:]_][[:alnum:]_]*=.*'.
Another exception is when rscript inherits from 'AsIs'.
homogeneousThe default value of homogeneous is TRUE if and only if either
of the following is fulfilled:
worker is localhost
revtunnel is FALSE and master is localhost
worker is neither an IP number nor a fully qualified domain
name (FQDN). A hostname is considered to be a FQDN if it contains
one or more periods
In all other cases, homogeneous defaults to FALSE.
Argument connectTimeout does not work properly on Unix and
macOS due to limitation in R itself. For more details on this, please see
R-devel thread 'BUG?: On Linux setTimeLimit() fails to propagate timeout
error when it occurs (works on Windows)' on 2016-10-26
(https://stat.ethz.ch/pipermail/r-devel/2016-October/073309.html).
When used, the timeout will eventually trigger an error, but it won't happen
until the socket connection timeout timeout itself happens.
If there is no communication between the master and a worker within the
timeout limit, then the corresponding socket connection will be
closed automatically. This will eventually result in an error in code
trying to access the connection.
This timeout is also what terminates a stray-running parallel cluster-node
process.
When setting up a cluster of localhost workers, that is, workers running on the same machine as the master R process, occasionally a connection to a worker ("cluster node") may fail to be set up. When this occurs, an informative error message with troubleshooting suggestions will be produced. The most common reason for such localhost failures is due to port clashes. Retrying will often resolve the problem.
If R stalls when setting up a cluster of local workers, then it might
be that you have a virtual private network (VPN) enabled that is
configured to prevent you from connecting to localhost. To verify that
this is the case, call the following from the terminal:
This also freezed if the VPN intercepts connections to localhost.
If this happens, try also:
In rare cases, 127.0.0.1 might work when localhost does not.
If the latter works, setting R option:
options(parallelly.localhost.hostname = "127.0.0.1")should solve it (the default is "localhost"). You can set this
automatically when R starts by adding it to your ~/.Rprofile startup
file. Alternatively, set environment variable
R_PARALLELLY_LOCALHOST_HOSTNAME=127.0.0.1 in your ~/.Renviron file.
If using 127.0.0.1 did not work around the problem, check your VPN
settings and make sure it allows connections to localhost or 127.0.0.1.
A cluster of remote workers runs R processes on external machines. These
external R processes are launched over, typically, SSH to the remote
machine. For this to work, each of the remote machines needs to have
R installed, which preferably is of the same version as what is on the
main machine. For this to work, it is required that one can SSH to the
remote machines. Ideally, the SSH connections use authentication based
on public-private SSH keys such that the set up of the remote workers can
be fully automated (see above). If makeClusterPSOCK() fails to set
up one or more remote R workers, then an informative error message is
produced.
There are a few reasons for failing to set up remote workers. If this
happens, start by asserting that you can SSH to the remote machine and
launch Rscript by calling something like:
{local}$ ssh -l alice remote.server.org
{remote}$ Rscript --version
R scripting front-end version 4.2.2 (2022-10-31)
{remote}$ logout
{local}$
When you have confirmed the above to work, then confirm that you can achieve the same in a single command-line call;
{local}$ ssh -l alice remote.server.org Rscript --version
R scripting front-end version 4.2.2 (2022-10-31)
{local}$
The latter will assert that you have proper startup configuration also for non-interactive shell sessions on the remote machine.
If the remote machines are running on MS Windows, make sure to add argument
rscript_sh = "cmd" when calling makeClusterPSOCK(), because the default
is rscript_sh = "sh", which assumes that that the remote machines are
Unix-like machines.
Another reason for failing to setup remote workers could be that they are
running an R version that is not compatible with the version that your main
R session is running. For instance, if we run R (>= 3.6.0) locally and the
workers run R (< 3.5.0), we will get:
Error in unserialize(node$con) : error reading from connection.
This is because R (>= 3.6.0) uses serialization format version 3 by default
whereas R (< 3.5.0) only supports version 2. We can see the version of the
R workers by adding rscript_args = c("-e", shQuote("getRversion()")) when
calling makeClusterPSOCK().
When creating a cluster object, for instance via parallel::makeCluster()
or parallelly::makeClusterPSOCK(), in a package help example, in a package
vignette, or in a package test, we must remember to stop the cluster at
the end of all examples(*), vignettes, and unit tests. This is required in
order to not leave behind stray parallel cluster workers after our main R
session terminates. On Linux and macOS, the operating system often takes
care of terminating the worker processes if we forget, but on MS Windows
such processes will keep running in the background until they time out
themselves, which takes 30 days (sic!).
R CMD check --as-cran will indirectly detect these stray worker processes
on MS Windows when running R (>= 4.3.0). They are detected, because they
result in placeholder Rscript<hexcode> files being left behind in
the temporary directory. The check NOTE to look out for
(only in R (>= 4.3.0)) is:
* checking for detritus in the temp directory ... NOTE
Found the following files/directories:
'Rscript1058267d0c10' 'Rscriptbd4267d0c10'Those Rscript<hexcode> files are from background R worker processes,
which almost always are parallel cluster:s that we forgot to stop
at the end. To stop all cluster workers, use parallel::stopCluster()
at the end of your examples(*), vignettes, and package tests for every
cluster object that is created.
(*) Currently, examples are excluded from the detritus checks. This was validated with R-devel revision 82991 (2022-10-02).
## NOTE: Drop 'dryrun = TRUE' below in order to actually connect. Add
## 'verbose = TRUE' if you run into problems and need to troubleshoot.
## ---------------------------------------------------------------
## Section 1. Setting up parallel workers on the local machine
## ---------------------------------------------------------------
## EXAMPLE: Two workers on the local machine
workers <- c("localhost", "localhost")
cl <- makeClusterPSOCK(workers, dryrun = TRUE, quiet = TRUE)
## EXAMPLE: Launch 124 workers on MS Windows 10, where half are
## running on CPU Group #0 and half on CPU Group #1.
## (https://lovickconsulting.com/2021/11/18/
## running-r-clusters-on-an-amd-threadripper-3990x-in-windows-10-2/)
## The parallel workers are launched as:
## "%COMSPEC%" /c start /B /NODE 1 /AFFINITY 0xFFFFFFFFFFFFFFFE ...
## ...
## "%COMSPEC%" /c start /B /NODE 1 /AFFINITY 0xFFFFFFFFFFFFFFFE ...
## Temporarily disable CPU load protection for this example
oopts <- options(parallelly.maxWorkers.localhost = Inf)
ncores <- 124
cpu_groups <- c(0, 1)
cl <- lapply(cpu_groups, FUN = function(cpu_group) {
parallelly::makeClusterPSOCK(ncores %/% length(cpu_groups),
rscript = I(c(
Sys.getenv("COMSPEC"), "/c", "start", "/B",
"/NODE", cpu_group, "/AFFINITY", "0xFFFFFFFFFFFFFFFE",
"*"
)),
dryrun = TRUE, quiet = TRUE
)
})
## merge the two 62-node clusters into one with 124 nodes
cl <- do.call(c, cl)
#> Warning: The combined cluster contains 123 duplicated nodes
## Re-enable CPU load protection
options(oopts)
## ---------------------------------------------------------------
## Section 2. Setting up parallel workers on remote machines
## ---------------------------------------------------------------
## EXAMPLE: Three remote workers
## Setup of three R workers on two remote machines are set up
## The parallel workers are launched as:
## '/usr/bin/ssh' -R 11058:localhost:11058 n1.remote.org ...
## '/usr/bin/ssh' -R 11059:localhost:11058 n2.remote.org ...
## '/usr/bin/ssh' -R 11060:localhost:11058 n1.remote.org ...
workers <- c("n1.remote.org", "n2.remote.org", "n1.remote.org")
cl <- makeClusterPSOCK(workers, dryrun = TRUE, quiet = TRUE)
## EXAMPLE: Two remote workers running on MS Windows. Because the
## remote workers are MS Windows machines, we need to use
## rscript_sh = "cmd".
## The parallel workers are launched as:
## '/usr/bin/ssh' -R 11912:localhost:11912 mswin1.remote.org ...
## '/usr/bin/ssh' -R 11913:localhost:11912 mswin2.remote.org ...
workers <- c("mswin1.remote.org", "mswin2.remote.org")
cl <- makeClusterPSOCK(workers, rscript_sh = "cmd", dryrun = TRUE, quiet = TRUE)
## EXAMPLE: Local and remote workers
## Same setup when the two machines are on the local network and
## have identical software setups
cl <- makeClusterPSOCK(
workers,
revtunnel = FALSE, homogeneous = TRUE,
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: Three remote workers 'n1', 'n2', and 'n3' that can only be
## accessed via jumphost 'login.remote.org'
## The parallel workers are launched as:
## '/usr/bin/ssh' -R 11226:localhost:11226 -J login.remote.org n1 ...
## '/usr/bin/ssh' -R 11227:localhost:11226 -J login.remote.org n2 ...
## '/usr/bin/ssh' -R 11228:localhost:11226 -J login.remote.org n1 ...
workers <- c("n1", "n2", "n1")
cl <- makeClusterPSOCK(
workers,
rshopts = c("-J", "login.remote.org"),
homogeneous = FALSE,
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: Remote worker running on Linux from MS Windows machine
## Connect to remote Unix machine 'remote.server.org' on port 2200
## as user 'bob' from a MS Windows machine with PuTTY installed.
## Using the explicit special rshcmd = "<putty-plink>", will force
## makeClusterPSOCK() to search for and use the PuTTY plink software,
## preventing it from using other SSH clients on the system search PATH.
## The parallel worker is launched as:
## 'plink' -l bob -P 2200 -i C:/Users/bobby/.ssh/putty.ppk remote.server.org ...
cl <- makeClusterPSOCK(
"remote.server.org", user = "bob",
rshcmd = "<putty-plink>",
rshopts = c("-P", 2200, "-i", "C:/Users/bobby/.ssh/putty.ppk"),
dryrun = TRUE, quiet = TRUE
)
#> Warning: Failed to locate a default SSH client (checked: ‘putty-plink’). Please specify one via argument 'rshcmd'. Will still try with ‘ssh’.
## EXAMPLE: Remote workers with specific setup
## Setup of remote worker with more detailed control on
## authentication and reverse SSH tunneling
## The parallel worker is launched as:
## '/usr/bin/ssh' -l johnny -v -R 11000:gateway:11942 remote.server.org ...
## "R_DEFAULT_PACKAGES=... 'nice' '/path/to/Rscript' --no-init-file ...
cl <- makeClusterPSOCK(
"remote.server.org", user = "johnny",
## Manual configuration of reverse SSH tunneling
revtunnel = FALSE,
rshopts = c("-v", "-R 11000:gateway:11942"),
master = "gateway", port = 11942,
## Run Rscript nicely and skip any startup scripts
rscript = c("nice", "/path/to/Rscript"),
rscript_args = c("--no-init-file"),
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: Remote worker running on Linux from RStudio on MS Windows
## Connect to remote Unix machine 'remote.server.org' on port 2200
## as user 'bob' from a MS Windows machine via RStudio's SSH client.
## Using the explicit special rshcmd = "<rstudio-ssh>", will force
## makeClusterPSOCK() to use the SSH client that comes with RStudio,
## preventing it from using other SSH clients on the system search PATH.
## The parallel worker is launched as:
## 'ssh' -l bob remote.server.org:2200 ...
cl <- makeClusterPSOCK(
"remote.server.org:2200", user = "bob", rshcmd = "<rstudio-ssh>",
dryrun = TRUE, quiet = TRUE
)
#> Warning: Failed to locate a default SSH client (checked: ‘rstudio-ssh’). Please specify one via argument 'rshcmd'. Will still try with ‘ssh’.
## ---------------------------------------------------------------
## Section 3. Setting up parallel workers on HPC cluster
## ---------------------------------------------------------------
## EXAMPLE: 'Grid Engine' is a high-performance compute (HPC) job
## scheduler where one can request compute resources on multiple nodes,
## each running multiple cores. Examples of Grid Engine schedulers are
## Oracle Grid Engine (formerly Sun Grid Engine), Univa Grid Engine,
## and Son of Grid Engine - all commonly referred to as SGE schedulers.
## Each SGE cluster may have its own configuration with their own way
## of requesting parallel slots. Here are a few examples:
##
## ## Request 18 slots on a single host
## qsub -pe smp 18 script.sh
##
## ## Request 18 slots on one or more hosts
## qsub -pe mpi 18 script.sh
##
## This will launch the job script 'script.sh' on one host, while have
## reserved in total 18 slots (CPU cores) on this host and possible
## other hosts.
##
## This example shows how to use the SGE command 'qrsh' to launch
## 18 parallel workers from R, which is assumed to have been launched
## by 'script.sh'.
##
## The parallel workers are launched as:
## 'qrsh' -inherit -nostdin -V comphost01 ...
## 'qrsh' -inherit -nostdin -V comphost01 ...
## ...
## 'qrsh' -inherit -nostdin -V comphost06 ...
cl <- makeClusterPSOCK(
availableWorkers(),
rshcmd = "qrsh", rshopts = c("-inherit", "-nostdin", "-V"),
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: The 'Fujitsu Technical Computing Suite' is a high-performance
## compute (HPC) job scheduler where one can request compute resources on
## multiple nodes, each running multiple cores. For example,
##
## pjsub -L vnode=3 -L vnode-core=18 script.sh
##
## reserves 18 cores on three nodes. The job script runs on the first
## with enviroment variables set to infer the other nodes, resulting in
## availableWorkers() to return 3 * 18 workers. When the HPC environment
## does not support SSH between compute nodes, one can use the 'pjrsh'
## command to launch the parallel workers.
##
## The parallel workers are launched as:
## 'pjrsh' comphost01 ...
## 'pjrsh' comphost01 ...
## ...
## 'pjrsh' comphost06 ...
cl <- makeClusterPSOCK(
availableWorkers(),
rshcmd = "pjrsh",
dryrun = TRUE, quiet = TRUE
)
## ---------------------------------------------------------------
## Section 4. Setting up remote parallel workers in the cloud
## ---------------------------------------------------------------
## EXAMPLE: Remote worker running on AWS
## Launching worker on Amazon AWS EC2 running one of the
## Amazon Machine Images (AMI) provided by RStudio
## (https://www.louisaslett.com/RStudio_AMI/)
##
## The parallel worker is launched as:
## '/usr/bin/ssh' -R 11153:localhost:11153 -l ubuntu ...
## -o StrictHostKeyChecking=no -o IdentitiesOnly=yes ...
## -i ~/.ssh/my-private-aws-key.pem 1.2.3.4 ...
public_ip <- "1.2.3.4"
ssh_private_key_file <- "~/.ssh/my-private-aws-key.pem"
cl <- makeClusterPSOCK(
## Public IP number of EC2 instance
public_ip,
## User name (always 'ubuntu')
user = "ubuntu",
## Use private SSH key registered with AWS
rshopts = c(
"-o", "StrictHostKeyChecking=no",
"-o", "IdentitiesOnly=yes",
"-i", ssh_private_key_file
),
## Set up .libPaths() for the 'ubuntu' user
## and then install the future package
rscript_startup = quote(local({
p <- Sys.getenv("R_LIBS_USER")
dir.create(p, recursive = TRUE, showWarnings = FALSE)
.libPaths(p)
install.packages("future")
})),
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: Remote worker running on GCE
## Launching worker on Google Cloud Engine (GCE) running a
## container based VM (with a #cloud-config specification)
public_ip <- "1.2.3.4"
user <- "johnny"
ssh_private_key_file <- "~/.ssh/google_compute_engine"
cl <- makeClusterPSOCK(
## Public IP number of GCE instance
public_ip,
## User name (== SSH key label (sic!))
user = user,
## Use private SSH key registered with GCE
rshopts = c(
"-o", "StrictHostKeyChecking=no",
"-o", "IdentitiesOnly=yes",
"-i", ssh_private_key_file
),
## Launch Rscript inside Docker container
rscript = c(
"docker", "run", "--net=host", "rocker/r-parallel",
"Rscript"
),
dryrun = TRUE, quiet = TRUE
)
## ---------------------------------------------------------------
## Section 5. Parallel workers running locally inside virtual
## machines, Linux containers, etc.
## ---------------------------------------------------------------
## EXAMPLE: Two workers limited to 100% CPU process and 50 MiB of
## memory using Linux CGroups management. The 100% CPU quota limit
## constrain each worker to use at most one CPU worth of
## processing preventing them from overusing the machine, e.g.
## through unintended nested parallelization. The 50 MiB memory
## limit is strict - if a worker use more than this, the operating
## system will terminate the worker instantly.
## See 'man systemd.resource-control' for more details.
cl <- makeClusterPSOCK(2L,
rscript = c("systemd-run", "--user", "--scope",
"-p", "CPUQuota=100%",
"-p", "MemoryMax=50M", "-p", "MemorySwapMax=50M",
"*"
),
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: Two workers running in Docker on the local machine
## Setup of 2 Docker workers running rocker/r-parallel
##
## The parallel workers are launched as:
## R_DEFAULT_PACKAGES=... '/usr/bin/docker' 'run' '--net=host' 'rocker/r-parallel' ...
## R_DEFAULT_PACKAGES=... '/usr/bin/docker' 'run' '--net=host' 'rocker/r-parallel' ...
cl <- makeClusterPSOCK(
rep("localhost", times = 2L),
## Launch Rscript inside Docker container
rscript = c(
"docker", "run", "--net=host", "rocker/r-parallel",
"Rscript"
),
## IMPORTANT: Because Docker runs inside a virtual machine (VM) on macOS
## and MS Windows (not Linux), when the R worker tries to connect back to
## the default 'localhost' it will fail, because the main R session is
## not running in the VM, but outside on the host. To reach the host on
## macOS and MS Windows, make sure to use master = "host.docker.internal"
master = if (.Platform$OS.type == "unix") NULL else "host.docker.internal",
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: Two workers running via Linux container 'rocker/r-parallel' from
## DockerHub on the local machine using Apptainer (formerly Singularity)
##
## The parallel workers are launched as:
## R_DEFAULT_PACKAGES=... '/usr/bin/apptainer' 'exec' 'docker://rocker/r-parallel' ...
## R_DEFAULT_PACKAGES=... '/usr/bin/apptainer' 'exec' 'docker://rocker/r-parallel' ...
cl <- makeClusterPSOCK(
rep("localhost", times = 2L),
## Launch Rscript inside Linux container
rscript = c(
"apptainer", "exec", "docker://rocker/r-parallel",
"Rscript"
),
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: One worker running in udocker on the local machine
## Setup of a single udocker.py worker running rocker/r-parallel
##
## The parallel worker is launched as:
## R_DEFAULT_PACKAGES=... 'udocker.py' 'run' 'rocker/r-parallel' ...
cl <- makeClusterPSOCK(
"localhost",
## Launch Rscript inside Docker container (using udocker)
rscript = c(
"udocker.py", "run", "rocker/r-parallel",
"Rscript"
),
## Manually launch parallel workers
## (need double shQuote():s because udocker.py drops one level)
rscript_args = c(
"-e", shQuote(shQuote("parallel:::.workRSOCK()"))
),
dryrun = TRUE, quiet = TRUE
)
## EXAMPLE: One worker running in Wine for Linux on the local machine
## To install R for MS Windows in Wine, do something like:
## winecfg # In GUI, set 'Windows version' to 'Windows 10'
## wget https://cran.r-project.org/bin/windows/base/R-4.4.2-win.exe
## wine R-4.4.2-win.exe /SILENT
## Prevent packages from being installed to R's system library:
## chmod ugo-w "$HOME/.wine/drive_c/Program Files/R/R-4.4.2/library/"
## Verify it works:
## wine "C:/Program Files/R/R-4.4.2/bin/x64/Rscript.exe" --version
##
## The parallel worker is launched as:
## R_DEFAULT_PACKAGES=... WINEDEBUG=fixme-all R_LIBS_SITE= R_LIBS_USER= 'wine' ...
cl <- makeClusterPSOCK(1L,
rscript = c(
## Silence Wine warnings
"WINEDEBUG=fixme-all",
## Don't pass LC_* and R_LIBS* environments from host to Wine
sprintf("%s=", grep("^(LC_|R_LIBS)", names(Sys.getenv()), value = TRUE)),
"wine",
"C:/Program Files/R/R-4.4.2/bin/x64/Rscript.exe"
),
dryrun = TRUE, quiet = TRUE
)