future (version 1.16.0)

makeClusterPSOCK: Create a PSOCK cluster of R workers for parallel processing

Description

The 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.

Usage

makeClusterPSOCK(
  workers,
  makeNode = makeNodePSOCK,
  port = c("auto", "random"),
  ...,
  autoStop = FALSE,
  verbose = getOption("future.debug", FALSE)
)

makeNodePSOCK( worker = "localhost", master = NULL, port, connectTimeout = getOption("future.makeNodePSOCK.connectTimeout", as.numeric(Sys.getenv("R_FUTURE_MAKENODEPSOCK_CONNECTTIMEOUT", 2 * 60))), timeout = getOption("future.makeNodePSOCK.timeout", as.numeric(Sys.getenv("R_FUTURE_MAKENODEPSOCK_TIMEOUT", 30 * 24 * 60 * 60))), rscript = NULL, homogeneous = NULL, rscript_args = NULL, rscript_startup = NULL, rscript_libs = NULL, methods = TRUE, useXDR = TRUE, outfile = "/dev/null", renice = NA_integer_, rshcmd = getOption("future.makeNodePSOCK.rshcmd", Sys.getenv("R_FUTURE_MAKENODEPSOCK_RSHCMD")), user = NULL, revtunnel = TRUE, rshlogfile = NULL, rshopts = getOption("future.makeNodePSOCK.rshopts", Sys.getenv("R_FUTURE_MAKENODEPSOCK_RSHOPTS")), rank = 1L, manual = FALSE, dryrun = FALSE, verbose = FALSE )

Value

An object of class c("SOCKcluster", "cluster") consisting of a list of "SOCKnode" or "SOCK0node" workers.

makeNodePSOCK() returns a "SOCKnode" or "SOCK0node" object representing an established connection to a worker.

Definition of <em>localhost</em>

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.

Default SSH client and options (arguments <code>rshcmd</code> and <code>rshopts</code>)

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 future.makeNodePSOCK.rshcmd. If that is not set, then the default is to use ssh. Most Unix-like systems, including macOS, have ssh preinstalled on the PATH. This is also true for recent Windows 10 (since version 1803; April 2018) (*).

For Windows systems prior to Windows 10, it is less common to find ssh on the PATH. Instead it is more likely that such systems have the PuTTY software and its SSH client plink installed. PuTTY puts itself on the 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.

You can override the default set of SSH clients that are searched for by specifying them in 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.

(*) Known issue with the Windows 10 SSH client: There is a bug in the SSH client of Windows 10 that prevents it to work with reverse SSH tunneling (https://github.com/PowerShell/Win32-OpenSSH/issues/1265; Oct 2018). Because of this, it is recommended to use the PuTTY SSH client or the RStudio SSH client until this bug has been resolved in Windows 10.

Additional SSH options may be specified via argument rshopts, which defaults to option future.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.

Accessing external machines that prompts for a password

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.

Accessing external machines via key-based SSH authentication

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".

Reverse SSH tunneling

The default is to use reverse SSH tunneling (revtunnel = TRUE) for workers running on other machines. This avoids the complication of otherwise having to configure port forwarding in firewalls, which often requires static IP address as well as privileges to edit the firewall, 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.

Default value of argument <code>rscript</code>

If 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.

Default value of argument <code>homogeneous</code>

The 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.

Connection time out

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.

Communication time out

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.

Troubleshooting

Failing to set up local workers: 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.

Failing to set up remote workers: 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 3.6.1 (2019-07-05)
{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 3.6.1 (2019-07-05)
{local}$

The latter will assert that you have proper startup configuration also for non-interactive shell sessions on the remote machine.

Examples

Run this code
# NOT RUN {
## NOTE: Drop 'dryrun = TRUE' below in order to actually connect.  Add
## 'verbose = TRUE' if you run into problems and need to troubleshoot.


## EXAMPLE: Two workers on the local machine
workers <- c("localhost", "localhost")
cl <- makeClusterPSOCK(workers, dryrun = TRUE)

## EXAMPLE: Three remote workers
## Setup of three R workers on two remote machines are set up
workers <- c("n1.remote.org", "n2.remote.org", "n1.remote.org")
cl <- makeClusterPSOCK(workers, dryrun = 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
)

## EXAMPLE: Remote workers with specific setup
## Setup of remote worker with more detailed control on
## authentication and reverse SSH tunnelling
cl <- makeClusterPSOCK(
  "remote.server.org", user = "johnny",
  ## Manual configuration of reverse SSH tunnelling
  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("--vanilla"),
  dryrun = TRUE
)

## EXAMPLE: Two workers running in Docker on the local machine
## Setup of 2 Docker workers running 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 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 Windows, make sure to use master = "host.docker.internal"
  # master = "host.docker.internal",  # <= macOS & Windows
  dryrun = TRUE
)


## EXAMPLE: Two workers running in Singularity on the local machine
## Setup of 2 Singularity workers running rocker/r-parallel
cl <- makeClusterPSOCK(
  rep("localhost", times = 2L),
  ## Launch Rscript inside Docker container
  rscript = c(
    "singularity", "exec", "docker://rocker/r-parallel",
    "Rscript"
  ),
  dryrun = TRUE
)


## EXAMPLE: One worker running in udocker on the local machine
## Setup of a single udocker.py worker running 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:::.slaveRSOCK()"))
  ),
  dryrun = TRUE
)


## EXAMPLE: Remote worker running on AWS
## Launching worker on Amazon AWS EC2 running one of the
## Amazon Machine Images (AMI) provided by RStudio
## (http://www.louisaslett.com/RStudio_AMI/)
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
)


## 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
)


## EXAMPLE: Remote worker running on Linux from Windows machine
## Connect to remote Unix machine 'remote.server.org' on port 2200
## as user 'bob' from a 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.
cl <- makeClusterPSOCK(
  "remote.server.org", user = "bob",
  rshcmd = "<putty-plink>",
  rshopts = c("-P", 2200, "-i", "C:/Users/bobby/.ssh/putty.ppk"),
  dryrun = TRUE
)


## EXAMPLE: Remote worker running on Linux from RStudio on Windows
## Connect to remote Unix machine 'remote.server.org' on port 2200
## as user 'bob' from a 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.
cl <- makeClusterPSOCK(
  "remote.server.org", user = "bob", rshcmd = "<rstudio-ssh>",
  dryrun = TRUE
)
# }

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