---
title: "User Guide"
output:
  rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{User Guide}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{css echo=FALSE}
img {
    border: 0px !important;
    margin: 2em 2em 2em 2em !important;
}
code {
    border: 0px !important;
}
```

```{r echo=FALSE, results="hide"}
knitr::opts_chunk$set(
    cache = FALSE,
    echo = TRUE,
    collapse = TRUE,
    comment = "#>"
)
options(clustermq.scheduler = "local")
library(clustermq)
```

## Installation

### ZeroMQ

First, we need the [ZeroMQ](https://github.com/zeromq/libzmq)
system library. This is probably already installed on your system. If not, your
package manager will provide it:

```{sh, eval=FALSE}
# You can skip this step on Windows and macOS, the package binary has it
# On a computing cluster, we recommend to use Conda or Linuxbrew
brew install zeromq # Linuxbrew, Homebrew on macOS
conda install zeromq # Conda, Miniconda
sudo apt-get install libzmq3-dev # Ubuntu
sudo yum install zeromq-devel # Fedora
pacman -S zeromq # Arch Linux
```

You can also manually install it [from the
release](https://github.com/zeromq/libzmq/releases), e.g. using
[zeromq-4.3.3.tar.gz](https://github.com/zeromq/libzmq/releases/download/v4.3.3/zeromq-4.3.3.tar.gz):


```{sh eval=FALSE}
# tar -xf zeromq-4.3.3.tar.gz
# cd zeromq-4.3.3
./configure
make
make DESTDIR=/path/for/the/library install
```

If R and ZeroMQ are not provided by the same package manager you will need to
let R know where to find the library when installing `clustermq`:

```{sh eval=FALSE}
LD_LIBRARY_PATH=/path/for/the/library/lib R -e "install.packages('clustermq')"
```

### R package

The latest stable version is available [on
CRAN](https://cran.r-project.org/package=clustermq).

Alternatively, it is also available on
[`Github`](https://github.com/mschubert/clustermq).

```{r eval=FALSE}
# from CRAN
install.packages('clustermq')

# from Github
# install.packages('remotes')
remotes::install_github('mschubert/clustermq')
```

In the [`develop`](https://github.com/mschubert/clustermq/tree/develop) branch,
we will introduce code changes and new features. These may contain bugs, poor
documentation, or other inconveniences. This branch may not install at times.
However, [feedback is very
welcome](https://github.com/mschubert/clustermq/issues/new).

```{r eval=FALSE}
# install.packages('remotes')
remotes::install_github('mschubert/clustermq', ref="develop")
```

## Configuration {#configuration}

Choose your preferred parallelism using:

```{r eval=FALSE}
options(clustermq.scheduler = "your scheduler here")
```

There are three kinds of schedulers:

* `LOCAL` - sequential processing of calls (default if no HPC scheduler found)
* [Multiple cores on the same machine](#local-parallelization)
* [HPC schedulers](#setting-up-the-scheduler)

Parallel and HPC schedulers can also be used [via SSH](#ssh-connector).

### Local parallelization

While this is not the main focus of the package, you can use it to parallelize
function calls locally on multiple cores or processes. This can also be useful
to test your code before submitting it to a scheduler.

* Multiprocess (*recommended*) - Use the `callr` package to run and manage
  multiple parallel R processes with `options(clustermq.scheduler="multiprocess")`
* Multicore - Uses the `parallel` package to fork the current R process into
  multiple threads with `options(clustermq.scheduler="multicore")`. This
  sometimes causes problems (macOS, RStudio) and is not available on Windows.

### Setting up the scheduler

An HPC cluster's scheduler ensures that computing jobs are distributed to
available worker nodes. Hence, this is what `clustermq` interfaces with in
order to do computations.

By default, we will take whichever scheduler we find and fall back on local
processing. This will work in most, but not all cases.

To set up a scheduler explicitly, see the following links:

* [LSF](#LSF) - *should work without setup*
* [SGE](#SGE) - *should work without setup*
* [SLURM](#SLURM) - *should work without setup*
* [PBS](#PBS)/[Torque](#TORQUE) - *needs* `options(clustermq.scheduler="PBS"/"Torque")`
* if you want another scheduler, [open an
  issue](https://github.com/mschubert/clustermq/issues/new)

Default submission templates [are
provided](https://github.com/mschubert/clustermq/tree/master/inst) and [can be
customized](#configuration),
e.g. to activate [compute environments or
containers](#Environments).

### SSH connector {#ssh-connector}

There are reasons why you might prefer to not to work on the computing cluster
directly but rather on your local machine instead.
[RStudio](https://posit.co/products/open-source/rstudio/) is an excellent local
IDE, it's more responsive than and feature-rich than browser-based solutions
([RStudio server](https://posit.co/products/open-source/rstudio-server/),
[Project Jupyter](https://jupyter.org/)), and it avoids X forwarding issues
when you want to look at plots you just made.

Using this setup, however, you lost access to the computing cluster. Instead,
you had to copy your data there, and then submit individual scripts as jobs,
aggregating the data in the end again. `clustermq` is trying to solve this by
providing a transparent SSH interface.

In order to use `clustermq` from your local machine, the package needs to be
installed on both there and on the computing cluster. On the computing cluster,
[set up your scheduler](#setting-up-the-scheduler) and make sure `clustermq`
runs there without problems. Note that the *remote scheduler* can not be
`LOCAL` (default if no HPC scheduler found) or `SSH` for this to work.

```{r eval=FALSE}
# If this is set to 'LOCAL' or 'SSH' you will get the following error:
#  Expected PROXY_READY, received ‘PROXY_ERROR: Remote SSH QSys is not allowed’
options(
    clustermq.scheduler = "multiprocess" # or multicore, LSF, SGE, Slurm etc.
)
```

On your *local machine*, add the following options in your `~/.Rprofile`:

```{r eval=FALSE}
options(
    clustermq.scheduler = "ssh",
    clustermq.ssh.host = "user@host", # use your user and host, obviously
    clustermq.ssh.log = "~/cmq_ssh.log" # log for easier debugging
)
```

We recommend that you [set up SSH keys](https://www.digitalocean.com/community/tutorials/how-to-configure-ssh-key-based-authentication-on-a-linux-server) for password-less login.

## Usage

### The `Q` function

The following arguments are supported by `Q`:

 * `fun` - The function to call. This needs to be self-sufficient (because it
        will not have access to the `master` environment)
 * `...` - All iterated arguments passed to the function. If there is more than
        one, all of them need to be named
 * `const` - A named list of non-iterated arguments passed to `fun`
 * `export` - A named list of objects to export to the worker environment

Behavior can further be fine-tuned using the options below:

 * `fail_on_error` - Whether to stop if one of the calls returns an error
 * `seed` - A common seed that is combined with job number for reproducible results
 * `memory` - Amount of memory to request for the job (`bsub -M`)
 * `n_jobs` - Number of jobs to submit for all the function calls
 * `job_size` - Number of function calls per job. If used in combination with
        `n_jobs` the latter will be overall limit
 * `chunk_size` - How many calls a worker should process before reporting back
        to the master. Default: every worker will report back 100 times total

The full documentation is available by typing `?Q`.

### Examples

The package is designed to distribute arbitrary function calls on HPC worker
nodes. There are, however, a couple of caveats to observe as the R session
running on a worker does not share your local memory.

The simplest example is to a function call that is completely self-sufficient,
and there is one argument (`x`) that we iterate through:

```{r}
fx = function(x) x * 2
Q(fx, x=1:3, n_jobs=1)
```

Non-iterated arguments are supported by the `const` argument:

```{r}
fx = function(x, y) x * 2 + y
Q(fx, x=1:3, const=list(y=10), n_jobs=1)
```

If a function relies on objects in its environment that are not passed as
arguments (including other functions), they can be exported using the `export` argument:

```{r}
fx = function(x) x * 2 + y
Q(fx, x=1:3, export=list(y=10), n_jobs=1)
```

If we want to use a package function we need to load it on the worker using the
`pkg` argument or referencing it with `package_name::`:

```{r}
fx = function(x) {
    x %>%
        mutate(area = Sepal.Length * Sepal.Width) %>%
        head()
}
Q(fx, x=list(iris), pkgs="dplyr", n_jobs=1)
```

### As parallel `foreach` backend

The [`foreach`](https://cran.r-project.org/package=foreach) package provides an
interface to perform repeated tasks on different backends. While it can perform
the function of simple loops using `%do%`:

```{r}
library(foreach)
x = foreach(i=1:3) %do% sqrt(i)
```

it can also perform these operations in parallel using `%dopar%`:

```{r}
x = foreach(i=1:3) %dopar% sqrt(i)
```

The latter allows registering different handlers for parallel execution, where
we can use `clustermq`:

```{r}
# set up the scheduler first, otherwise this will run sequentially
clustermq::register_dopar_cmq(n_jobs=2, memory=1024) # this accepts same arguments as `Q`
x = foreach(i=1:3) %dopar% sqrt(i) # this will be executed as jobs
```

As [BiocParallel](http://bioconductor.org/packages/release/bioc/html/BiocParallel.html)
supports `foreach` too, this means we can run all packages that use `BiocParallel`
on the cluster as well via `DoparParam`.

```{r eval=FALSE}
library(BiocParallel)
register(DoparParam()) # after register_dopar_cmq(...)
bplapply(1:3, sqrt)
```

### With `drake`

The [`drake`](https://github.com/ropensci/drake) package enables users to
define a dependency structure of different function calls, and only evaluate
them if the underlying data changed.

> drake — or, Data Frames in R for Make — is a general-purpose workflow manager
> for data-driven tasks. It rebuilds intermediate data objects when their
> dependencies change, and it skips work when the results are already up to
> date. Not every runthrough starts from scratch, and completed workflows have
> tangible evidence of reproducibility. drake also supports scalability,
> parallel computing, and a smooth user experience when it comes to setting up,
> deploying, and maintaining data science projects.

It can use `clustermq` to perform calculations as jobs:

```{r eval=FALSE}
library(drake)
load_mtcars_example()
# clean(destroy = TRUE)
# options(clustermq.scheduler = "multicore")
make(my_plan, parallelism = "clustermq", jobs = 2, verbose = 4)
```

## Options

The various configurable options are mentioned throughout the documentation,
where applicable, however, we list all of the options here for reference.

Options can be set by including a call to `options(<key> = <value>)` in your
`.Rprofile`, or by calling `options(<key> = <value>)` in a script or
interactively during a session.

* `clustermq.scheduler` - One of the supported
      [`clustermq` schedulers](#configuration); options are `"LOCAL"`,
      `"multiprocess"`, `"multicore"`, `"lsf"`, `"sge"`, `"slurm"`, `"pbs"`,
      `"Torque"`, or `"ssh"` (default is the HPC scheduler found in `$PATH`,
      otherwise `"LOCAL"`)
* `clustermq.host` - The name of the node or device for constructing the
      `ZeroMQ` host address (default is `Sys.info()["nodename"]`)
* `clustermq.ssh.host` - The user name and host for
      [connecting to the HPC via SSH](#ssh-connector) (e.g. `user@host`); we
      recommend setting up SSH keys for password-less login
* `clustermq.ssh.log` - Path for a file (on the SSH host) that will be created
      and populated with logging information regarding the SSH connection
      (e.g. `"~/cmq_ssh.log"`); helpful for debugging purposes
* `clustermq.ssh.timeout` - The amount of time to wait (in seconds) for a SSH
      start-up connection before timing out (default is 5 seconds)
* `clustermq.worker.timeout` - The amount of time to wait (in seconds) for
      master-worker communication before timing out (default is 600 seconds)
* `clustermq.error.timeout` - The amount of time to wait (in seconds), in case
      of a worker error, for the remaining workers to finish their computations
      and shut down cleanly (default is `min(timeout, 30)` seconds)
* `clustermq.template` - Path to a [template file](#scheduler-templates) for
      submitting HPC jobs; only necessary if using your own template, otherwise
      the default template will be used (default depends on
      `clustermq.scheduler`)
* `clustermq.data.warning` - The threshold for the size of the common data (in
      Mb) before `clustermq` throws a warning (default is 1000)
* `clustermq.defaults` - A named-list of default values for the HPC template;
      this takes precedence over defaults specified in the template file
      (default is an empty list (i.e. `list()`))

## Troubleshooting

### Debugging workers

Function calls evaluated by workers are wrapped in event handlers, which means
that even if a call evaluation throws an error, this should be reported back to
the main R session.

However, there are reasons why workers might crash, and in which case they can
not report back. These include:

* A segfault in a low-level process
* Process kill due to resource constraints (e.g. walltime)
* Reaching the wait timeout without any signal from the master process
* Probably others

In this case, it is useful to have the worker(s) create a log file that will
also include events that are not reported back. It can be requested using:

```{r eval=FALSE}
Q(..., log_worker=TRUE)
```

This will create a file called *<cmq_id>-<array_index>.log* in your current
working directory, irrespective of which scheduler you use.

You can customize the file name using

```{r eval=FALSE}
Q(..., template=list(log_file = <yourlog>))
```

Note that in this case `log_file` is a template field of your scheduler script,
and hence needs to be present there in order for this to work. The default
templates all have this field included.

In order to log each worker separately, some schedulers support wildcards in
their log file names. For instance:

* Multicore/Multiprocess: `log_file="/path/to.file.%i"`
* SGE: `log_file="/path/to.file.\$TASK_ID"`
* LSF: `log_file="/path/to.file.%I"`
* Slurm: `log_file="/path/to.file.%a"`
* PBS: `log_file="/path/to.file.$PBS_ARRAY_INDEX"`
* Torque: `log_file="/path/to.file.$PBS_ARRAYID"`

Your scheduler documentation will have more details about the available
options.

When reporting a bug that includes worker crashes, please always include a log
file.

### SSH

Before trying remote schedulers via SSH, make sure that the scheduler works
when you first connect to the cluster and run a job from there.

If the terminal is stuck at

```
Connecting <user@host> via SSH ...
```

make sure that each step of your SSH connection works by typing the following
commands in your **local** terminal and make sure that you don't get errors or
warnings in each step:

```{sh eval=FALSE}
# test your ssh login that you set up in ~/.ssh/config
# if this fails you have not set up SSH correctly
ssh <user@host>

# test port forwarding from 54709 remote to 6687 local (ports are random)
# if the fails you will not be able to use clustermq via SSH
ssh -R 54709:localhost:6687 <user@host> R --vanilla
```

If you get an `Command not found: R` error, make sure your `$PATH` is set up
correctly in your `~/.bash_profile` and/or your `~/.bashrc` (depending on your
cluster config you might need either). You may also need to modify your [SSH
template](#ssh-template) to load R as a module or conda environment.

If you get a SSH warning or error try again with `ssh -v` to enable verbose
output. If the forward itself works, run the following in your local R session
(ideally also in command-line R, [not only in
RStudio](https://github.com/mschubert/clustermq/issues/206)):

```{r eval=FALSE}
options(clustermq.scheduler = "ssh",
        clustermq.ssh.log = "~/ssh_proxy.log")
Q(identity, x=1, n_jobs=1)
```

This will create a log file *on the remote server* that will contain any errors
that might have occurred during `ssh_proxy` startup.

If the `ssh_proxy` startup fails on your local machine with the error

```
Remote R process did not respond after 5 seconds. Check your SSH server log.
```

but the server log does not show any errors, then you can try increasing the
timeout:

```{r eval=FALSE}
options(clustermq.ssh.timeout = 30) # in seconds
```

This can happen when your SSH startup template includes additional steps before
starting R, such as activating a module or conda environment, or having to
confirm the connection via two-factor authentication.

## Environments

### Environments for workers

In some cases, it may be necessary to activate a specific computing environment
on the scheduler jobs prior to starting up the worker. This can be, for
instance, because *R* was only installed in a specific environment or
container.

Examples for such environments or containers are:

* [Bash module](https://modules.sourceforge.net/) environments
* [Conda](https://conda.io/) environments
* [Docker](https://www.docker.com/)/[Singularity](https://singularity.lbl.gov/) containers

It should be possible to activate them in the job submission script (i.e., the
template file). This is widely untested, but would look the following for the
[LSF](#LSF) scheduler (analogous for others):

```{sh eval=FALSE}
#BSUB-J {{ job_name }}[1-{{ n_jobs }}]  # name of the job / array jobs
#BSUB-o {{ log_file | /dev/null }}      # stdout + stderr
#BSUB-M {{ memory | 4096 }}             # Memory requirements in Mbytes
#BSUB-R rusage[mem={{ memory | 4096 }}] # Memory requirements in Mbytes
##BSUB-q default                        # name of the queue (uncomment)
##BSUB-W {{ walltime | 6:00 }}          # walltime (uncomment)

module load {{ bashenv | default_bash_env }}
# or: source activate {{ conda | default_conda_env_name }}
# or: your environment activation command
ulimit -v $(( 1024 * {{ memory | 4096 }} ))
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
```

This template still needs to be filled, so in the above example you need to
pass either

```{r eval=FALSE}
Q(..., template=list(bashenv="my environment name"))
```

or set it via an *.Rprofile* option:

```{r eval=FALSE}
options(
    clustermq.defaults = list(bashenv="my default env")
)
```

### Running master inside containers

If your master process is inside a container, accessing the HPC scheduler is
more difficult. Containers, including singularity and docker, isolate the
processes inside the container from the host. The *R* process will not be able
to submit a job because the scheduler cannot be found.

Note that the HPC node running the master process must be allowed to submit
jobs. Not all HPC systems allow compute nodes to submit jobs. If that is the
case, you may need to run the master process on the login node, and discuss the
issue with your system administrator.

If your container is binary compatible with the host, you may be able to bind
in the scheduler executable to the container.

For example, PBS might look something like:

```{sh eval=FALSE}
#PBS directives ...

module load singularity

SINGULARITYENV_APPEND_PATH=/opt/pbs/bin
singularity exec --bind /opt/pbs/bin r_image.sif Rscript master_script.R
```

A working example of binding SLURM into a CentOS 7 container image from a
CentOS 7 host is available at
https://groups.google.com/a/lbl.gov/d/msg/singularity/syLcsIWWzdo/NZvF2Ud2AAAJ

Alternatively, you can create a script that uses SSH to execute the scheduler
on the login node. For this, you will need an SSH client in the container,
[keys set up for password-less login](https://www.digitalocean.com/community/tutorials/how-to-configure-ssh-key-based-authentication-on-a-linux-server),
and create a script to call the scheduler on the login node via ssh (e.g.
`~/bin/qsub` for SGE/PBS/Torque, `bsub` for LSF and `sbatch` for Slurm):

```{sh eval=FALSE}
#!/bin/bash
ssh -i ~/.ssh/<your key file> ${PBS_O_HOST:-"no_host_not_in_a_pbs_job"} qsub "$@"
```

Make sure the script is executable, and bind/copy it into the container
somewhere on `$PATH`. Home directories are bound in by default in singularity.

```{sh eval=FALSE}
chmod u+x ~/bin/qsub
SINGULARITYENV_APPEND_PATH=~/bin
```

## Scheduler templates {#scheduler-templates}

### LSF {#LSF}

In your `~/.Rprofile` on your computing cluster, set the following options:

```{r eval=FALSE}
options(
    clustermq.scheduler = "lsf",
    clustermq.template = "/path/to/file/below" # if using your own template
)
```

The option `clustermq.template` should point to a LSF template file like the
one below (only needed if you want to supply your own template rather than
using the default).

```{sh eval=FALSE}
#BSUB-J {{ job_name }}[1-{{ n_jobs }}]  # name of the job / array jobs
#BSUB-n {{ cores | 1 }}                 # number of cores to use per job
#BSUB-o {{ log_file | /dev/null }}      # stdout + stderr; %I for array index
#BSUB-M {{ memory | 4096 }}             # Memory requirements in Mbytes
#BSUB-R rusage[mem={{ memory | 4096 }}] # Memory requirements in Mbytes
##BSUB-q default                        # name of the queue (uncomment)
##BSUB-W {{ walltime | 6:00 }}          # walltime (uncomment)

ulimit -v $(( 1024 * {{ memory | 4096 }} ))
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
```

In this file, `#BSUB-*` defines command-line arguments to the `bsub` program.

* Memory: defined by `BSUB-M` and `BSUB-R`. Check your local setup if the
  memory values supplied are MiB or KiB, default is `4096` if not requesting
  memory when calling `Q()`
* Queue: `BSUB-q default`. Use the queue with name *default*. This will most
  likely not exist on your system, so choose the right name (or comment out
  this line with an additional `#`)
* Walltime: `BSUB-W {{ walltime }}`. Set the maximum time a job is allowed to
  run before being killed. The default here is to disable this line. If you
  enable it, enter a fixed value or pass the `walltime` argument to each
  function call. The way it is written, it will use 6 hours if no arguemnt is
  given.
* For other options, see [the LSF
  documentation](https://www.ibm.com/docs/en/spectrum-lsf/10.1.0?topic=bsub-options)
  and add them via `#BSUB-*` (where `*` represents the argument)
* Do not change the identifiers in curly braces (`{{ ... }}`), as they are used
  to fill in the right variables

Once this is done, the package will use your settings and no longer warn you of
the missing options.

### SGE {#SGE}

In your `~/.Rprofile` on your computing cluster, set the following options:

```{r eval=FALSE}
options(
    clustermq.scheduler = "sge",
    clustermq.template = "/path/to/file/below" # if using your own template
)
```

The option `clustermq.template` should point to a SGE template file like the
one below (only needed if you want to supply your own template rather than
using the default).

```{sh eval=FALSE}
#$ -N {{ job_name }}               # job name
#$ -q default                      # submit to queue named "default"
#$ -j y                            # combine stdout/error in one file
#$ -o {{ log_file | /dev/null }}   # output file
#$ -cwd                            # use pwd as work dir
#$ -V                              # use environment variable
#$ -t 1-{{ n_jobs }}               # submit jobs as array
#$ -pe {{ cores | 1 }}             # number of cores to use per job

ulimit -v $(( 1024 * {{ memory | 4096 }} ))
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
```

In this file, `#$-*` defines command-line arguments to the `qsub` program.

* Queue: `$ -q default`. Use the queue with name *default*. This will most
  likely not exist on your system, so choose the right name (or comment out
  this line with an additional `#`)
* For other options, see [the SGE
  documentation](https://gridscheduler.sourceforge.net/htmlman/manuals.html). Do
  not change the identifiers in curly braces (`{{ ... }}`), as they are used to
  fill in the right variables.

Once this is done, the package will use your settings and no longer warn you of
the missing options.

### SLURM {#SLURM}

In your `~/.Rprofile` on your computing cluster, set the following options:

```{r eval=FALSE}
options(
    clustermq.scheduler = "slurm",
    clustermq.template = "/path/to/file/below" # if using your own template
)
```

The option `clustermq.template` should point to a SLURM template file like the
one below (only needed if you want to supply your own template rather than
using the default).

```{sh eval=FALSE}
#!/bin/sh
#SBATCH --job-name={{ job_name }}
#SBATCH --partition=default
#SBATCH --output={{ log_file | /dev/null }} # you can add .%a for array index
#SBATCH --error={{ log_file | /dev/null }}
#SBATCH --mem-per-cpu={{ memory | 4096 }}
#SBATCH --array=1-{{ n_jobs }}
#SBATCH --cpus-per-task={{ cores | 1 }}

ulimit -v $(( 1024 * {{ memory | 4096 }} ))
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
```

In this file, `#SBATCH` defines command-line arguments to the `sbatch` program.

* Queue: `SBATCH --partition default`. Use the queue with name *default*. This
  will most likely not exist on your system, so choose the right name (or
  comment out this line with an additional `#`)
* For other options, see [the SLURM
  documentation](https://slurm.schedmd.com/sbatch.html). Do not change the
  identifiers in curly braces (`{{ ... }}`), as they are used to fill in the
  right variables.

Once this is done, the package will use your settings and no longer warn you of
the missing options.

### PBS {#PBS}

In your `~/.Rprofile` on your computing cluster, set the following options:

```{r eval=FALSE}
options(
    clustermq.scheduler = "pbs",
    clustermq.template = "/path/to/file/below" # if using your own template
)
```

The option `clustermq.template` should point to a PBS template file like the
one below (only needed if you want to supply your own template rather than
using the default).

```{sh eval=FALSE}
#PBS -N {{ job_name }}
#PBS -J 1-{{ n_jobs }}
#PBS -l select=1:ncpus={{ cores | 1 }}:mpiprocs={{ cores | 1 }}:mem={{ memory | 4096 }}MB
#PBS -l walltime={{ walltime | 12:00:00 }}
#PBS -o {{ log_file | /dev/null }}
#PBS -j oe

#PBS -q default

ulimit -v $(( 1024 * {{ memory | 4096 }} ))
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
```

In this file, `#PBS-*` defines command-line arguments to the `qsub` program.

* Queue: `#PBS-q default`. Use the queue with name *default*. This will most
  likely not exist on your system, so choose the right name (or comment out
  this line with an additional `#`)
* For other options, see the PBS documentation. Do not change the identifiers
  in curly braces (`{{ ... }}`), as they are used to fill in the right
  variables.

Once this is done, the package will use your settings and no longer warn you of
the missing options.

### Torque {#Torque}

In your `~/.Rprofile` on your computing cluster, set the following options:

```{r eval=FALSE}
options(clustermq.scheduler = "Torque",
        clustermq.template = "/path/to/file/below" # if using your own template
)
```

The option `clustermq.template` should point to a Torque template file like the
one below (only needed if you want to supply your own template rather than
using the default).

```{sh eval=FALSE}
#PBS -N {{ job_name }}
#PBS -l nodes={{ n_jobs }}:ppn={{ cores | 1 }},walltime={{ walltime | 12:00:00 }}
#PBS -o {{ log_file | /dev/null }}
#PBS -q default
#PBS -j oe

ulimit -v $(( 1024 * {{ memory | 4096 }} ))
CMQ_AUTH={{ auth }} R --no-save --no-restore -e 'clustermq:::worker("{{ master }}")'
```

In this file, `#PBS-*` defines command-line arguments to the `qsub` program.

* Queue: `#PBS -q default`. Use the queue with name *default*. This will most
  likely not exist on your system, so choose the right name (or comment out
  this line with an additional `#`)
* For other options, see the Torque documentation. Do not change the
  identifiers in curly braces (`{{ ... }}`), as they are used to fill in the
  right variables.

Once this is done, the package will use your settings and no longer warn you of
the missing options.

### SSH {#ssh-template}

While SSH is not a scheduler, we can access remote schedulers via SSH. If you
want to use it, first make sure that your real scheduler is running when
manually connecting to the HPC environment.

```{r eval=FALSE}
options(clustermq.scheduler = "ssh",
		clustermq.ssh.host = "myhost", # set this up in your local ~/.ssh/config
        clustermq.ssh.log = "~/ssh_proxy.log", # log file on your HPC
        clustermq.ssh.timeout = 30, # if changing the default connection timeout
        clustermq.template = "/path/to/file/below" # if using your own template
)
```

The default template is shown below. If `R` is not in your HPC `$PATH`, you may
need to specify its path or [load the required bash modules/conda
environments](https://github.com/mschubert/clustermq/issues/281).

```{sh eval=FALSE}
ssh -o "ExitOnForwardFailure yes" -f \
    -R {{ ctl_port }}:localhost:{{ local_port }} \
    -R {{ job_port }}:localhost:{{ fwd_port }} \
    {{ ssh_host }} \
    "R --no-save --no-restore -e \
        'clustermq:::ssh_proxy(ctl={{ ctl_port }}, job={{ job_port }})' \
        > {{ ssh_log | /dev/null }} 2>&1"
```
