# User Guide

## Installation

### ZeroMQ

First, we need the ZeroMQ system library. This is probably already installed on your system. If not, your package manager will provide it:

    # 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

### R package

The latest stable version is available on CRAN.

Alternatively, it is also available on `Github`.

    # from CRAN
    install.packages('clustermq')

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

In the `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.

    # install.packages('remotes')
    remotes::install_github('mschubert/clustermq', ref="develop")

## Configuration

### 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 saves memory but 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 - should work without setup
- SGE - should work without setup
- SLURM - should work without setup
- PBS/Torque - needs `options(clustermq.scheduler="PBS"/"Torque")`
- if you want another scheduler, open an issue

Default submission templates are provided and can be customized, e.g. to activate compute environments or containers.

### 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 is an excellent local IDE, it’s more responsive than and feature-rich than browser-based solutions (RStudio server, Project Jupyter), 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 and make sure `clustermq` runs there without problems. On your local machine, add the following options in your `~/.Rprofile`:

    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 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:

    fx = function(x) x * 2
    Q(fx, x=1:3, n_jobs=1)
    #> Running sequentially ('LOCAL') ...
    #> [[1]]
    #> [1] 2
    #>
    #> [[2]]
    #> [1] 4
    #>
    #> [[3]]
    #> [1] 6

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

    fx = function(x, y) x * 2 + y
    Q(fx, x=1:3, const=list(y=10), n_jobs=1)
    #> Running sequentially ('LOCAL') ...
    #> [[1]]
    #> [1] 12
    #>
    #> [[2]]
    #> [1] 14
    #>
    #> [[3]]
    #> [1] 16

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

    fx = function(x) x * 2 + y
    Q(fx, x=1:3, export=list(y=10), n_jobs=1)
    #> Running sequentially ('LOCAL') ...
    #> [[1]]
    #> [1] 12
    #>
    #> [[2]]
    #> [1] 14
    #>
    #> [[3]]
    #> [1] 16

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::`:

    fx = function(x) {
        x %>%
            mutate(area = Sepal.Length * Sepal.Width) %>%
            head()
    }
    Q(fx, x=list(iris), pkgs="dplyr", n_jobs=1)
    #> Running sequentially ('LOCAL') ...
    #>
    #> Attaching package: 'dplyr'
    #> The following objects are masked from 'package:stats':
    #>
    #>     filter, lag
    #> The following objects are masked from 'package:base':
    #>
    #>     intersect, setdiff, setequal, union
    #> [[1]]
    #>   Sepal.Length Sepal.Width Petal.Length Petal.Width Species  area
    #> 1          5.1         3.5          1.4         0.2  setosa 17.85
    #> 2          4.9         3.0          1.4         0.2  setosa 14.70
    #> 3          4.7         3.2          1.3         0.2  setosa 15.04
    #> 4          4.6         3.1          1.5         0.2  setosa 14.26
    #> 5          5.0         3.6          1.4         0.2  setosa 18.00
    #> 6          5.4         3.9          1.7         0.4  setosa 21.06

### As parallel `foreach` backend

The `foreach` package provides an interface to perform repeated tasks on different backends. While it can perform the function of simple loops using `%do%`:

    library(foreach)
    x = foreach(i=1:3) %do% sqrt(i)

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

    x = foreach(i=1:3) %dopar% sqrt(i)
    #> Running sequentially ('LOCAL') ...

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

    # 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
    #> Running sequentially ('LOCAL') ...

As BiocParallel supports `foreach` too, this means we can run all packages that use `BiocParallel` on the cluster as well via `DoparParam`.

    library(BiocParallel)
    register(DoparParam()) # after register_dopar_cmq(...)
    bplapply(1:3, sqrt)

### With `drake`

The `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:

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

## 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:

    Q(..., log_worker=TRUE)

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

You can customize the file name using

    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:

    # 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).

If you get a SSH warning or error try again with `ssh -v` to enable verbose output.

If the forward itself works, set the following option in your `~/.Rprofile`:

    options(clustermq.ssh.log = "~/ssh_proxy.log")

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:

    options(clustermq.ssh.timeout = 10) # or a higher number

This can happens when your SSH startup template includes additional steps before starting R, such as activating a module or conda environment.

## 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 environments
- Conda environments
- Docker/Singularity 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 scheduler (analogous for others):

    #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

    Q(..., template=list(bashenv="my environment name"))

or set it via an .Rprofile option:

    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:

    #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, 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):

    #!/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.

    chmod u+x ~/bin/qsub
    SINGULARITYENV_APPEND_PATH=~/bin

## Scheduler templates

### LSF

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

    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).

    #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 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

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

    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).

    #$ -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. 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

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

    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).

    #!/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. 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

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

    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).

    #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

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

    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).

    #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.