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.

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:

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 = "[email protected]", # 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:

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

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:

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:

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 <[email protected]> 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 <[email protected]>

# 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 <[email protected]> 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:

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 "[email protected]"

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.

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.

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.

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.

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.

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