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<!-- README.md is generated from README.Rmd. Please edit that file -->
<img src="man/figures/logo_slurm.png" align="right" width=150px>
# rslurm: submit R code to a Slurm cluster
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### About
Development of this R package was supported by the National
Socio-Environmental Synthesis Center (SESYNC) under funding received
from the National Science Foundation grants DBI-1052875 and DBI-1639145.
The package was developed by Philippe Marchand and Ian Carroll, with
Mike Smorul and Rachael Blake contributing. Erick Verleye is the current
maintainer.
As of January 2023 the University of Colorado Boulder’s Earth Lab
adopted rslurm and is responsible for all future maintenance.
### Installation
You can install the released version of
[rslurm](https://cran.r-project.org/package=rslurm) from
[CRAN](https://CRAN.R-project.org) with:
``` r
install.packages("rslurm")
```
And the development version from
[GitHub](https://github.com/earthlab/rslurm) with:
``` r
# install.packages("devtools")
devtools::install_github("earthlab/rslurm")
```
### Documentation
Package documentation is accessible from the R console through
`package?rslurm` and [online](https://www.earthdatascience.org/rslurm/).
### Example
Note that job submission is only possible on a system with access to a
Slurm workload manager (i.e. a system where the command line utilities
`squeue` or `sinfo` return information from a Slurm head node).
To illustrate a typical rslurm workflow, we use a simple function that
takes a mean and standard deviation as parameters, generates a million
normal deviates and returns the sample mean and standard deviation.
``` r
test_func <- function(par_mu, par_sd) {
samp <- rnorm(10^6, par_mu, par_sd)
c(s_mu = mean(samp), s_sd = sd(samp))
}
```
We then create a parameter data frame where each row is a parameter set
and each column matches an argument of the function.
``` r
pars <- data.frame(par_mu = 1:10,
par_sd = seq(0.1, 1, length.out = 10))
```
We can now pass that function and the parameters data frame to
`slurm_apply`, specifying the number of cluster nodes to use and the
number of CPUs per node.
``` r
library(rslurm)
sjob <- slurm_apply(test_func, pars, jobname = 'test_apply',
nodes = 2, cpus_per_node = 2, submit = FALSE)
```
The output of `slurm_apply` is a `slurm_job` object that stores a few
pieces of information (job name, job ID, and the number of nodes) needed
to retrieve the job’s output.
See [Get
started](https://www.earthdatascience.org/rslurm/articles/rslurm.html)
for more information.
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