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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
<img src="man/figures/logo_slurm.png" align="right" width=150px>
# rslurm: submit R code to a Slurm cluster
<!-- badges: start -->
[](https://CRAN.R-project.org/package=rslurm)
[![R build status]https://github.com/earthlab/rslurm/workflows/R-CMD-check/badge.svg)](https://github.com/earthlab/rslurm/actions)
[](https://www.repostatus.org/#active)
[](https://CRAN.R-project.org/package=rslurm)
[](https://zenodo.org/badge/latestdoi/37485241)
[](https://github.com/earthlab/rslurm/actions/workflows/R-CMD-check.yaml)
<!-- badges: end -->
### 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, Ian Carroll, and Quentin Read, 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, eval = FALSE}
install.packages("rslurm")
```
And the development version from [GitHub](https://github.com/earthlab/rslurm) with:
``` {r, eval = FALSE}
# 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, eval = FALSE}
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, eval = FALSE}
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, eval = FALSE}
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.