# Introduction

Want it or not, a lot of times jobs fail. In such cases, it could be hard to figure out what went wrong. The slurmR package has some tools that can help you deal with this.

The documentation that follows applies for job submitted with sbatch, this is, job that were submitted using either Slurm_lapply, Slurm_sapply, Slurm_Map, or Slurm_EvalQ.

# Checking logs

When calling any of the *apply family functions, slurmR creates a folder with the name equal to job_name in tmp_path as follows:

• 00-rscript.r: The R script that is used to load the data, and execute whatever the instruction is (sapply, lapply, Map, etc.).

• 01-bash.sh: The Slurm configuration bash file. This passes all the SBATCH options the user specified and calls Rscript to submit the job.

• 02-output-%A-%a.out: The name-pattern for the log files generated by Rscript. In the case of job-arrays, the pattern %A is the jobid and %a is the Array id. This is usually the place where to look for useful information on why the script failed.

• 03-answer-%03i.rds: The name pattern of the output rds files. Usually, the jobs end-up writing an output, e.g. the results from the lapply call, and the %i in the pattern indicates the array id.

• *.rds Further R objects that were exported for this particular job. In the case of Slurm_lapply, for example, it usually includes X1.rds, X2.rds, …, X[njobs].rds files. Other R objects needed for the call will be saved in this same folder as well.

If there’s an issue with the submitted job, the user can take a look at these files. In general, looking at the log files is enough to figure out what could be going on. Let’s see the following example:

1. We are submitting a job that runs a complicated algorithm
library(slurmR)
x <- Slurm_lapply(
1:1000, function(x) complicated_algorithm(x),
njobs = 4,
plan = "submit"
)

By printing the output, you may see something like this:

x
Call:
Slurm_lapply(X = 1:1000, FUN = function(x) complicated_algorithm(x), njobs = 4,
plan = "submit")
job_name : slurmr-job-5724cb1616
tmp_path : /auto/rcf-40/vegayon/slurmR/slurmr-job-5724cb1616
job ID   : 6163924
Status: All jobs are pending resource allocation or are on it's way to start. (Code 1)
This is a job array. The status of each job, by array id, is the following:
done      :  -
failed    :  -
pending   :  -
running   :  1, 2, 3, 4.

The problem is, what happens if one of these fails, for example, 1 and 3:

x
Call:
Slurm_lapply(X = 1:1000, FUN = function(x) complicated_algorithm(x), njobs = 4,
plan = "submit")
job_name : slurmr-job-5724cb1616
tmp_path : /auto/rcf-40/vegayon/slurmR/slurmr-job-5724cb1616
job ID   : 6163924
Status: One or more jobs failed. (Code 99)
This is a job array. The status of each job, by array id, is the following:
done      :  2, 4.
failed    :  1, 3.
pending   :  -
running   :  -

We can check the log-files of the failed jobs using Slurm_log, for example, if we wanted to checkout the log-file of the first job of the array, we can type:

Slurm_log(x, which. = 1)

By default, while in interactive mode, you will get a prompt telling you that less (the default) will be called using the system2 command, and asking you if you wish to continue. You can change the way to checkout the log file by using an alternative command, like cat, e.g.:

Slurm_log(x, which. = 1, cmd = "cat")

Again, while in interactive mode, you will get a prompt asking you to enter "y" or "n". If the command fails, it is usually due to a missing log, either you entered an invalid number in which., or the job-array didn’t started the log-file. If the error has to do with the later, then you can always inspect the files located in the job folder using command line tools:

\$ cd /path-to-the-temp-dir/path-to-the-job-name/

# Job-resubmission

Following the previous case, let’s imagine that the failure was due to some unexpected error (the node failed), so we can resubmit the job, in order to do such, we can use the function sbatch like it follows:

# Recall that x is a slurm_job object
sbatch(x, array = "1,3")

This will re-submit the job, but only the components 1 and 3. Once it is done, the user can collect the results using Slurm_collect. This will read in the results of all jobs, not just 1 and 3.

If for some reason the R session was closed before been able to save the slurm_job object, users can always recover the slurm_job object by using the read_slurm_job function, e.g.:

# Starting from a fresh session
library(slurmR)

# By typing the path to the job folder, slurmR will recover the job
x <- read_slurm_job("/path-to-the-temp-dir/path-to-the-job-name/")