As a successor of the packages BatchJobs and BatchExperiments, batchtools provides a parallel implementation of Map for high performance computing systems managed by schedulers like Slurm, Sun Grid Engine, OpenLava, TORQUE/OpenPBS, Load Sharing Facility (LSF) or Docker Swarm (see the setup section in the vignette).
Main features:
- Convenience: All relevant batch system operations (submitting, listing, killing) are either handled internally or abstracted via simple R functions
- Portability: With a well-defined interface, the source is independent from the underlying batch system - prototype locally, deploy on any high performance cluster
- Reproducibility: Every computational part has an associated seed stored in a data base which ensures reproducibility even when the underlying batch system changes
- Abstraction: The code layers for algorithms, experiment definitions and execution are cleanly separated and allow to write readable and maintainable code to manage large scale computer experiments
Install the stable version from CRAN:
install.packages("batchtools")
For the development version, use devtools:
devtools::install_github("mllg/batchtools")
Next, you need to setup batchtools for your HPC (it will run sequentially otherwise).
See the vignette for instructions.
The development of BatchJobs and BatchExperiments is discontinued for the following reasons:
- Maintainability: The packages BatchJobs and BatchExperiments are tightly connected which makes maintenance difficult. Changes have to be synchronized and tested against the current CRAN versions for compatibility. Furthermore, BatchExperiments violates CRAN policies by calling internal functions of BatchJobs.
- Data base issues: Although we invested weeks to mitigate issues with locks of the SQLite data base or file system (staged queries, file system timeouts, ...),
BatchJobskept working unreliable on some systems with high latency under certain conditions. This madeBatchJobsunusable for many users.
BatchJobs and BatchExperiments will remain on CRAN, but new features are unlikely to be ported back. The vignette contains a section comparing the packages.
- NEWS
- Function reference
- Vignette
- JOSS Paper: Short paper on batchtools. Please cite this if you use batchtools.
- Paper on BatchJobs/BatchExperiments: The described concept still holds for batchtools and most examples work analogously (see the vignette for differences between the packages).
Please cite the JOSS paper using the following BibTeX entry:
@article{,
doi = {10.21105/joss.00135},
url = {https://doi.org/10.21105/joss.00135},
year = {2017},
month = {feb},
publisher = {The Open Journal},
volume = {2},
number = {10},
author = {Michel Lang and Bernd Bischl and Dirk Surmann},
title = {batchtools: Tools for R to work on batch systems},
journal = {The Journal of Open Source Software}
}
- The High Performance Computing Task View lists the most relevant packages for scientific computing with R.
- clustermq is a similar approach which also supports multiple schedulers. Uses the ZeroMQ network protocol for communication, and shines if you have millions of fast jobs.
- batch assists in splitting and submitting jobs to LSF and MOSIX clusters.
- flowr supports LSF, Slurm, TORQUE and Moab and provides a scatter-gather approach to define computational jobs.
- future.batchtools implements
batchtoolsas backend for future. - doFuture together with future.batchtools connects
batchtoolsto foreach. - drake uses graphs to define computational jobs.
batchtoolsis used as a backend via future.batchtools.
This R package is licensed under the LGPL-3.
If you encounter problems using this software (lack of documentation, misleading or wrong documentation, unexpected behaviour, bugs, ...) or just want to suggest features, please open an issue in the issue tracker.
Pull requests are welcome and will be included at the discretion of the author.
If you have customized a template file for your (larger) computing site, please share it: fork the repository, place your template in inst/templates and send a pull request.
