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As a successor of the packages [BatchJobs](https://github.com/tudo-r/BatchJobs) and [BatchExperiments](https://github.com/tudo-r/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](https://batchtools.mlr-org.com/batchtools/articles/batchtools.html)).
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Main features:
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* Convenience: All relevant batch system operations (submitting, listing, killing) are either handled internally or abstracted via simple R functions
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* Portability: With a well-defined interface, the source is independent from the underlying batch system - prototype locally, deploy on any high performance cluster
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* Reproducibility: Every computational part has an associated seed stored in a data base which ensures reproducibility even when the underlying batch system changes
@@ -30,7 +31,8 @@ Install the stable version from CRAN:
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install.packages("batchtools")
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```
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For the development version, use [devtools](https://cran.r-project.org/package=devtools):
Main features: \* Convenience: All relevant batch system operations
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(submitting, listing, killing) are either handled internally or
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abstracted via simple R functions \* Portability: With a well-defined
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interface, the source is independent from the underlying batch system -
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prototype locally, deploy on any high performance cluster \*
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Reproducibility: Every computational part has an associated seed stored
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in a data base which ensures reproducibility even when the underlying
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batch system changes \* Abstraction: The code layers for algorithms,
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experiment definitions and execution are cleanly separated and allow to
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write readable and maintainable code to manage large scale computer
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experiments
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Main features:
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- Convenience: All relevant batch system operations (submitting,
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listing, killing) are either handled internally or abstracted via
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simple R functions
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- Portability: With a well-defined interface, the source is independent
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from the underlying batch system - prototype locally, deploy on any
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high performance cluster
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- Reproducibility: Every computational part has an associated seed
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stored in a data base which ensures reproducibility even when the
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underlying batch system changes
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- Abstraction: The code layers for algorithms, experiment definitions
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and execution are cleanly separated and allow to write readable and
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maintainable code to manage large scale computer experiments
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## Installation
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@@ -50,11 +53,6 @@ For the development version, use
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devtools::install_github("mlr-org/batchtools")
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```
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## Using github PAT from envvar GITHUB_PAT. Use `gitcreds::gitcreds_set()` and unset GITHUB_PAT in .Renviron (or elsewhere) if you want to use the more secure git credential store instead.
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## Skipping install of 'batchtools' from a github remote, the SHA1 (e4b9dacd) has not changed since last install.
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## Use `force = TRUE` to force installation
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Next, you need to setup `batchtools` for your HPC (it will run
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