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https://github.com/HenrikBengtsson/doFuture
:rocket: R package: doFuture - Use Foreach to Parallelize via Future Framework
https://github.com/HenrikBengtsson/doFuture
batchjobs batchtools biocparallel cran distributed-computing foreach hpc hpc-clusters package parallel plyr r
Last synced: 26 days ago
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:rocket: R package: doFuture - Use Foreach to Parallelize via Future Framework
- Host: GitHub
- URL: https://github.com/HenrikBengtsson/doFuture
- Owner: HenrikBengtsson
- Created: 2016-05-05T05:35:46.000Z (about 8 years ago)
- Default Branch: develop
- Last Pushed: 2024-04-19T01:13:02.000Z (3 months ago)
- Last Synced: 2024-05-09T10:35:41.038Z (about 2 months ago)
- Topics: batchjobs, batchtools, biocparallel, cran, distributed-computing, foreach, hpc, hpc-clusters, package, parallel, plyr, r
- Language: R
- Homepage: https://doFuture.futureverse.org
- Size: 802 KB
- Stars: 83
- Watchers: 5
- Forks: 6
- Open Issues: 7
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
Lists
- jimsghstars - HenrikBengtsson/doFuture - :rocket: R package: doFuture - Use Foreach to Parallelize via Future Framework (R)
README
# doFuture: Use Foreach to Parallelize via the Future Framework
## Introduction
The **[future]** package provides a generic API for using futures in
R. A future is a simple yet powerful mechanism to evaluate an R
expression and retrieve its value at some point in time. Futures can
be resolved in many different ways depending on which strategy is
used. There are various types of synchronous and asynchronous futures
to choose from in the **[future]** package. Additional future
backends are implemented in other packages. For instance, the
**[future.batchtools]** package provides futures for _any_ type of
backend that the **[batchtools]** package supports. For an
introduction to futures in R, please consult the vignettes of the
**[future]** package.
The **[foreach]** package implements a map-reduce API with functions
`foreach()` and `times()` that provides us with powerful methods for
iterating over one or more sets of elements with the option to do it
in parallel.
## Two alternatives
The **[doFuture]** package provides two alternatives for using futures
with **foreach**:
1. `y <- foreach(...) %dofuture% { ... }`
1. `registerDoFuture()` + `y <- foreach(...) %dopar% { ... }`.
### Alternative 1: `%dofuture%`
The _first alternative_ (recommended), which uses `%dofuture%`, avoids
having to use `registerDoFuture()`. The `%dofuture%` operator
provides a more consistent behavior than `%dopar%`, e.g. there is a
unique set of foreach arguments instead of one per possible adapter.
Identification of globals, random number generation (RNG), and error
handling is handled by the future ecosystem, just like with other
map-reduce solutions such as **[future.apply]** and **[furrr]**. An
example is:
```r
library(doFuture)
plan(multisession)
y <- foreach(x = 1:4, y = 1:10) %dofuture% {
z <- x + y
slow_sqrt(z)
}
```
This alternative is the recommended way to let `foreach()` parallelize
via the future framework, especially if you start out from scratch.
See `help("%dofuture%", package = "doFuture")` for more details and
examples on this approach.
### Alternative 2: `registerDoFuture()` + `%dopar%`
The _second alternative_ is based on the traditional **foreach**
approach where one registers a foreach adapter to be used by `%dopar%`.
A popular adapter is `doParallel::registerDoParallel()`, which
parallelizes on the local machine using the **parallel** package.
This package provides `registerDoFuture()`, which parallelizes using
the **future** package, meaning any future-compliant parallel backend
can be used.
An example is:
```r
library(doFuture)
registerDoFuture()
plan(multisession)
y <- foreach(x = 1:4, y = 1:10) %dopar% {
z <- x + y
slow_sqrt(z)
}
```
This alternative is useful if you already have a lot of R code that
uses `%dopar%` and you just want to switch to using the future
framework for parallelization. Using `registerDoFuture()` is also
useful when you wish to use the future framework with packages and
functions that uses `foreach()` and `%dopar%` internally,
e.g. **[caret]**, **[plyr]**, **[NMF]**, and **[glmnet]**. It can
also be used to configure the Bioconductor **[BiocParallel]** package,
and any package that rely on it, to parallelize via the future
framework.
See `help("registerDoFuture", package = "doFuture")` for more details
and examples on this approach.
[doFuture]: https://doFuture.futureverse.org
[future]: https://future.futureverse.org
[foreach]: https://cran.r-project.org/package=foreach
[batchtools]: https://cran.r-project.org/package=batchtools
[future.batchtools]: https://future.batchtools.futureverse.org
[future.apply]: https://future.apply.futureverse.org
[furrr]: https://furrr.futureverse.org
[caret]: https://cran.r-project.org/package=caret
[plyr]: https://cran.r-project.org/package=plyr
[NMF]: https://cran.r-project.org/package=NMF
[glmnet]: https://cran.r-project.org/package=glmnet
[BiocParallel]: https://bioconductor.org/packages/BiocParallel/
## Installation
R package doFuture is available on [CRAN](https://cran.r-project.org/package=doFuture) and can be installed in R as:
```r
install.packages("doFuture")
```
### Pre-release version
To install the pre-release version that is available in Git branch `develop` on GitHub, use:
```r
remotes::install_github("HenrikBengtsson/doFuture", ref="develop")
```
This will install the package from source.
## Contributing
To contribute to this package, please see [CONTRIBUTING.md](CONTRIBUTING.md).