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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
eval = FALSE,
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# crew: a distributed worker launcher framework <img src='man/figures/logo-readme.png' align="right" height="139"/>
<!-- badges: start --->
[](https://CRAN.R-project.org/package=crew)
[](https://www.repostatus.org/#active)
[](https://github.com/wlandau/crew/actions?query=workflow%3Acheck)
[](https://app.codecov.io/gh/wlandau/crew)
[](https://github.com/wlandau/crew/actions?query=workflow%3Alint)
[](https://github.com/wlandau/crew/actions?query=workflow%3Apkgdown)
<!-- badges: end --->
In computationally demanding analysis projects, statisticians and data scientists asynchronously deploy long-running tasks to distributed systems, ranging from traditional clusters to cloud services. The [NNG](https://nng.nanomsg.org)-powered [`mirai`](https://github.com/shikokuchuo/mirai) R package is a sleek and sophisticated scheduler that efficiently processes these intense workloads. The `crew` package extends [`mirai`](https://github.com/shikokuchuo/mirai) with a unifying interface for third-party worker launchers. Inspiration also comes from packages [`future`](https://future.futureverse.org/), [`rrq`](https://mrc-ide.github.io/rrq/), [`clustermq`](https://mschubert.github.io/clustermq/), and [`batchtools`](https://mllg.github.io/batchtools/).
# 📣 Request for community contributions 📣
**The [launcher plugin framework](https://wlandau.github.io/crew/articles/plugins.html) aims to extend `crew` to modern platforms and services like [Google Cloud Run](https://cloud.google.com/run/), [Kubernetes](https://kubernetes.io/), and beyond. At the time of writing, plugins for cloud computing do not yet exist. So if you have access to these services and know how to use them, please consider contributing a package with plugins of your own. The tutorial at <https://wlandau.github.io/crew/articles/plugins.html> explains how. The maintainer of `crew` would love to work with you!**
# Installation
Type | Source | Command
---|---|---
Release | CRAN | `install.packages("crew")`
Development | GitHub | `remotes::install_github("wlandau/crew")`
Development | R-universe | `install.packages("crew", repos = "https://wlandau.r-universe.dev")`
# Documentation
The documentation website at <https://wlandau.github.io/crew/> includes a [function reference](https://wlandau.github.io/crew/reference/index.html) and tutorial vignettes linked below.
1. [Introduction to `crew`](https://wlandau.github.io/crew/articles/introduction.html)
1. [Controller groups](https://wlandau.github.io/crew/articles/groups.html)
1. [Integration with Shiny](https://wlandau.github.io/crew/articles/shiny.html)
1. [How to write launcher plugins](https://wlandau.github.io/crew/articles/plugins.html)
1. [Known risks of `crew`](https://wlandau.github.io/crew/articles/risks.html)
# Risks
The `crew` package has unavoidable risks, and the user is responsible for safety, security, and computational resources. Please read the [software license](https://wlandau.github.io/crew/LICENSE.html) and the [vignette about specific known risks](https://wlandau.github.io/crew/articles/risks.html).
# Similar work
* [`mirai`](https://github.com/shikokuchuo/mirai): a powerful R framework for asynchronous tasks built on [NNG](https://nng.nanomsg.org). The purpose of `crew` is to extend [`mirai`](https://github.com/shikokuchuo/mirai) to different computing platforms for distributed workers.
* [`rrq`](https://mrc-ide.github.io/rrq/): a task queue for R based on [Redis](https://redis.io).
* [`rrqueue`](http://traitecoevo.github.io/rrqueue/): predecessor of [`rrq`](https://mrc-ide.github.io/rrq/).
* [`clustermq`](https://mschubert.github.io/clustermq/): sends R function calls as jobs to computing clusters.
* [`future`](https://future.futureverse.org/): a unified interface for asynchronous evaluation of single tasks and map-reduce calls on a wide variety of backend technologies.
* [`batchtools`](https://mllg.github.io/batchtools/): tools for computation on batch systems.
* [`targets`](https://docs.ropensci.org/targets/): a Make-like pipeline tool for R.
* [`later`](https://r-lib.github.io/later/): delayed evaluation of synchronous tasks.
* [`promises`](https://rstudio.github.io/promises/): minimally-invasive asynchronous programming for a small number of tasks within Shiny apps.
* [`callr`](https://github.com/r-lib/callr): initiates R process from other R processes.
* [High-performance computing CRAN task view](https://CRAN.R-project.org/view=HighPerformanceComputing).
# Thanks
The `crew` package incorporates insightful ideas from the following people.
* [Charlie Gao](https://github.com/shikokuchuo) created [`mirai`](https://github.com/shikokuchuo/mirai) and [`nanonext`](https://github.com/shikokuchuo/nanonext) and graciously accommodated the complicated and demanding feature requests that made `crew` possible.
* [Rich FitzJohn](https://github.com/richfitz) and [Robert Ashton](https://github.com/r-ash) developed [`rrq`](https://mrc-ide.github.io/rrq//).
* [Gábor Csárdi](https://github.com/gaborcsardi/) developed [`callr`](https://github.com/r-lib/callr) and wrote an [edifying blog post on implementing task queues](https://www.tidyverse.org/blog/2019/09/callr-task-q/).
* [Kirill Müller](https://github.com/krlmlr/) created the [`workers`](https://github.com/wlandau/workers) prototype, an initial effort that led directly to the current implementation of `crew`. `crew` would not exist without Kirill's insights about orchestration models for R processes.
* [Henrik Bengtsson](https://github.com/futureverse/). Henrik's [`future`](https://github.com/futureverse/future/) package ecosystem demonstrates the incredible power of a consistent R interface on top of a varying collection of high-performance computing technologies.
* [Michael Schubert](https://github.com/mschubert/). Michael's [`clustermq`](https://mschubert.github.io/clustermq/) package supports efficient high-performance computing on traditional clusters, and it demonstrates the value of a central `R6` object to manage an entire collection of persistent workers.
* [David Kretch](https://github.com/davidkretch). The [`paws`](https://github.com/paws-r/paws) R package is a powerful interface to Amazon Web Services, and the documentation clearly communicates the capabilities and limitations of AWS to R users.
* [Adam Banker](https://github.com/adambanker), co-authored [`paws`](https://github.com/paws-r/paws) with [David Kretch](https://github.com/davidkretch).
* [David Neuzerling](https://github.com/mdneuzerling). David's [`lambdr`](https://github.com/mdneuzerling/lambdr/) package establishes a helpful pattern to submit and collect AWS Lambda jobs from R.
* [Mark Edmondson](https://github.com/MarkEdmondson1234/). Mark maintains several R packages to interface with Google Cloud Platform such as [`googleCloudStorageR`](https://github.com/cloudyr/googleCloudStorageR) and [`googleCloudRunner`](https://github.com/MarkEdmondson1234/googleCloudRunner), and he [started the conversation](https://github.com/ropensci/targets/issues/720) around helping [`targets`](https://github.com/ropensci/targets) submit jobs to Google Cloud Run.
* [Joe Cheng](https://github.com/jcheng5) for sparking the integration of `crew` with [`promises`](https://rstudio.github.io/promises/).
# Code of Conduct
Please note that the `crew` project is released with a [Contributor Code of Conduct](https://github.com/wlandau/crew/blob/main/CODE_OF_CONDUCT.md). By contributing to this project, you agree to abide by its terms.
# Citation
```{r, eval = FALSE, warning = FALSE, comment = ""}
To cite package ‘crew’ in publications use:
Landau WM (2023). _crew: A Distributed Worker Launcher Framework_.
https://wlandau.github.io/crew/, https://github.com/wlandau/crew.
A BibTeX entry for LaTeX users is
@Manual{,
title = {crew: A Distributed Worker Launcher Framework},
author = {William Michael Landau},
year = {2023},
note = {https://wlandau.github.io/crew/, https://github.com/wlandau/crew},
}
```