mirai

みらい

( 未来 )

Minimalist Async Evaluation Framework for R

Lightweight parallel code execution and distributed computing.

mirai() returns a ‘mirai’ object immediately. Designed for simplicity, a ‘mirai’ evaluates an R expression asynchronously, on local or network resources, resolving automatically upon completion.

State of the art networking and concurrency via nanonext offers reliable and efficient scheduling over fast inter-process communications or TCP/IP secured by TLS.

mirai パッケージを試してみたところ、かなり速くて驚きました

Installation

Install the latest release from CRAN:

install.packages("mirai")

Or the development version from R-universe:

install.packages("mirai", repos = "https://shikokuchuo.r-universe.dev")

Quick Start

Use mirai() to evaluate an expression asynchronously in a separate, clean R process.

A ‘mirai’ object is returned immediately.

library(mirai)

m <- mirai(
  {
    res <- rnorm(x) + y ^ 2
    res / rev(res)
  },
  x = 10,
  y = runif(1)
)

m
#> < mirai | $data >

Above, all specified name = value pairs are passed through to the ‘mirai’.

The ‘mirai’ yields an ‘unresolved’ logical NA whilst the async operation is ongoing.

m$data
#> 'unresolved' logi NA

To check whether a mirai has resolved:

unresolved(m)
#> [1] FALSE

Upon completion, the ‘mirai’ resolves automatically to the evaluated result.

m$data
#>  [1]  2.4181337  1.8674723  6.6228207 -1.0355541  0.8055614  1.2413703
#>  [7] -0.9656666  0.1509931  0.5354832  0.4135421

Alternatively, explicitly call and wait for the result using call_mirai().

call_mirai(m)$data
#>  [1]  2.4181337  1.8674723  6.6228207 -1.0355541  0.8055614  1.2413703
#>  [7] -0.9656666  0.1509931  0.5354832  0.4135421

Daemons

Daemons are persistent background processes created to receive ‘mirai’ requests.

They may be deployed for:

Local parallel processing; or

Remote network distributed computing.

Launchers allow daemons to be started both on the local machine and across the network via SSH etc.

Secure TLS connections can be automatically-configured on-the-fly for remote daemon connections.

Refer to the {mirai} vignette for full package functionality. This may be accessed within R by:

vignette("mirai", package = "mirai")

Integrations

The following core integrations are documented, with usage examples in the linked vignettes:

parallel - provides an alternative communications backend for R, implementing a low-level feature request by R-Core at R Project Sprint 2023.

promises - ‘mirai’ may be used interchangeably with ‘promises’ by using the promise pipe %...>% or the as.promise() method.

plumber - serves as an asynchronous / distributed backend, scaling applications via the use of promises.

shiny - serves as an asynchronous / distributed backend, plugging directly into the reactive framework or accepted anywhere that takes a ‘promise’.

torch - the custom serialization interface allows tensors and complex objects such as models and optimizers to be used seamlessly across parallel processes.

Powering Crew and Targets High Performance Computing

targets, a Make-like pipeline tool for statistics and data science, has integrated and adopted crew as its default high-performance computing backend.

crew is a distributed worker-launcher extending mirai to different distributed computing platforms, from traditional clusters to cloud services.

crew.cluster enables mirai-based workflows on traditional high-performance computing clusters using LFS, PBS/TORQUE, SGE and SLURM.

crew.aws.batch extends mirai to cloud computing using AWS Batch.

Thanks

We would like to thank in particular:

Will Landau, for being instrumental in shaping development of the package, from initiating the original request for persistent daemons, through to orchestrating robustness testing for the high performance computing requirements of crew and targets.

Joe Cheng, for optimising the promises method to make mirai work seamlessly within Shiny, prototyping non-polling promises and guidance on implementing error stack traces.

Luke Tierney, R Core, for discussion on R’s implementation of L’Ecuyer-CMRG streams, used to ensure statistical independence in parallel processing, and collaboration in ‘providing an alternative communications backend for R’.

Henrik Bengtsson, for valuable insights leading to the interface accepting broader usage patterns.

Daniel Falbel, for discussion around an efficient solution to serialization and transmission of torch tensors.

Links

◈ mirai R package: https://shikokuchuo.net/mirai/

mirai is listed in CRAN Task View:
- High Performance Computing: https://cran.r-project.org/view=HighPerformanceComputing

◈ nanonext R package: https://shikokuchuo.net/nanonext/

NNG website: https://nng.nanomsg.org/

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Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.