( 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
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")
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$data
#> [1] -0.5767602 1.6767432 2.5189824 1.8092596 5.9579094 0.1678441
#> [7] 0.5527123 0.3969857 0.5963942 -1.7338229
Alternatively, explicitly call and wait for the result using
call_mirai()
.
call_mirai(m)$data
#> [1] -0.5767602 1.6767432 2.5189824 1.8092596 5.9579094 0.1678441
#> [7] 0.5527123 0.3969857 0.5963942 -1.7338229
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")
The following core integrations are documented, with usage examples in the linked vignettes:
Provides an alternative communications backend for R, implementing a
low-level feature request by R-Core at R Project Sprint 2023.
‘miraiCluster’ may also be used with foreach
, which is supported via
doParallel
.
Implements the next generation of completely event-driven, non-polling
promises. ‘mirai’ may be used interchageably with ‘promises’, including
with the promise pipe %...>%
.
Asynchronous parallel / distributed backend, supporting the next level of responsiveness and scalability for Shiny. Launches ExtendedTasks, or plugs directly into the reactive framework for advanced uses.
Asynchronous parallel / distributed backend, capable of scaling Plumber applications in production usage.
Allows queries using the Apache Arrow format to be handled seamlessly over ADBC database connections hosted in daemon processes.
Allows Torch tensors and complex objects such as models and optimizers to be used seamlessly across parallel processes.
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.
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, and prototyping
non-polling promises, which is implemented across nanonext
and
mirai
.
Luke Tierney of R Core, for discussion
on L’Ecuyer-CMRG streams to ensure statistical independence in parallel
processing, and making it possible for mirai
to be the first
‘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.
Kirill Müller for discussion on using ‘daemons’ to host Arrow database connections.
for funding work on the TLS implementation in nanonext
, used to
provide secure connections in mirai
.
◈ 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/
–
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.