( 未来 )
Minimalist Async Evaluation Framework
for R
High-performance parallel code execution and
distributed computing.
Designed for simplicity, a ‘mirai’
evaluates an R expression asynchronously, on local or network resources,
resolving automatically upon completion.
Modern networking
and concurrency built on nanonext and NNG (Nanomsg Next Gen) ensures
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
Use mirai()
to evaluate an expression asynchronously in
a separate, clean R process.
A ‘mirai’ object is returned immediately.
library(mirai)
<- list(x = 2, y = 5, z = double(1e8))
input
<- mirai(
m
{<- rnorm(1e6, mean = mean, sd = sd)
res max(res) - min(res)
},mean = input$x,
sd = input$y
)
Above, all name = value
pairs are passed through to the
mirai via the ...
argument.
Whilst the async operation is ongoing, attempting to access the data yields an ‘unresolved’ logical NA.
m#> < mirai [] >
$data
m#> 'unresolved' logi NA
To check whether a mirai has resolved:
unresolved(m)
#> [1] TRUE
To wait for and collect the evaluated result, use the mirai’s
[]
method:
m[]#> [1] 48.09123
It is not necessary to wait, as the mirai resolves automatically
whenever the async operation completes, the evaluated result then
available at $data
.
m#> < mirai [$data] >
$data
m#> [1] 48.09123
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.
The mirai vignette 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
.
Install the latest release from CRAN or R-multiverse:
install.packages("mirai")
The current development version is available from R-universe:
install.packages("mirai", repos = "https://shikokuchuo.r-universe.dev")
◈ mirai R package: https://shikokuchuo.net/mirai/
◈ nanonext R
package: https://shikokuchuo.net/nanonext/
mirai is listed in CRAN High Performance Computing Task View:
https://cran.r-project.org/view=HighPerformanceComputing
–
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.