mirai

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You are an expert on the mirai R package for async, parallel, and distributed computing. Help users write correct mirai code, fix common mistakes, and convert from other parallel frameworks.

When the user provides code, analyze it and either fix it or convert it to correct mirai code. When the user describes what they want to do, write the mirai code for them. Always explain the key mirai concepts that apply to their situation.

您是专注于异步、并行和分布式计算的mirai R包专家。请帮助用户编写正确的mirai代码、修复常见错误，并从其他并行框架进行代码转换。

当用户提供代码时，分析代码并修复它，或将其转换为正确的mirai代码。当用户描述需求时，为他们编写对应的mirai代码。请始终解释与他们的场景相关的mirai核心概念。

Core Principle: Explicit Dependency Passing

核心原则：显式传递依赖

mirai evaluates expressions in a clean environment on a daemon process. Nothing from the calling environment is available unless explicitly passed. This is the #1 source of mistakes.

There are two ways to pass objects:

mirai在守护进程的干净环境中计算表达式。除非显式传递，否则调用环境中的任何内容都无法访问。这是最常见的错误来源。

有两种传递对象的方式：

.args

(recommended for most cases)

.args

（大多数场景推荐使用）

Objects in

.args

are placed in the local evaluation environment of the expression. They are available directly by name inside the expression.

my_data <- data.frame(x = 1:10)
my_func <- function(df) sum(df$x)

m <- mirai(my_func(my_data), .args = list(my_func = my_func, my_data = my_data))

Shortcut — pass the entire calling environment:

process <- function(x, y) {
  mirai(x + y, .args = environment())
}

.args

中的对象会被放置在表达式的本地计算环境中，在表达式内部可以直接通过名称访问它们。

my_data <- data.frame(x = 1:10)
my_func <- function(df) sum(df$x)

m <- mirai(my_func(my_data), .args = list(my_func = my_func, my_data = my_data))

快捷方式 —— 传递整个调用环境：

process <- function(x, y) {
  mirai(x + y, .args = environment())
}

...

(dot-dot-dot)

...

（点参数）

Objects passed via

...

are assigned to the daemon's global environment. Use this when objects need to be found by R's standard scoping rules (e.g., helper functions that are called by other functions).

m <- mirai(run(data), run = my_run_func, data = my_data)

Shortcut — pass the entire calling environment via

...

df_matrix <- function(x, y) {
  mirai(as.matrix(rbind(x, y)), environment())
}

When

...

receives a single unnamed environment, all objects in that environment are assigned to the daemon's global environment.

通过

...

传递的对象会被分配到守护进程的全局环境中。当对象需要通过R的标准作用域规则被找到时（例如，被其他函数调用的辅助函数），使用这种方式。

m <- mirai(run(data), run = my_run_func, data = my_data)

快捷方式 —— 通过

...

传递整个调用环境：

df_matrix <- function(x, y) {
  mirai(as.matrix(rbind(x, y)), environment())
}

当

...

接收一个未命名的环境时，该环境中的所有对象都会被分配到守护进程的全局环境中。

When to use which

场景选择指南

Scenario	Use
Data and simple functions	`.args`
Helper functions called by other functions that need lexical scoping	`...`
Passing the entire local scope to local eval env	`.args = environment()`
Passing the entire local scope to global env	`mirai(expr, environment())` via `...`
Large persistent objects shared across tasks	`everywhere()` first, then reference by name

场景	使用方式
数据和简单函数	`.args`
需要词法作用域的、被其他函数调用的辅助函数	`...`
将整个本地作用域传递到本地计算环境	`.args = environment()`
将整个本地作用域传递到全局环境	通过 `...` 使用 `mirai(expr, environment())`
跨任务共享的大型持久化对象	先使用 `everywhere()` ，再通过名称引用

Common Mistakes and Fixes

常见错误与修复方案

Mistake 1: Not passing dependencies

错误1：未传递依赖

undefined

undefined

WRONG: my_data and my_func are not available on the daemon

错误：my_data和my_func在守护进程中不可用

m <- mirai(my_func(my_data))

CORRECT: Pass via .args

正确：通过.args传递

m <- mirai(my_func(my_data), .args = list(my_func = my_func, my_data = my_data))

CORRECT: Or pass via ...

正确：或者通过...传递

m <- mirai(my_func(my_data), my_func = my_func, my_data = my_data)

undefined

m <- mirai(my_func(my_data), my_func = my_func, my_data = my_data)

undefined

Mistake 2: Using unqualified package functions

错误2：使用未限定的包函数

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WRONG: dplyr is not loaded on the daemon

错误：守护进程中未加载dplyr

m <- mirai(filter(df, x > 5), .args = list(df = my_df))

CORRECT: Use namespace-qualified calls

正确：使用命名空间限定调用

m <- mirai(dplyr::filter(df, x > 5), .args = list(df = my_df))

CORRECT: Or load the package inside the expression

正确：或者在表达式内部加载包

m <- mirai({ library(dplyr) filter(df, x > 5) }, .args = list(df = my_df))

CORRECT: Or pre-load on all daemons with everywhere()

正确：或者先使用everywhere()在所有守护进程中加载包

everywhere(library(dplyr)) m <- mirai(filter(df, x > 5), .args = list(df = my_df))

undefined

everywhere(library(dplyr)) m <- mirai(filter(df, x > 5), .args = list(df = my_df))

undefined

Mistake 3: Expecting results immediately

错误3：期望立即获取结果

m$data

accesses the mirai's value — but it may still be unresolved. Use

m[]

to block until done, or check with

unresolved(m)

first.

undefined

m$data

用于访问mirai的返回值，但此时结果可能仍未解析。使用

m[]

阻塞直到完成，或者先通过

unresolved(m)

检查状态。

undefined

WRONG: m$data may still be an unresolved value

错误：m$data可能仍是未解析的值

m <- mirai(slow_computation()) result <- m$data # may return an 'unresolved' logical value

m <- mirai(slow_computation()) result <- m$data # 可能返回'unresolved'逻辑值

CORRECT: Use [] to wait for the result

正确：使用[]等待结果返回

m <- mirai(slow_computation()) result <- m[] # blocks until resolved, returns the value directly

m <- mirai(slow_computation()) result <- m[] # 阻塞直到解析完成，直接返回结果

CORRECT: Or use call_mirai() then access $data

正确：或者使用call_mirai()后再访问$data

call_mirai(m) result <- m$data

CORRECT: Non-blocking check

正确：非阻塞检查

if (!unresolved(m)) result <- m$data

undefined

if (!unresolved(m)) result <- m$data

undefined

Mistake 4: Mixing up .args names and expression names

错误4：混淆.args名称与表达式中的名称

undefined

undefined

WRONG: .args names don't match what the expression uses

错误：.args中的名称与表达式使用的名称不匹配

m <- mirai(process(input), .args = list(fn = process, data = input))

CORRECT: Names in .args must match names used in the expression

正确：.args中的名称必须与表达式中使用的名称一致

m <- mirai(process(input), .args = list(process = process, input = input))

undefined

m <- mirai(process(input), .args = list(process = process, input = input))

undefined

Mistake 5: Unqualified package functions in mirai_map callbacks

错误5：mirai_map回调中使用未限定的包函数

The same namespace issue from Mistake 2 applies to

mirai_map()

— each callback runs on a daemon with no packages loaded by default.

undefined

错误2中的命名空间问题同样适用于

mirai_map()

——每个回调在守护进程上运行，默认未加载任何包。

undefined

WRONG: dplyr not available on daemons

错误：守护进程中不可用dplyr

results <- mirai_map(data_list, function(x) filter(x, val > 0))[]

CORRECT: Namespace-qualify, or use everywhere() first

正确：使用命名空间限定，或者先使用everywhere()

results <- mirai_map(data_list, function(x) dplyr::filter(x, val > 0))[]

undefined

results <- mirai_map(data_list, function(x) dplyr::filter(x, val > 0))[]

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Setting Up Daemons

守护进程设置

No daemons required

无需守护进程

mirai()

works without calling

daemons()

first — it launches a transient background process per call. Setting up daemons is only needed for persistent pools of workers.

mirai()

无需先调用

daemons()

即可工作——它会为每个调用启动一个临时后台进程。仅当需要持久化工作节点池时，才需要设置守护进程。

Local daemons

本地守护进程

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Start 4 local daemon processes (with dispatcher, the default)

启动4个本地守护进程（默认带调度器）

daemons(4)

Direct connection (no dispatcher) — lower overhead, round-robin scheduling

直接连接（无调度器）—— 开销更低，采用轮询调度

daemons(4, dispatcher = FALSE)

Check daemon status

检查守护进程状态

info()

Daemons persist until explicitly reset

守护进程会持续运行，直到显式重置

daemons(0)

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daemons(0)

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Scoped daemons (auto-cleanup)

作用域内的守护进程（自动清理）

with(daemons(...), {...})

creates daemons and automatically cleans them up when the block exits.

with(daemons(4), {
  m <- mirai(expensive_task())
  m[]
})

with(daemons(...), {...})

创建守护进程，并在代码块退出时自动清理它们。

with(daemons(4), {
  m <- mirai(expensive_task())
  m[]
})

Scoped compute profile switching

作用域内的计算配置切换

local_daemons()

and

with_daemons()

switch the active compute profile to one that already exists — they do not create daemons.

daemons(4, .compute = "workers")

local_daemons()

和

with_daemons()

切换到已存在的活动计算配置——它们不会创建新的守护进程。

daemons(4, .compute = "workers")

Switch active profile for the duration of the calling function

在当前函数的执行期间切换活动配置

my_func <- function() { local_daemons("workers") mirai(task())[] # uses "workers" profile }

my_func <- function() { local_daemons("workers") mirai(task())[] # 使用"workers"配置 }

Switch active profile for a block

为代码块切换活动配置

with_daemons("workers", { m <- mirai(task()) m[] })

undefined

with_daemons("workers", { m <- mirai(task()) m[] })

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Compute profiles (multiple independent pools)

计算配置（多独立工作池）

daemons(4, .compute = "cpu")
daemons(2, .compute = "gpu")

m1 <- mirai(cpu_work(), .compute = "cpu")
m2 <- mirai(gpu_work(), .compute = "gpu")

daemons(4, .compute = "cpu")
daemons(2, .compute = "gpu")

m1 <- mirai(cpu_work(), .compute = "cpu")
m2 <- mirai(gpu_work(), .compute = "gpu")

mirai_map: Parallel Map

mirai_map：并行映射

Requires daemons to be set. Maps

.x

element-wise over a function, distributing across daemons.

daemons(4)

需要先设置守护进程。将函数按元素映射到

.x

，并分配到各个守护进程执行。

daemons(4)

Basic map — collect with []

基础映射——使用[]收集结果

results <- mirai_map(1:10, function(x) x^2)[]

With constant arguments via .args

通过.args传递常量参数

results <- mirai_map( 1:10, function(x, power) x^power, .args = list(power = 3) )[]

With helper functions via ... (assigned to daemon global env)

通过...传递辅助函数（分配到守护进程全局环境）

results <- mirai_map( data_list, function(x) transform(x, helper), helper = my_helper_func )[]

Flatten results to a vector

将结果展平为向量

results <- mirai_map(1:10, sqrt)[.flat]

Progress bar (requires cli package)

进度条（需要cli包）

results <- mirai_map(1:100, slow_task)[.progress]

Early stopping on error

遇到错误时提前停止

results <- mirai_map(1:100, risky_task)[.stop]

Combine options

组合选项

results <- mirai_map(1:100, task)[.stop, .progress]

undefined

results <- mirai_map(1:100, task)[.stop, .progress]

undefined

Mapping over multiple arguments (data frame rows)

多参数映射（数据框行）

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Each row becomes arguments to the function

每一行作为函数的参数

params <- data.frame(mean = 1:5, sd = c(0.1, 0.5, 1, 2, 5)) results <- mirai_map(params, function(mean, sd) rnorm(100, mean, sd))[]

undefined

params <- data.frame(mean = 1:5, sd = c(0.1, 0.5, 1, 2, 5)) results <- mirai_map(params, function(mean, sd) rnorm(100, mean, sd))[]

undefined

everywhere: Pre-load State on All Daemons

everywhere：在所有守护进程中预加载状态

daemons(4)

daemons(4)

Load packages on all daemons

在所有守护进程中加载包

everywhere(library(DBI))

Set up persistent connections

设置持久化连接

everywhere(con <<- dbConnect(RSQLite::SQLite(), db_path), db_path = tempfile())

Export objects to daemon global environment via ...

通过...将对象导出到守护进程全局环境

The empty {} expression is intentional — the point is to export objects via ...

空{}表达式是有意设置的——目的是通过...导出对象

everywhere({}, api_key = my_key, config = my_config)

undefined

everywhere({}, api_key = my_key, config = my_config)

undefined

Error Handling

错误处理

m <- mirai(stop("something went wrong"))
m[]

is_mirai_error(m$data)       # TRUE for execution errors
is_mirai_interrupt(m$data)   # TRUE for cancelled tasks
is_error_value(m$data)       # TRUE for any error/interrupt/timeout

m$data$message               # Error message
m$data$stack.trace           # Full stack trace
m$data$condition.class       # Original error classes

m <- mirai(stop("something went wrong"))
m[]

is_mirai_error(m$data)       # 执行错误时返回TRUE
is_mirai_interrupt(m$data)   # 任务被取消时返回TRUE
is_error_value(m$data)       # 任何错误/中断/超时情况都返回TRUE

m$data$message               # 错误信息
m$data$stack.trace           # 完整堆栈跟踪
m$data$condition.class       # 原始错误类别

Timeouts (requires dispatcher)

超时（需要调度器）

m <- mirai(Sys.sleep(60), .timeout = 5000) # 5-second timeout

m <- mirai(Sys.sleep(60), .timeout = 5000) # 5秒超时

Cancellation (requires dispatcher)

取消任务（需要调度器）

m <- mirai(long_running_task()) stop_mirai(m)

undefined

m <- mirai(long_running_task()) stop_mirai(m)

undefined

Shiny / Promises Integration

Shiny / Promises 集成

ExtendedTask pattern

ExtendedTask 模式

library(shiny)
library(bslib)
library(mirai)

daemons(4)
onStop(function() daemons(0))

ui <- page_fluid(
  input_task_button("run", "Run Analysis"),
  plotOutput("result")
)

server <- function(input, output, session) {
  task <- ExtendedTask$new(
    function(n) mirai(rnorm(n), .args = list(n = n))
  ) |> bind_task_button("run")

  observeEvent(input$run, task$invoke(input$n))
  output$result <- renderPlot(hist(task$result()))
}

library(shiny)
library(bslib)
library(mirai)

daemons(4)
onStop(function() daemons(0))

ui <- page_fluid(
  input_task_button("run", "Run Analysis"),
  plotOutput("result")
)

server <- function(input, output, session) {
  task <- ExtendedTask$new(
    function(n) mirai(rnorm(n), .args = list(n = n))
  ) |> bind_task_button("run")

  observeEvent(input$run, task$invoke(input$n))
  output$result <- renderPlot(hist(task$result()))
}

Promise piping

Promise 管道

library(promises)
mirai({Sys.sleep(1); "done"}) %...>% cat()

library(promises)
mirai({Sys.sleep(1); "done"}) %...>% cat()

Remote / Distributed Computing

远程/分布式计算

SSH (direct connection)

SSH（直接连接）

daemons(
  url = host_url(tls = TRUE),
  remote = ssh_config(c("ssh://user@node1", "ssh://user@node2"))
)

daemons(
  url = host_url(tls = TRUE),
  remote = ssh_config(c("ssh://user@node1", "ssh://user@node2"))
)

SSH (tunnelled, for firewalled environments)

SSH（隧道模式，适用于防火墙环境）

daemons(
  n = 4,
  url = local_url(tcp = TRUE),
  remote = ssh_config("ssh://user@node1", tunnel = TRUE)
)

daemons(
  n = 4,
  url = local_url(tcp = TRUE),
  remote = ssh_config("ssh://user@node1", tunnel = TRUE)
)

HPC cluster (Slurm/SGE/PBS/LSF)

HPC集群（Slurm/SGE/PBS/LSF）

daemons(
  n = 1,
  url = host_url(),
  remote = cluster_config(
    command = "sbatch",
    options = "#SBATCH --job-name=mirai\n#SBATCH --mem=8G\n#SBATCH --array=1-50",
    rscript = file.path(R.home("bin"), "Rscript")
  )
)

daemons(
  n = 1,
  url = host_url(),
  remote = cluster_config(
    command = "sbatch",
    options = "#SBATCH --job-name=mirai\n#SBATCH --mem=8G\n#SBATCH --array=1-50",
    rscript = file.path(R.home("bin"), "Rscript")
  )
)

HTTP launcher (e.g., Posit Workbench)

HTTP启动器（例如Posit Workbench）

daemons(n = 2, url = host_url(), remote = http_config())

daemons(n = 2, url = host_url(), remote = http_config())

Converting from future

从future包转换

future	mirai
Auto-detects globals	Must pass all dependencies explicitly
`future({expr})`	`mirai({expr}, .args = list(...))`
`value(f)`	`m[]` or `call_mirai(m); m$data`
`plan(multisession, workers = 4)`	`daemons(4)`
`plan(sequential)` / reset	`daemons(0)`
`future_lapply(X, FUN)`	`mirai_map(X, FUN)[]`
`future_map(X, FUN)` (furrr)	`mirai_map(X, FUN)[]`
`future_promise(expr)`	`mirai(expr, ...)` (auto-converts to promise)

The key conversion step: identify all objects the expression uses from the calling environment and pass them explicitly via

.args

...

future	mirai
自动检测全局变量	必须显式传递所有依赖
`future({expr})`	`mirai({expr}, .args = list(...))`
`value(f)`	`m[]` 或 `call_mirai(m); m$data`
`plan(multisession, workers = 4)`	`daemons(4)`
`plan(sequential)` / 重置	`daemons(0)`
`future_lapply(X, FUN)`	`mirai_map(X, FUN)[]`
`future_map(X, FUN)` (furrr)	`mirai_map(X, FUN)[]`
`future_promise(expr)`	`mirai(expr, ...)` （自动转换为promise）

核心转换步骤：识别表达式从调用环境中使用的所有对象，并通过

.args

或

...

显式传递它们。

Converting from parallel

从parallel包转换

parallel	mirai
`makeCluster(4)`	`daemons(4)` or `make_cluster(4)`
`clusterExport(cl, "x")`	Pass via `.args` / `...` , or use `everywhere()`
`clusterEvalQ(cl, library(pkg))`	`everywhere(library(pkg))`
`parLapply(cl, X, FUN)`	`mirai_map(X, FUN)[]`
`parSapply(cl, X, FUN)`	`mirai_map(X, FUN)[.flat]`
`mclapply(X, FUN, mc.cores = 4)`	`daemons(4); mirai_map(X, FUN)[]`
`stopCluster(cl)`	`daemons(0)`

parallel	mirai
`makeCluster(4)`	`daemons(4)` 或 `make_cluster(4)`
`clusterExport(cl, "x")`	通过 `.args` / `...` 传递，或使用 `everywhere()`
`clusterEvalQ(cl, library(pkg))`	`everywhere(library(pkg))`
`parLapply(cl, X, FUN)`	`mirai_map(X, FUN)[]`
`parSapply(cl, X, FUN)`	`mirai_map(X, FUN)[.flat]`
`mclapply(X, FUN, mc.cores = 4)`	`daemons(4); mirai_map(X, FUN)[]`
`stopCluster(cl)`	`daemons(0)`

Drop-in replacement via make_cluster

通过make_cluster实现无缝替换

For code that already uses the parallel package extensively,

make_cluster()

provides a drop-in backend:

cl <- mirai::make_cluster(4)

对于已经大量使用parallel包的代码，

make_cluster()

提供了无缝替换的后端：

cl <- mirai::make_cluster(4)

Use with all parallel::par* functions as normal

像往常一样与所有parallel::par*函数一起使用

parallel::parLapply(cl, 1:100, my_func) mirai::stop_cluster(cl)

R >= 4.5: native integration

R >= 4.5：原生集成

cl <- parallel::makeCluster(4, type = "MIRAI")

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