NEWS.md
Add localPlan(<plan>) and withPlan(<plan>, <expression>) methods to evaluate an expression, including futures, using a temporary future plan.
Add interrupt(), which interrupts a future, if the parallel backend supports it, otherwise it is silently ignored.
Add reset(), which resets a future that has completed, failed, or been interrupted. The future is reset back to a lazy, vanilla future that can be relaunched.
Add minifuture(), which is like future(), but with different default arguments resulting in less overhead with the added burden on having to specify globals and packages, not having conditions and standard output relayed, and ignoring random number generation.
Interrupted futures are now handled and produce an informative error.
All parallel backends now prevent nested parallelization, unless explicitly allowed, e.g. settings recognized by parallelly::availableCores() or set by the future plan(). Previously, this had to be implemented by each backend, but now it’s handled automatically by the future framework.
Add new FutureBackend API for writing future backends.
Added argument maxSizeOfObjects to future(), which controls the maximum total size of objects send to and from the worker. The default value can be controlled when setting up the future backend, e.g. plan(multisession, maxSizeOfObjects = 1000^3).
The default value maxSizeOfObjects is now configured per backend, and if not set there, by R option future.globals.maxSize (sic!). Backends sequential and multicore has no such limit, i.e. maxSizeOfObjects = +Inf.
FutureInterruptError. Not all backends manage to catch interrupts, leading to the parallel R workers to terminate, resulting in a regular FutureError. Previously, interrupts would result in un-determistic behavior and errors depending of future backend.future::makeClusterPSOCK() still works, whereas parallelly::makeClusterPSOCK() is the preferred use. The long-term goal is to clean out these re-exports. Starting with this release, the future package no longer re-exports autoStopCluster(), makeClusterMPI(), makeNodePSOCK().cleanup attribute, cf. attr(cluster, "cleanup").parallelly::serializedSize().resolved() for ClusterFuture:s would produce Error: 'inherits(future, "Future")' is not TRUE instead of an intended, informative error message that the connection to the parallel worker is broken.workers argument. For example, plan(cluster, workers = cl), where cl is a cluster object, would come with an extra overhead, because the workers object was unnecessarily transferred to the cluster nodes.getExpression() on ‘cluster’ future could under some circumstances call local() on the global search path rather than base::local() as intended. For example, if a package that exports its own local() function was attached, then that would be called instead, often leading to a hard-to-troubleshoot error.Add prototype of an internal event-logging framework for the purpose of profiling futures and their backends.
Add option future.globalenv.onMisuse for optionally asserting that a future expression does not result in variables being added to the global environment.
Add option future.onFutureCondition.keepFuture for controlling whether FutureCondition objects should keep a copy of the Future object or not. The default is to keep a copy, but if the future carries large global objects, then the FutureCondition will also be large, which can result in memory issues and slow downs.
remote(). Note that plan(remote, ...) has been deprecated since future 1.24.0 [2022-02-19] and defunct since future 1.30.0 (2022-12-15).Deprecated plan(multiprocess, ...) now equals plan(sequential), while still producing one warning each time a future is created.
Argument local is defunct and has been removed. Previously only local = FALSE was defunct.
Remove defunct argument value from all resolve() methods.
Remove defunct functions transparent() and TransparentFuture().
futureOf() used listenv::map(), which is deprecated in listenv (>= 0.9.0) in favor of listenv::mapping().
Starting with R (>= 4.2.0), the internal function myInternalIP() no longer detected when an attempted system call failed, resulting in an obscure error instead of falling back to alternatives. This was because errors produced by system2() no longer inherits from class simpleError.
R CMD check may produce “checking for detritus in the temp directory … NOTE” and how to avoid them.plan(sequential) and when getOption("warn") == 0. This bug was introduced in future 1.26.0 [2022-05-27].Using the deprecated plan(multiprocess) will now trigger a deprecation warning each time a multiprocess future is created. This means that there could be a lot of warnings produced. Note that multiprocess has been deprecated since future 1.20.0 [2020-10-30]. Please use multisession (recommended) or multicore instead.
Removing values(), which has been defunct since future 1.23.0. Use value() instead.
source(..., local = TRUE) is preferred over source() when used inside futures.do.call(plan, args = list(multisession, workers = 2)) would ignore the workers argument, and any other arguments.Previously deprecated use of local = FALSE with futures is now defunct.
The R option to temporarily allow plan(transparent) although it was declared defunct has now been removed; plan(transparent), together with functions transparent() and TransparentFuture() are now formally defunct.
Using argument persistent with multisession futures is now defunct. Previously only persistent = TRUE was defunct.
workers = 1 can now be overridden by specifying workers = I(1).Some warnings and errors showed the wrong call.
print() for FutureResult would report captured conditions all with class list, instead of their condition classes.
R CMD check --as-cran on R-devel and MS Windows would trigger a NOTE on “Check: for detritus in the temp directory” and “Found the following files/directories: ‘Rscript1349cb8aeeba0’ …”. There were two package tests that explicitly created PSOCK cluster without stopping them. A third test launched multisession future without resolving it, which prevented the PSOCK worker to terminate. This was not detected in R 4.2.0. It is not a problem on macOS and Linux, because there background workers are automatically terminated when the main R session terminates.R options and environment variables are now reset on the workers after future is resolved as they were after any packages required by the future has been loaded and attached. Previously, they were reset to what they were before these were loaded and attached. In addition, only pre-existing R options and environment variables are reset. Any new ones added are not removed for now, because we do not know which added R options or environment variables might have been added from loading a package and that are essential for that package to work.
If it was changed while evaluating the future expression, the current working directory is now reset when the future has been resolved.
futureSessionInfo() gained argument anonymize. If TRUE (default), host and user names are anonymized.
futureSessionInfo() now also report on the main R session details.
The bug fix in future 1.22.0 that addressed the issue where object a in future(fcn(), globals = list(a = 42, fcn = function() a)) would not be found has been redesigned in a more robust way.
Use of packages such as data.table and ff in cluster and multisession futures broke in future 1.25.0. For data.table, we saw “Error in setalloccol(ans) : verbose must be TRUE or FALSE”. For ff, we saw “Error in splitted$path[nopath] <- getOption(”fftempdir”) : replacement has length zero”. See ‘Significant Changes’ for why and how this was fixed.
The deprecation warning for using local = FALSE was silenced for sequential futures since future 1.25.0.
futureCall() ignored arguments stdout, conditions, earlySignal, label, and gc.
Strategy ‘transparent’ was deprecated in future 1.24.0 and is now defunct. Use plan(sequential, split = TRUE) instead.
Strategy ‘multiprocess’ was deprecated in future 1.20.0, and ‘remote’ was deprecated in future 1.24.0. Since then, attempts to use them in plan() would produce a deprecation warning, which was limited to one per R session. Starting with this release, this warning is now produced whenever using plan() with these deprecated future strategies.
Now f <- future(..., stdout = structure(TRUE, drop = TRUE)) will cause the captured standard output to be dropped from the future object as soon as it has been relayed once, for instance, by value(f). Similarly, conditions = structure("conditions", drop = TRUE) will drop captured non-error conditions as soon as they have been relayed. This can help decrease the amount of memory used, especially if there are many active futures.
Now resolve() respects option future.wait.interval. Previously, it was hardcoded to poll for results every 0.1 seconds.
value() will only attempt to recover UTF-8 symbols in the captured standard output if the future was evaluated on an MS Windows that does not support capturing of UTF-8 symbols. Support for UTF-8 capturing on also MS Windows was added in R 4.2.0, but it typically requires an up-to-date MS Windows 10 or MS Windows Server 2022.future.wait.interval was decreased from 0.2 seconds to 0.01 seconds. This controls the polling frequency for finding an available worker when all workers are currently busy. Starting with this release, this option also controls the polling frequency of resolve().future(..., seed = TRUE) forwards the RNG state in the calling R session. Previously, it would leave it intact.nbrOfFreeWorkers() would produce “Error: ‘is.character(name)’ is not TRUE” for plan(multisession, workers = 1).
Internal calls to FutureRegistry(action = "collect-first") and FutureRegistry(action = "collect-last") could signal errors early when polling resolved().
Strategy ‘remote’ is deprecated in favor of ‘cluster’. The plan() function will give an informative deprecation warning when ‘remote’ is used. For now, this warning is given only once per R session.
Strategy ‘transparent’ is deprecated in favor of ‘sequential’ with argument split = TRUE set. The plan() function will give an informative deprecation warning when ‘transparent’ is used. For now, this warning is given only once per R session.
plan() now produces a one-time warning if a ‘transparent’ strategy is set. The warning reminds the user that ‘transparent’ should only be used for troubleshooting purposes and never be used in production. These days plan(sequential, split = TRUE) together with debug() is probably a better approach for troubleshooting. The long-term plan is to deprecate the ‘transparent’ strategy.
Support for persistent = TRUE with multisession futures is defunct.
\u2713) would be relayed as <U+2713> (8 characters). The reason for this is a limitation in R itself on MS Windows. Now, value() attempts to recover such MS Windows output to UTF-8 before relaying it. There is an option for disabling this new feature.quit() must not be used in forked R processes.future(..., seed) will set the random seed as late as possible just before the future expression is evaluated. Previously it was done before package dependencies where attached, which could lead to non-reproduce random numbers in case a package dependency would update the RNG seed when attached.values(), which has been deprecated since future 1.20.0, is now defunct. Use value() instead.
Support for persistent = TRUE with multisession futures is defunct. If still needed, a temporary workaround is to use cluster futures. However, it is likely that support for persistent will eventually be deprecated for all future backends.
Argument value of resolve(), deprecated since future 1.15.0, is defunct in favor of argument result.
A lazy future remains a generic future until it is launched, which means it is not assigned a future backend class until launched.
Argument seed for futureAssign() and futureCall() now defaults to FALSE just like for future().
R_FUTURE_* environment variables are now only read when the future package is loaded, where they set the corresponding future.* option. Previously, some of these environment variables were queried by different functions as a fallback to when an option was not set. By only parsing them when the package is loaded, it decrease the overhead in functions, and it clarifies that options can be changed at runtime whereas environment variables should only be set at startup.
The overhead of initiating futures have been significantly reduced. For example, the roundtrip time for value(future(NULL)) is about twice as fast for ‘sequential’, ‘cluster’, and ‘multisession’ futures. For ‘multicore’ futures the roundtrip speedup is about 20%. The speedup comes from pre-compiling the R expression that will be used to resolve the future expression into R expression templates which then can quickly compiled for each future. This speeds up the creation of these expression by ~10 times, compared when re-compiling them each time.
The default timeout for resolved() was decreased from 0.20 to 0.01 seconds for cluster/multisession and multicore futures, which means they will spend less time waiting for results when they are not available.
Analogously to how globals may be scanned for “non-exportable” objects when option future.globals.onReference is set to "error" or "warning", value() will now check for similar problems in the resolved value object. An example of this is f <- future(xml2::read_xml("<body></body>")), which will result in an invalid xml_document object if run in parallel, because such objects cannot be transferred between R processes.
In addition to specify which condition classes to be captured and relayed, it is now possible to also specify condition classes to be ignored. For example, conditions = structure("condition", exclude = "message") captures all conditions but message conditions.
Now cluster futures use homogeneous = NULL as the default instead of homogeneous = TRUE. The new default will result in the parallelly package trying to infer whether TRUE or FALSE should be used based on the workers argument.
Now the the post-mortem analysis report of multicore and cluster futures in case their results could not be retrieved include information on globals and their sizes, and if some of them are non-exportable. A similar, detailed report is also produced when a cluster future fails to set up and launch itself on a parallel worker.
if option future.fork.multithreading.enable is FALSE, RcppParallel, in addition to OpenMP, is forced to run with a single threaded whenever running in a forked process (=‘multicore’ futures). This is done by setting environment variable RCPP_PARALLEL_NUM_THREADS to 1.
Add futureSessionInfo() to get a quick overview of the future framework, its current setup, and to run simple tests on it.
Now plan(multicore) warns immediately if multicore processing, that is, forked processing, is not supported, e.g. when running in the RStudio Console.
plan(multiprocess, workers = n) did not warn about ‘multiprocess’ being deprecated when argument workers was specified.
getGlobalsAndPackages() could throw a false error on “Did you mean to create the future within a function? Invalid future expression tries to use global ... variables that do not exist: ... is solely part of a formula or used in some S4 generic functions.
When enabled, option future.globals.onReference could falsely alert on ‘Detected a non-exportable reference (externalptr) in one of the globals (<unknown>) used in the future expression’ in globals, e.g. when using future.apply or furrr map-reduce functions when using a ‘multisession’ backend.
future(fcn(), globals = list(a = 42, fcn = function() a)) would fail with “Error in fcn() : object ‘a’ not found” when using sequential or multicore futures. This affected also map-reduce calls such as future.apply::future_lapply(1, function(x) a, future.globals = list(a = 42)).
Resolving a ‘sequential’ future without globals would result in internal several ...future.* objects being written to the calling environment, which might be the global environment.
Environment variable R_FUTURE_PLAN would propagate down with nested futures, forcing itself onto also nested future plans. Now it is unset in nested futures, resulting in a sequential future strategy unless another was explicitly set by plan().
Transparent futures no longer warn about local = FALSE being deprecated. Although local = FALSE is being deprecated, it is still used internally by ‘transparent’ futures for a while longer. Please do not use ‘transparent’ futures in production code and never in a package.
remote() could produce an error on “object ‘homogeneous’ not found”.
nbrOfFreeWorkers() for ‘cluster’ futures assumed that the current plan is set to cluster too.
In order to handle them conditionally higher up in the call chain, warnings and errors produced from using the random number generator (RNG) in a future without declaring the intention to use one are now of class RngFutureWarning and RngFutureError, respectively. Both of these classes inherits from RngFutureCondition.
Now run-time errors from resolving a future take precedence over RngFutureError:s. That is, future({ rnorm(1); log("a") }, seed = FALSE) will signal an error ‘log(“a”)’ instead of an RNG error when option future.rng.onMisuse is set to "error".
nbrOfFreeWorkers() to query how many workers are free to take on futures immediately. Until all third-party future backends have implemented this, some backends might produce an error saying it is not yet supported.future(..., seed = TRUE) with ‘sequential’ futures would set the RNG kind of the parent process. Now it behaves the same regardless of future backend.
Signaling immediateCondition:s with ‘multicore’ could result in Error in save_rds(obj, file) : save_rds() failed to rename temporary save file '/tmp/RtmpxNyIyK/progression21f3f31eadc.rds.tmp' (NA bytes; last modified on NA) to '/tmp/RtmpxNyIyK/progression21f3f31eadc.rds' (NA bytes; last modified on NA). There was an assertion at the end of the internal save_rds() function that incorrectly assumed that the target file should exist. However, the file might have already been processed and removed by the future in the main R session.
value() with both a run-time error and an RNG mistake would signal the RNG warning instead of the run-time error when the for-internal-use-only argument signal was set to FALSE.
Due to a mistake introduced in future 1.20.0, the package would end up assigning a .packageVersion object to the global environment when loaded.
Strategy ‘multiprocess’ is deprecated in favor of either ‘multisession’ or ‘multicore’, depending on operating system and R setup. The plan() function will give an informative deprecation warning when ‘multiprocess’ is used. This warning is given only once per R session.
Launching R or Rscript with command-line option --parallel=n, where n > 1, will now use ‘multisession’ as future strategy. Previously, it would use ‘multiprocess’, which is now deprecated.
Support for local = FALSE is deprecated. For the time being, it remains supported for ‘transparent’ futures and ‘cluster’ futures that use persistent = TRUE. However, note that persistent = TRUE will also deprecated at some point in the future. These deprecations are required in order to further standardize the Future API across various types of parallel backends.
Now multisession workers inherit the package library path from the main R session when they are created, that is, when calling plan(multisession). To avoid this, use plan(multisession, rscript_libs = NULL), which is an argument passed down to makeClusterPSOCK(). With this update, ‘sequential’, ‘multisession’, and ‘multicore’ futures see the exact same library path.
Several functions for managing parallel-style processing have been moved to a new parallelly package. Specifically, functions availableCores(), availableWorkers(), supportsMulticore(), as.cluster(), autoStopCluster(), makeClusterMPI(), makeClusterPSOCK(), and makeNodePSOCK() have been moved. None of them are specific to futures per se and are likely useful elsewhere too. Also, having them in a separate, standalone package will speed up the process of releasing any updates to these functions. The code base of the future package shrunk about 10-15% from this migration. For backward compatibility, the migrated functions remain in this package as re-exports.
Setting up a future strategy with argument split = TRUE will cause the standard output and non-error conditions to be split (“tee:d”) on the worker’s end, while still relaying back to the main R session as before. This can be useful when debugging with browse() or debug(), e.g. plan(sequential, split = TRUE). Without it, debug output is not displayed.
Now multicore futures relay immediateCondition:s in a near-live fashion.
It is now possible to pass any arguments that makeClusterPSOCK() accepts in the call to plan(cluster, ...) and plan(multisession, ...). For instance, to set the working directory of the cluster workers to a temporary folder, pass argument rscript_startup = "setwd(tempdir())". Another example is rscript_libs = c(libs, "*") to prepend the library path on the worker with the paths in libs.
plan() and tweak() check for even more arguments that must not be set by either of them. Specifically, attempts to adjust the following arguments of future() will result in an error: conditions, envir, globals, packages, stdout, and substitute in addition to already validated lazy and seed.
tweak() now returns a wrapper function that calls the original future strategy function with the modified defaults. Previously, it would make a copy of the original function with modified argument defaults. This new approach will make it possible to introduce new future arguments that can be modified by tweak() and plan() without having to update every future backend package, e.g. the new split = TRUE argument.
Add a ‘Best Practices for Package Developers’ vignette.
Add a ‘How the Future Framework is Validated’ vignette.
Since last version, future 1.19.1, future(..., conditions = character(0L)) would no longer avoid intercepting conditions as intended; instead, it muffles all conditions. From now on, use conditions = NULL.
Relaying of immediateCondition:s was not near-live for multisession and cluster if the underlying PSOCK cluster used useXDR=FALSE for communication.
print() for Future would also print any attributes of its environment.
The error message produced by nbrOfWorkers() was incomplete.
Renamed environment variable R_FUTURE_MAKENODEPSOCK_tries used by makeClusterPSOCK() to R_FUTURE_MAKENODEPSOCK_TRIES.
The Mandelbrot demo would produce random numbers without declaring so.
Strategy ‘multiprocess’ is deprecated in favor of either ‘multisession’ or ‘multicore’, depending on operating system and R setup.
values() is deprecated. Use value() instead.
All backward compatible code for the legacy, defunct, internal Future element value is now removed. Using or relying on it is an error.
Futures detect when random number generation (RNG) was used to resolve them. If a future uses RNG without parallel RNG was requested, then an informative warning is produced. To request parallel RNG, specify argument seed, e.g. f <- future(rnorm(3), seed = TRUE) or y %<-% { rnorm(3) } %seed% TRUE. Higher-level map-reduce APIs provide similarly named “seed” arguments to achieve the same. To, escalate these warning to errors, set option future.rng.onMisuse to "error". To silence them, set it to "ignore".
Now, all non-captured conditions are muffled, if possible. For instance, future(warning("boom"), conditions = c("message")) will truly muffle the warning regardless of backend used. This was needed to fix below bug.
makeClusterPSOCK() will now retry to create a cluster node up to tries (default: 3) times before giving up. If argument port species more than one port (e.g. port = "random") then it will also attempt find a valid random port up to tries times before giving up. The pre-validation of the random port is only supported in R (>= 4.0.0) and skipped otherwise.
makeClusterPSOCK() skips shell quoting of the elements in rscript if it inherits from AsIs.
makeClusterPSOCK(), or actually makeNodePSOCK(), gained argument quiet, which can be used to silence output produced by manual = TRUE.
If multithreading is disabled but multicore futures fail to acknowledge the setting on the current system, then an informative FutureWarning is produced by such futures.
Now availableCores() better supports Slurm. Specifically, if environment variable SLURM_CPUS_PER_TASK is not set, which requires that option --slurm-cpus-per-task=n is specified and SLURM_JOB_NUM_NODES=1, then it falls back to using SLURM_CPUS_ON_NODE, e.g. when using --ntasks=n.
Now availableCores() and availableWorkers() supports LSF/OpenLava. Specifically, they acknowledge environment variable LSB_DJOB_NUMPROC and LSB_HOSTS, respectively.
plan(multisession), plan(cluster, workers = <number>), and makeClusterPSOCK() which they both use internally, sets up localhost workers twice as fast compared to versions since future 1.12.0, which brings it back to par with a bare-bone parallel::makeCluster(..., setup_strategy = "sequential") setup. The slowdown was introduced in future 1.12.0 (2019-03-07) when protection against leaving stray R processes behind from failed worker startup was implemented. This protection now makes use of memoization for speedup.Sequential and multicore backends, but not multisession, would produce errors on “‘…’ used in an incorrect context” in cases where ... was part of argument globals and not the evaluation environment.
Contrary to other future backends, any conditions produced while resolving a sequential future using future(..., conditions = character()) would be signaled, although the most reasonable expectation would be that they are silenced. Now, all non-captured conditions are muffled, if possible.
Option future.rng.onMisuse was not passed down to nested futures.
Disabling multithreading in forked processes by setting R option future.fork.multithreading.enable or environment variable R_FUTURE_FORK_MULTITHREADING_ENABLE to FALSE would cause multicore futures to always return value 1L. This bug was introduced in future 1.17.0 (2020-04-17).
getGlobalsAndPackages() did not always return a globals element that was of class FutureGlobals.
getGlobalsAndPackages(..., globals) would recalculate total_size even when it was already calculated or known to be zero.
getGlobalsAndPackages(Formula::Formula(~ x)) would produce “the condition has length > 1” warnings (which will become errors in future R versions).
print() on RichSOCKcluster gives information not only on the name of the host but also on the version of R and the platform of each node (“worker”), e.g. “Socket cluster with 3 nodes where 2 nodes are on host ‘localhost’ (R version 4.0.0 (2020-04-24), platform x86_64-w64-mingw32), 1 node is on host ‘n3’ (R version 3.6.3 (2020-02-29), platform x86_64-pc-linux-gnu)”.
Error messages from cluster future failures are now more informative than “Unexpected result (of class ‘NULL’ != ‘FutureResult’)”. For example, if the future package is not installed on the worker, then the error message clearly says so. Even, if there is an unexpected result error from a PSOCK cluster future, then the error produced give extra information on node where it failed, e.g. “Unexpected result (of class ‘NULL’ != ‘FutureResult’) retrieved for ClusterFuture future (label = ‘ClusterFuture worker (‘RichSOCKnode’ #1 on host ‘n3’ (R version 3.6.3 (2020-02-29), platform x86_64-pc-linux-gnu)) is out of sync.”
It is now possible to set environment variables on workers before they are launched by makeClusterPSOCK() by specify them as as "<name>=<value>" as part of the rscript vector argument, e.g. rscript = c("ABC=123", "DEF='hello world'", "Rscript"). This works because elements in rscript that match regular expression [[:alpha:]_][[:alnum:]_]*=.* are no longer shell quoted.
makeClusterPSOCK() now returns a cluster that in addition to inheriting from SOCKcluster it will also inherit from RichSOCKcluster.
Made makeClusterPSOCK() and makeNodePSOCK() agile to the name change from parallel:::.slaveRSOCK() to parallel:::.workRSOCK() in R (>= 4.1.0).
makeClusterPSOCK(..., rscript) will not try to locate rscript[1] if argument homogeneous is FALSE (or inferred to be FALSE).
makeClusterPSOCK(..., rscript_envs) would result in a syntax error when starting the workers due to non-ASCII quotation marks if option useFancyQuotes was not set to FALSE.
plan(list(...)) would produce ‘Error in UseMethod(“tweak”) : no applicable method for ’tweak’ applied to an object of class “list”’ if a non-function object named ‘list’ was on the search path.
plan(x$abc) with x <- list(abc = sequential) would produce ‘Error in UseMethod(“tweak”) : no applicable method for ’tweak’ applied to an object of class “c(‘FutureStrategyList’, ‘list’)”’.
TESTS: R_FUTURE_FORK_ENABLE=false R CMD check ... would produce ‘Error: connections left open: …’ when checking the ‘multiprocess’ example.
Support for persistent = TRUE with multisession futures is deprecated. If still needed, a temporary workaround is to use cluster futures. However, it is likely that support for persistent will eventually be deprecated for all future backends.
Options future.globals.method, future.globals.onMissing, and future.globals.resolve are deprecated and produce warnings if set. They may only be used for troubleshooting purposes because they may affect how futures are evaluated, which means that reproducibility cannot be guaranteed elsewhere.
values() to value() to clean up and simplify the API.makeClusterPSOCK() gained argument rscript_envs for setting environment variables in workers on startup, e.g. rscript_envs = c(FOO = "3.14", "BAR").
Now the result of a future holds session details in case an error occurred while evaluating the future.
_R_CHECK_LIMIT_CORES_ set. To better emulate CRAN submission checks, the future package will, when loaded, set this environment variable to ‘TRUE’ if unset and if R CMD check is running. Note that future::availableCores() respects _R_CHECK_LIMIT_CORES_ and returns at most 2L (two cores) if detected.Any globals named version and has_future would be overwritten with “garbage” values internally.
Disabling of multi-threading when using ‘multicore’ futures did not work on all platforms.
oplan <- plan(new_strategy) returns the list of all nested strategies previously set, instead of just the strategy on top of this stack. This makes it easier to temporarily use another plan. For the old behavior, use oplan <- plan(new_strategy)[[1]].Now value() detects if a future(..., seed = FALSE) call generated random numbers, which then might give unreliable results because non-parallel safe, non-statistically sound random number generation (RNG) was used. If option future.rng.onMisuse is "warning", a warning is produced. If "error", an error is produced. If "ignore" (default), the mistake is silently ignored. Using seed = NULL is like seed = FALSE but without performing the RNG validation.
For convenience, argument seed of future() may now also be an ordinary single integer random seed. If so, a L’Ecuyer-CMRG RNG seed is created from this seed. If seed = TRUE, then a L’Ecuyer-CMRG RNG seed based on the current RNG state is used. Use seed = FALSE when it is known that the future does not use RNG.
ClusterFuture:s now relay immediateCondition:s back to the main process momentarily after they are signaled and before the future is resolved.
future.fork.multithreading.enable or environment variable R_FUTURE_FORK_MULTITHREADING_ENABLE to FALSE. This requires that RhpcBLASctl package is installed. Parallelization via multi-threaded processing (done in native code by some packages and externally library) while at the same time using forked (aka “multicore”) parallel processing is unstable in some cases. Note that this is not only true when using plan(multicore) but also when using, for instance, parallel::mclapply(). This is in beta so the above names and options might change later.Future and FutureResult objects with an internal version 1.7 or older have been deprecated since 1.14.0 (July 2019) and are now defunct.
Defunct hidden argument progress of resolve(), and hidden arguments/fields condition and calls of FutureResult are now gone.
makeClusterPSOCK() draws a random port from (when argument port is not specified) can now be controlled by environment variable R_FUTURE_RANDOM_PORTS. The default range is still 11000:11999 as with the parallel package.resolved() in future 1.15.0 would cause lazy futures to block if all workers were occupied.resolved() will now launch lazy futures.Now the “visibility” of future values is recorded and reflected by value().
Now option future.globals.onReference defaults to environment variable R_FUTURE_GLOBALS_ONREFERENCE.
?makeClusterPSOCK with instructions on how to troubleshoot when the setup of local and remote clusters fail.values() would resignal immediateCondition:s despite those should only be signaled at most once per future.
makeClusterPSOCK() could produce warnings like “cannot open file ‘/tmp/alice/Rtmpi69yYF/future.parent=2622.a3e32bc6af7.pid’: No such file”, e.g. when launching R workers running in Docker containers.
Package would set or update the RNG state of R (.Random.seed) when loaded, which could affect RNG reproducibility.
Package could set .Random.seed to NULL, instead of removing it, which in turn would produce a warning on “‘.Random.seed’ is not an integer vector but of type ‘NULL’, so ignored” when the next random number generated.
Now a future assignment to list environments produce more informative error messages if attempting to assign to more than one element.
makeClusterMPI() did not work for MPI clusters with comm other than 1.
All types of conditions are now captured and relayed. Previously, only conditions of class message and warning were relayed.
If one of the futures in a collection produces an error, then values() will signal that error as soon as it is detected. This means that while calling values() guarantees to resolve all futures, it does not guarantee that the result from all futures are gathered back to the master R session before the error is relayed.
values() now relays stdout and signal as soon as possible as long as the standard output and the conditions are relayed in their original order.
If a captured condition can be “muffled”, then it will be muffled. This helps to prevent conditions from being handled twice by condition handlers when futures are evaluated in the main R session, e.g. plan(sequential). Messages and warnings were already muffled in the past.
Forked processing is considered unstable when running R from certain environments, such as the RStudio environment. Because of this, ‘multicore’ futures have been disabled in those cases since future 1.13.0. This change caught several RStudio users by surprise. Starting with future 1.14.0, an informative one-time-per-session warning will be produced when attempts to use ‘multicore’ is made in non-supported environments such as RStudio. This warning will also be produced when using ‘multiprocess’, which will fall back to using ‘multisession’ futures. The warning can be disabled by setting R option future.supportsMulticore.unstable, or environment variable FUTURE_SUPPORTSMULTICORE_UNSTABLE to "quiet".
Now option future.startup.script falls back to environment variable R_FUTURE_STARTUP_SCRIPT.
Conditions inheriting immediateCondition are signaled as soon as possible. Contrary to other types of conditions, these will be signaled only once per future, despite being collected.
Early signaling did not take place for resolved() for ClusterFuture and MulticoreFuture.
When early signaling was enabled, functions such as resolved() and resolve() would relay captured conditions multiple times. This would, for instance, result in the same messages and warnings being outputted more than once. Now it is only value() that will resignal conditions.
The validation of connections failed to detect when the connection had been serialized (= a NIL external pointer) on some macOS systems.
Argument progress of resolve() is now defunct (was deprecated since future 1.12.0). Option future.progress is ignored. This will make room for other progress-update mechanisms that are in the works.
Usage of internal argument evaluator to future() is now deprecated.
Removed defunct argument output from FutureError().
FutureResult fields/arguments condition and calls are now defunct. Use conditions instead.
Future and FutureResult objects with an internal version 1.7 or older are deprecated and will eventually become defunct. Future backends that implement their own Future classes should update to implement a result() method instead of a value() method for their Future classes. All future backends available on CRAN and Bioconductor have already been updated accordingly.
help("supportsMulticore") for more details, e.g. how to re-enable process forking. Note that parallelization via ‘multisession’ is unaffected and will still work as before. Also, when forked processing is disabled, or otherwise not supported, using plan("multiprocess") will fall back to using ‘multisession’ futures.Forked processing can be disabled by setting R option future.fork.enable to FALSE (or environment variable R_FUTURE_FORK_ENABLE=false). When disabled, ‘multicore’ futures fall back to a ‘sequential’ futures even if the operating system supports process forking. If set of TRUE, ‘multicore’ will not fall back to ‘sequential’. If NA, or not set (the default), a set of best-practices rules will decide whether forking is enabled or not. See help("supportsMulticore") for more details.
Now availableCores() also recognizes PBS environment variable NCPUS, because the PBSPro scheduler does not set PBS_NUM_PPN.
If, option future.availableCores.custom is set to a function, then availableCores() will call that function and interpret its value as number of cores. Analogously, option future.availableWorkers.custom can be used to specify a hostnames of a set of workers that availableWorkers() sees. These new options provide a mechanism for anyone to customize availableCores() and availableWorkers() in case they do not (yet) recognize, say, environment variables that are specific the user’s compute environment or HPC scheduler.
makeClusterPSOCK() gained support for argument rscript_startup for evaluating one or more R expressions in the background R worker prior to the worker event loop launching. This provides a more convenient approach than having to use, say, rscript_args = c("-e", sQuote(code)).
makeClusterPSOCK() gained support for argument rscript_libs to control the R package library search path on the workers. For example, to prepend the folder ~/R-libs on the workers, use rscript_libs = c("~/R-libs", "*"), where "*" will be resolved to the current .libPaths() on the workers.
Debug messages are now prepended with a timestamp.
makeClusterPSOCK() did not shell quote the Rscript executable when running its pre-tests checking whether localhost Rscript processes can be killed by their PIDs or not.value of resolve() has been renamed to result to better reflect that not only values are collected when this argument is used. Argument value still works for backward compatibility, but will eventually be formally deprecated and then defunct.If makeClusterPSOCK() fails to create one of many nodes, then it will attempt to stop any nodes that were successfully created. This lowers the risk for leaving R worker processes behind.
Future results now hold the timestamps when the evaluation of the future started and finished.
Functions no longer produce “partial match of ‘condition’ to ‘conditions’” warnings with options(warnPartialMatchDollar = TRUE).
When future infix operators (%conditions%, %globals%, %label%, %lazy%, %packages%, %seed%, and %stdout%) that are intended for future assignments were used in the wrong context, they would incorrectly be applied to the next future created. Now they’re discarded.
makeClusterPSOCK() in future (>= 1.11.1) produced warnings when argument rscript had length(rscript) > 1.
Validation of L’Ecuyer-CMRG RNG seeds failed in recent R devel.
With options(OutDec = ","), the default value of several argument would resolve to NA_real_ rather than a numeric value resulting in errors such as “is.finite(alpha) is not TRUE”.
Argument progress of resolve() is now deprecated.
Argument output of FutureError() is now defunct.
FutureError no longer inherits simpleError.
makeClusterPSOCK() fails to connect to a worker, it produces an error with detailed information on what could have happened. In rare cases, another error could be produced when generating the information on what the workers PID is.The defaults of several arguments of makeClusterPSOCK() and makeNodePSOCK() can now be controlled via environment variables in addition to R options that was supported in the past. An advantage of using environment variables is that they will be inherited by child processes, also nested ones.
The printing of future plans is now less verbose when the workers argument is a complex object such as a PSOCK cluster object. Previously, the output would include verbose output of attributes, etc.
R CMD check is running or not. If it is, then a few future-specific environment variables are adjusted such that the tests play nice with the testing environment. For instance, it sets the socket connection timeout for PSOCK cluster workers to 120 seconds (instead of the default 30 days!). This will lower the risk for more and more zombie worker processes cluttering up the test machine (e.g. CRAN servers) in case a worker process is left behind despite the main R processes is terminated. Note that these adjustments are applied automatically to the checks of any package that depends on, or imports, the future package.makeClusterPSOCK() would fail to connect to a worker, for instance due to a port clash, then it would leave the R worker process running - also after the main R process terminated. When the worker is running on the same machine, makeClusterPSOCK() will now attempt to kill such stray R processes. Note that parallel::makePSOCKcluster() still has this problem.The future call stack (“traceback”) is now recorded when the evaluation of a future produces an error. Use backtrace() on the future to retrieve it.
Now futureCall() defaults to args = list() making is easier to call functions that do not take arguments, e.g. futureCall(function() 42).
plan() gained argument .skip = FALSE. When TRUE, setting the same future strategy as already set will be skipped, e.g. calling plan(multisession) consecutively will have the same effect as calling it just once.
makeClusterPSOCK() produces more informative error messages whenever the setup of R workers fails. Also, its verbose messages are now prefixed with [local output] to help distinguish the output produced by the current R session from that produced by background workers.
It is now possible to specify what type of SSH clients makeClusterPSOCK() automatically searches for and in what order, e.g. rshcmd = c("<rstudio-ssh>", "<putty-plink>").
Now makeClusterPSOCK() preserves the global RNG state (.Random.seed) also when it draws a random port number.
makeClusterPSOCK() gained argument rshlogfile.
Cluster futures provide more informative error messages when the communication with the worker node is out of sync.
Argument stdout was forced to TRUE when using single-core multicore or single-core multisession futures.
When evaluated in a local environment, futureCall(..., globals = "a") would set the value of global a to NULL, regardless if it exists or not and what its true value is.
makeClusterPSOCK(..., rscript = "my_r") would in some cases fail to find the intended my_r executable.
ROBUSTNESS: A cluster future, including a multisession one, could retrieve results from the wrong workers if a new set of cluster workers had been set up after the future was created/launched but before the results were retrieved. This could happen because connections in R are indexed solely by integers which are recycled when old connections are closed and new ones are created. Now cluster futures assert that the connections to the workers are valid, and if not, an informative error message is produced.
Calling result() on a non-resolved UniprocessFuture would signal evaluation errors.
Add support for manually specifying globals in addition to those that are automatically identified via argument globals or %globals%. Two examples are globals = structure(TRUE, add = list(a = 42L, b = 3.14)) and globals = structure(TRUE, add = c("a", "b")). Analogously, attribute ignore can be used to exclude automatically identified globals.
The error reported when failing to retrieve the results of a future evaluated on a localhost cluster/multisession worker or a forked/multicore worker is now more informative. Specifically, it mentions whether the worker process is still alive or not.
Add makeClusterMPI(n) for creating MPI-based clusters of a similar kind as parallel::makeCluster(n, type = "MPI") but that also attempts to workaround issues where parallel::stopCluster() causes R to stall.
makeClusterPSOCK() and makeClusterMPI() gained argument autoStop for controlling whether the cluster should be automatically stopped when garbage collected or not.
BETA: Now resolved() for ClusterFuture is non-blocking also for clusters of type MPIcluster as created by parallel::makeCluster(..., type = "MPI").
R option width is passed down so that standard output is captured consistently across workers and consistently with the master process.
Now more future.* options are passed down so that they are also acknowledged when using nested futures.
Add vignette on ‘Outputting Text’.
CLEANUP: Only the core parts of the API are now listed in the help index. This was done to clarify the Future API. Help for non-core parts are still via cross references in the indexed API as well via help().
When using forced, nested ‘multicore’ parallel processing, such as, plan(list(tweak(multicore, workers = 2), tweak(multicore, workers = 2))), then the child process would attempt to resolve futures owned by the parent process resulting in an error (on ‘bad error message’).
When using plan(multicore), if a forked worker would terminate unexpectedly, it could corrupt the master R session such that any further attempts of using forked workers would fail. A forked worker could be terminated this way if the user pressed Ctrl-C (the worker receives a SIGINT signal).
makeClusterPSOCK() produced a warning when environment variable R_PARALLEL_PORT was set to random (e.g. as on CRAN).
Printing a plan() could produce an error when the deparsed call used to set up the plan() was longer than 60 characters.
future::future_lapply() is defunct (gives an error if called). Please use the one in the future.apply package instead.
Argument output of FutureError() is formally deprecated.
Removed all FutureEvaluationCondition classes and related methods.
getGlobalsAndPackages() gained argument maxSize.
makeClusterPSOCK() now produces a more informative warning if environment variable R_PARALLEL_PORT specifies a non-numeric port.
Now plan() gives a more informative error message in case it fails, e.g. when the internal future validation fails and why.
Added UnexpectedFutureResultError to be used by backends for signaling in a standard way that an unexpected result was retrieved from a worker.
When the communication between an asynchronous future and a background R process failed, further querying of the future state/results could end up in an infinite waiting loop. Now the failed communication error is recorded and re-signaled if any further querying attempts.
Internal, seldom used myExternalIP() failed to recognize IPv4 answers from some of the lookup servers. This could in turn produce another error.
In R (>= 3.5.0), multicore futures would produce multiple warnings originating from querying whether background processes have completed or not. These warnings are now suppressed.
More errors related to orchestration of futures are of class FutureError to make it easier to distinguish them from future evaluation errors.
Add support for a richer set of results returned by resolved futures. Previously only the value of the future expression, which could be a captured error to be resignaled, was expected. Now a FutureResult object may be returned instead. Although not supported in this release, this update opens up for reporting on additional information from the evaluation of futures, e.g. captured output, timing and memory benchmarks, etc. Before that can take place, existing future backend packages will have to be updated accordingly.
backtrace() returns only the last call that produced the error. It is unfortunately not possible to capture the call stack that led up to the error when evaluating a future expression.
value() for MulticoreFuture would not produce an error when a (forked) background R workers would terminate before the future expression is resolved. This was a limitation inherited from the parallel package. Now an informative FutureError message is produced.
value() for MulticoreFuture would not signal errors unless they inherited from simpleError - now it’s enough for them to inherits from error.
value() for ClusterFuture no longer produces a FutureEvaluationError, but FutureError, if the connection to the R worker has changed (which happens if something as drastic as closeAllConnections() have been called.)
futureCall(..., globals = FALSE) would produce “Error: second argument must be a list”, because the explicit arguments where not exported. This could also happen when specifying globals by name or as a named list.
Nested futures were too conservative in requiring global variables to exist, even when they were false positives.
Argument workers of future strategies may now also be a function, which is called without argument when the future strategy is set up and used as is. For instance, plan(multiprocess, workers = halfCores) where halfCores <- function() { max(1, round(availableCores()/ 2)) } will use half of the number of available cores. This is useful when using nested future strategies with remote machines.
On Windows, makeClusterPSOCK(), and therefore plan(multisession) and plan(multiprocess), will use the SSH client distributed with RStudio as a fallback if neither ssh nor plink is available on the system PATH.
Now plan() makes sure that nbrOfWorkers() will work for the new strategy. This will help catch mistakes such as plan(cluster, workers = cl) where cl is a basic R list rather than a cluster list early on.
Added %packages% to explicitly control packages to be attached when a future is resolved, e.g. y %<-% { YT[2] } %packages% "data.table". Note, this is only needed in cases where the automatic identification of global and package dependencies is not sufficient.
Added condition classes FutureCondition, FutureMessage, FutureWarning, and FutureError representing conditions that occur while a future is setup, launched, queried, or retrieved. They do not represent conditions that occur while evaluating the future expression. For those conditions, new classes FutureEvaluationCondition, FutureEvaulationMessage, FutureEvaluationWarning, and FutureEvaluationError exists.
if (runif(1) < 1/2) x <- 0; y <- 2 * x.externalptr) can not be exported, but there are exceptions. By setting options future.globals.onReference to "warning", a warning is produced informing the user about potential problems. If "error", an error is produced. Because there might be false positive, the default is "ignore", which will cause above scans to be skipped. If there are non-exportable globals and these tests are skipped, a run-time error may be produced only when the future expression is evaluated.The total size of global variables was overestimated, and dramatically so if defined in the global environment and there were are large objects there too. This would sometimes result in a false error saying that the total size is larger than the allowed limit.
An assignment such as x <- x + 1 where the left-hand side (LHS) x is a global failed to identify x as a global because the right-hand side (RHS) x would override it as a local variable. Updates to the globals package fixed this problem.
makeClusterPSOCK(..., renice = 19) would launch each PSOCK worker via nice +19 resulting in the error “nice: ‘+19’: No such file or directory”. This bug was inherited from parallel::makePSOCKcluster(). Now using nice --adjustment=19 instead.
Protection against passing future objects to other futures did not work for future strategy ‘multicore’.
future_lapply() has moved to the new future.apply package available on CRAN. The future::future_lapply() function will soon be deprecated, then defunct, and eventually be removed from the future package. Please update your code to make use of future.apply::future_lapply() instead.
Dropped defunct ‘eager’ and ‘lazy’ futures; use ‘sequential’ instead.
Dropped defunct arguments cluster and maxCores; use workers instead.
In previous version of the future package the FutureError class was used to represent both orchestration errors (now FutureError) and evaluation errors (now FutureEvaluationError). Any usage of class FutureError for the latter type of errors is deprecated and should be updated to FutureEvaluationError.
Now plan() accepts also strings such as "future::cluster".
Now backtrace(x[[ER]]) works also for non-environment x:s, e.g. lists.
When measuring the size of globals by scanning their content, for certain types of classes the inferred lengths of these objects were incorrect causing internal subset out-of-range issues.
print() for Future would output one global per line instead of concatenating the information with commas.
getGlobalsAndPackages().nbrOfWorkers() gave an error with plan(remote). Fixed by making the ‘remote’ future inherit cluster (as it should).quit(), but that appeared to have corrupted the main R session when running on Solaris.makeClusterPSOCK() now defaults to use the Windows PuTTY software’s SSH client plink -ssh, if ssh is not found.
Argument homogeneous of makeNodePSOCK(), a helper function of makeClusterPSOCK(), will default to FALSE also if the hostname is a fully qualified domain name (FQDN), that is, it “contains periods”. For instance, c('node1', 'node2.server.org') will use homogeneous = TRUE for the first worker and homogeneous = FALSE for the second.
makeClusterPSOCK() now asserts that each cluster node is functioning by retrieving and recording the node’s session information including the process ID of the corresponding R process.
Nested futures sets option mc.cores to prevent spawning of recursive parallel processes by mistake. Because ‘mc.cores’ controls additional processes, it was previously set to zero. However, since some functions such as mclapply() does not support that, it is now set to one instead.
makeClusterPSOCK() gained more detailed descriptions on arguments and what their defaults are.future_lapply() with multicore / multisession futures, would use a suboptimal workload balancing where it split up the data in one chunk too many. This is no longer a problem because of how argument workers is now defined for those type of futures (see note on top).
future_lapply(), as well as lazy multicore and lazy sequential futures, did not respect option future.globals.resolve, but was hardcoded to always resolve globals (future.globals.resolve = TRUE).
When globals larger than the allowed size (option future.globals.maxSize) are detected an informative error message is generated. Previous version introduced a bug causing the error to produce another error.
Lazy sequential futures would produce an error when resolved if required packages had been detached.
print() would not display globals gathered for lazy sequential futures.
Added package tests for globals part of formulas part of other globals, e.g. purrr::map(x, ~ rnorm(.)), which requires globals (>= 0.10.0).
Now package tests with parallel::makeCluster() not only test for type = "PSOCK" clusters but also "FORK" (when supported).
TESTS: Cleaned up test scripts such that the overall processing time for the tests was roughly halved, while preserving the same test coverage.
future_lapply() is now to not generate RNG seeds (future.seed = FALSE). If proper random number generation is needed, use future.seed = TRUE. For more details, see help page.future() and future_lapply() gained argument packages for explicitly specifying packages to be attached when the futures are evaluated. Note that the default throughout the future package is that all globals and all required packages are automatically identified and gathered, so in most cases those do not have to be specified manually.
The default values for arguments connectTimeout and timeout of makeNodePSOCK() can now be controlled via global options.
Now future_lapply() guarantees that the RNG state of the calling R process after returning is updated compared to what it was before and in the exact same way regardless of future.seed (except FALSE), future.scheduling and future strategy used. This is done in order to guarantee that an R script calling future_lapply() multiple times should be numerically reproducible given the same initial seed.
It is now possible to specify a pre-generated sequence of .Random.seed seeds to be used for each FUN(x[[i]], ...) call in future_lapply(x, FUN, ...).
future_lapply() scans global variables for non-resolved futures (to resolve them) and calculate their total size once. Previously, each chunk (a future) would redo this.Now future_lapply(X, FUN, ...) identifies global objects among X, FUN and ... recursively until no new globals are found. Previously, only the first level of globals were scanned. This is mostly thanks to a bug fix in globals 0.9.0.
A future that used a global object x of a class that overrides length() would produce an error if length(x) reports more elements than what can be subsetted.
nbrOfWorkers() gave an error with plan(cluster, workers = cl) where cl is a cluster object created by parallel::makeCluster(), etc. This prevented for instance future_lapply() to work with such setups.
plan(cluster, workers = cl) where cl <- makeCluster(..., type = MPI") would give an instant error due to an invalid internal assertion.
plan(), e.g. plan(cluster) will set up workers on all cluster nodes. Previously, this only happened when the first future was created.Renamed ‘eager’ futures to ‘sequential’, e.g. plan(sequential). The ‘eager’ futures will be deprecated in an upcoming release.
Added support for controlling whether a future is resolved eagerly or lazily when creating the future, e.g. future(..., lazy = TRUE), futureAssign(..., lazy = TRUE), and x %<-% { ... } %lazy% TRUE.
future(), futureAssign() and futureCall() gained argument seed, which specifies a L’Ecuyer-CMRG random seed to be used by the future. The seed for future assignment can be specified via %seed%.
futureAssign() now passes all additional arguments to future().
Added future_lapply() which supports load balancing (“chunking”) and perfect reproducibility (regardless of type of load balancing and how futures are resolved) via initial random seed.
Added availableWorkers(). By default it returns localhost workers according to availableCores(). In addition, it detects common HPC allocations given in environment variables set by the HPC scheduler.
The default for plan(cluster) is now workers = availableWorkers().
Now plan() stops any clusters that were implicitly created. For instance, a multisession cluster created by plan(multisession) will be stopped when plan(eager) is called.
makeClusterPSOCK() treats workers that refer to a local machine by its local or canonical hostname as “localhost”. This avoids having to launch such workers over SSH, which may not be supported on all systems / compute cluster.
Option future.debug = TRUE also reports on total size of globals identified and for cluster futures also the size of the individual global variables exported.
Option future.wait.timeout (replaces future.wait.times) specifies the maximum waiting time for a free workers (e.g. a core or a compute node) before generating a timeout error.
Option future.availableCores.fallback, which defaults to environment variable R_FUTURE_AVAILABLECORES_FALLBACK can now be used to specify the default number of cores / workers returned by availableCores() and availableWorkers() when no other settings are available. For instance, if R_FUTURE_AVAILABLECORES_FALLBACK=1 is set system wide in an HPC environment, then all R processes that uses availableCores() to detect how many cores can be used will run as single-core processes. Without this fallback setting, and without other core-specifying settings, the default will be to use all cores on the machine, which does not play well on multi-user systems.
plan(lazy) are now deprecated. Instead, use plan(eager) and then f <- future(..., lazy = TRUE) or x %<-% { ... } %lazy% TRUE. The reason behind this is that in some cases code that uses futures only works under eager evaluation (lazy = FALSE; the default), or vice verse. By removing the “lazy” future strategy, the user can no longer override the lazy = TRUE / FALSE that the developer is using.Creation of cluster futures (including multisession ones) would time out already after 40 seconds if all workers were busy. New default timeout is 30 days (option future.wait.timeout).
nbrOfWorkers() gave an error for plan(cluster, workers) where workers was a character vector or a cluster object of the parallel package. Because of this, future_lapply() gave an error with such setups.
availableCores(methods = "_R_CHECK_LIMIT_CORES_") would give an error if not running R CMD check.
Added makeClusterPSOCK() - a version of parallel::makePSOCKcluster() that allows for more flexible control of how PSOCK cluster workers are set up and how they are launched and communicated with if running on external machines.
Added generic as.cluster() for coercing objects to cluster objects to be used as in plan(cluster, workers = as.cluster(x)). Also added a c() implementation for cluster objects such that multiple cluster objects can be combined into a single one.
Added sessionDetails() for gathering details of the current R session.
plan() and plan("list") now prints more user-friendly output.
On Unix, internal myInternalIP() tries more alternatives for finding the local IP number.
values() for lists and list environments of futures where one or more of the futures resolved to NULL would give an error.
value() for ClusterFuture would give cryptic error message “Error in stop(ex) : bad error message” if the cluster worker had crashed / terminated. Now it will instead give an error message like “Failed to retrieve the value of ClusterFuture from cluster node #1 on ‘localhost’. The reason reported was”error reading from connection”.
Argument user to remote() was ignored (since 1.1.0).
workers = "localhost" they (again) use the exact same R executable as the main / calling R session (in all other cases it uses whatever Rscript is found in the PATH). This was already indeed implemented in 1.0.1, but with the added support for reverse SSH tunnels in 1.1.0 this default behavior was lost.REMOTE CLUSTERS: It is now very simple to use cluster() and remote() to connect to remote clusters / machines. As long as you can connect via SSH to those machines, it works also with these future. The new code completely avoids incoming firewall and incoming port forwarding issues previously needed. This is done by using reverse SSH tunneling. There is also no need to worry about internal or external IP numbers.
Added optional argument label to all futures, e.g. f <- future(42, label = "answer") and v %<-% { 42 } %label% "answer".
Added argument user to cluster() and remote().
Now all Future classes supports run() for launching the future and value() calls run() if the future has not been launched.
MEMORY: Now plan(cluster, gc = TRUE) causes the background R session to be garbage collected immediately after the value is collected. Since multisession and remote futures are special cases of cluster futures, the same is true for these as well.
ROBUSTNESS: Now the default future strategy is explicitly set when no strategies are set, e.g. when used nested futures. Previously, only mc.cores was set so that only a single core was used, but now also plan("default") set.
WORKAROUND: resolved() on cluster futures would block on Linux until future was resolved. This is due to a bug in R. The workaround is to use round the timeout (in seconds) to an integer, which seems to always work / be respected.
Global variables part of subassignments in future expressions are recognized and exported (iff found), e.g. x$a <- value, x[["a"]] <- value, and x[1,2,3] <- value.
Global variables part of formulae in future expressions are recognized and exported (iff found), e.g. y ~ x | z.
As an alternative to the default automatic identification of globals, it is now also possible to explicitly specify them either by their names (as a character vector) or by their names and values (as a named list), e.g. f <- future({ 2*a }, globals = c("a")) or f <- future({ 2*a }, globals = list(a = 42)). For future assignments one can use the %globals% operator, e.g. y %<-% { 2*a } %globals% c("a").
ROBUSTNESS: For the special case where ‘remote’ futures use workers = "localhost" they now use the exact same R executable as the main / calling R session (in all other cases it uses whatever Rscript is found in the PATH).
FutureError now extends simpleError and no longer the error class of captured errors.
Since future 0.13.0, a global pkg would be overwritten by the name of the last package attached in future.
Futures that generated R.oo::Exception errors, they triggered another internal error.
Add support for remote(..., myip = "<external>"), which now queries a set of external lookup services in case one of them fails.
Add mandelbrot() function used in demo to the API for convenience.
ROBUSTNESS: If .future.R script, which is sourced when the future package is attached, gives an error, then the error is ignored with a warning.
TROUBLESHOOTING: If the future requires attachment of packages, then each namespace is loaded separately and before attaching the package. This is done in order to see the actual error message in case there is a problem while loading the namespace. With require()/library() this error message is otherwise suppressed and replaced with a generic one.
Falsely identified global variables no longer generate an error when the future is created. Instead, we leave it to R and the evaluation of the individual futures to throw an error if the a global variable is truly missing. This was done in order to automatically handle future expressions that use non-standard evaluation (NSE), e.g. subset(df, x < 3) where x is falsely identified as a global variable.
Dropped support for system environment variable R_FUTURE_GLOBALS_MAXSIZE.
DEMO: Now the Mandelbrot demo tiles a single Mandelbrot region with one future per tile. This better illustrates parallelism.
Documented R options used by the future package.
Custom futures based on a constructor function that is defined outside a package gave an error.
plan("default") assumed that the future.plan option was a string; gave an error if it was a function.
Various future options were not passed on to futures.
A startup .future.R script is no longer sourced if the future package is attached by a future expression.
Added remote futures, which are cluster futures with convenient default arguments for simple remote access to R, e.g. plan(remote, workers = "login.my-server.org").
Now .future.R (if found in the current directory or otherwise in the user’s home directory) is sourced when the future package is attach (but not loaded). This helps separating scripts from configuration of futures.
Added support for plan(cluster, workers = c("n1", "n2", "n2", "n4")), where workers (also for ClusterFuture()) is a set of host names passed to parallel::makeCluster(workers). It can also be the number of localhost workers.
Added command line option --parallel=<p>, which is long for -p <p>.
Now command line option -p <p> also set the default future strategy to multiprocessing (if p >= 2 and eager otherwise), unless another strategy is already specified via option future.plan or system environment variable R_FUTURE_PLAN.
Now availableCores() also acknowledges environment variable NSLOTS set by Sun/Oracle Grid Engine (SGE).
MEMORY: Added argument gc = FALSE to all futures. When TRUE, the garbage collector will run at the very end in the process that evaluated the future (just before returning the value). This may help lowering the overall memory footprint when running multiple parallel R processes. The user can enable this by specifying plan(multiprocess, gc = TRUE). The developer can control this using future(expr, gc = TRUE) or v %<-% { expr } %tweak% list(gc = TRUE).
Added nbrOfWorkers().
Added informative print() method for the Future class.
values() passes arguments ... to value() of each future.
Added FutureError class.
maxCores and cluster to workers. If using the old argument names a deprecation warning will be generated, but it will still work until made defunct in a future release.resolve() for lists and environments did not work properly when the set of futures was not resolved in order, which could happen with asynchronous futures.Add support to plan() for specifying different future strategies for the different levels of nested futures.
Add backtrace() for listing the trace the expressions evaluated (the calls made) before a condition was caught.
Add transparent futures, which are eager futures with early signaling of conditioned enabled and whose expression is evaluated in the calling environment. This makes the evaluation of such futures as similar as possible to how R evaluates expressions, which in turn simplifies troubleshooting errors, etc.
Add support for early signaling of conditions. The default is (as before) to signal conditions when the value is queried. In addition, they may be signals as soon as possible, e.g. when checking whether a future is resolved or not.
Signaling of conditions when calling value() is now controlled by argument signal (previously onError).
Now UniprocessFuture:s captures the call stack for errors occurring while resolving futures.
ClusterFuture() gained argument persistent = FALSE. With persistent = TRUE, any objects in the cluster R session that was created during the evaluation of a previous future is available for succeeding futures that are evaluated in the same session. Moreover, globals are still identified and exported but “missing” globals will not give an error - instead it is assumed such globals are available in the environment where the future is evaluated.
OVERHEAD: Utility functions exported by ClusterFuture are now much smaller; previously they would export all of the package environment.
f <- multicore(NA, maxCores = 2) would end up in an endless waiting loop for a free core if availableCores() returned one.
ClusterFuture() would ignore local = TRUE.
Added multiprocess futures, which are multicore futures if supported, otherwise multisession futures. This makes it possible to use plan(multiprocess) everywhere regardless of operating system.
Future strategy functions gained class attributes such that it is possible to test what type of future is currently used, e.g. inherits(plan(), "multicore").
ROBUSTNESS: It is only the R process that created a future that can resolve it. If a non-resolved future is queried by another R process, then an informative error is generated explaining that this is not possible.
ROBUSTNESS: Now value() for multicore futures detects if the underlying forked R process was terminated before completing and if so generates an informative error messages.
resolve() gained argument recursive.
Added option future.globals.resolve for controlling whether global variables should be resolved for futures or not. If TRUE, then globals are searched recursively for any futures and if found such “global” futures are resolved. If FALSE, global futures are not located, but if they are later trying to be resolved by the parent future, then an informative error message is generated clarifying that only the R process that created the future can resolve it. The default is currently FALSE.
FIX: Exports of objects available in packages already attached by the future were still exported.
FIX: Now availableCores() returns 3L (=2L+1L) instead of 2L if _R_CHECK_LIMIT_CORES_ is set.
Add multisession futures, which analogously to multicore ones, use multiple cores on the local machine with the difference that they are evaluated in separate R session running in the background rather than separate forked R processes. A multisession future is a special type of cluster futures that do not require explicit setup of cluster nodes.
Add support for cluster futures, which can make use of a cluster of nodes created by parallel::makeCluster().
Add futureCall(), which is for futures what do.call() is otherwise.
Standardized how options are named, i.e. future.<option>. If you used any future options previously, make sure to check they follow the above format.
Now %<=% can also assign to multi-dimensional list environments.
Add futures(), values() and resolved().
Add resolve() to resolve futures in lists and environments.
Now availableCores() also acknowledges the number of CPUs allotted by Slurm.
CLEANUP: Now the internal future variable created by %<=% is removed when the future variable is resolved.
ROBUSTNESS: Now values of environment variables are trimmed before being parsed.
ROBUSTNESS: Add reproducibility test for random number generation using Pierre L’Ecuyer’s RNG stream regardless of how futures are evaluated, e.g. eager, lazy and multicore.
findGlobals(..., method = "ordered") in globals (> 0.5.0) such that a global variable preceding a local variable with the same name is properly identified and exported/frozen.Globals that were copies of package objects were not exported to the future environments.
The future package had to be attached or future::future() had to be imported, if %<=% was used internally in another package. Similarly, it also had to be attached if multicore futures where used.
eager() and multicore() gained argument globals, where globals = TRUE will validate that all global variables identified can be located already before the future is created. This provides the means for providing the same tests on global variables with eager and multicore futures as with lazy futures.plan("default") resets to the default strategy, which is synchronous eager evaluation unless option future_plan or environment variable R_FUTURE_PLAN has been set.
availableCores("mc.cores") returns getOption("mc.cores") + 1L, because option mc.cores specifies “allowed number of additional R processes” to be used in addition to the main R process.
multicore() gained argument maxCores, which makes it possible to use for instance plan(multicore, maxCores = 4L).
Add availableMulticore() [from (in-house) async package].
ROBUSTNESS: multicore() blocks until one of the CPU cores is available, iff all are currently occupied by other multicore futures.
old <- plan(new) now returns the old plan/strategy (was the newly set one).
Eager and lazy futures now records the result internally such that the expression is only evaluated once, even if their error values are requested multiple times.
Eager futures are always created regardless of error or not.
All Future objects are environments themselves that record the expression, the call environment and optional variables.
plan() records the call.Added plan().
Added eager future - useful for troubleshooting.