tar_test("future workers actually launch (run interactively)", { skip_if_not_installed("future") skip_if_not_installed("future.callr") tar_script({ future::plan(future.callr::callr) list( tar_target(x, seq_len(4)), tar_target( y, Sys.sleep(30), pattern = map(x) ) ) }) # The following should run 4 targets concurrently. # Terminate early if necessary. tar_make_future(workers = 4) out <- tar_progress() out <- out[out$name != "x", ] expect_true(all(out$progress == "started")) tar_destroy() }) # Run interactively: tar_test("custom future plans through structured resources", { skip_if_not_installed("future") tar_script({ future::plan(future::multisession, workers = 4) plan_multisession <- future::plan() future::plan(future::sequential) list( tar_target(x, seq_len(4)), tar_target( y, Sys.sleep(30), pattern = map(x), resources = tar_resources( future = tar_resources_future(plan = plan_multisession) ) ) ) }) # The following should run 4 targets concurrently. tar_make_future(workers = 4) # After all 4 targets start, terminate the pipeline early and show progress. # x should be built, and y and its 4 branches should be listed as started. out <- tar_progress() out <- out[out$name != "x", ] expect_true(all(out$progress == "started")) tar_destroy() }) tar_test("custom future plans through unstructured resources", { skip_if_not_installed("future") tar_script({ future::plan(future::multisession, workers = 4) plan_multisession <- future::plan() future::plan(future::sequential) suppressWarnings( list( tar_target(x, seq_len(4)), tar_target( y, Sys.sleep(30), pattern = map(x), resources = list(plan = plan_multisession) ) ) ) }) # The following should run 4 targets concurrently. tar_make_future(workers = 4) # After all 4 targets start, terminate the pipeline early and show progress. # x should be built, and y and its 4 branches should be listed as started. out <- tar_progress() out <- out[out$name != "x", ] expect_true(all(out$progress == "started")) tar_destroy() }) tar_test("parallel workload should run fast", { skip_if_not_installed("future") skip_if_not_installed("future.callr") tar_script({ library(targets) future::plan(future.callr::callr) list( tar_target( index_batch, seq_len(20), ), tar_target( data_continuous, index_batch, pattern = map(index_batch) ), tar_target( data_discrete, index_batch, pattern = map(index_batch) ), tar_target( fit_continuous, data_continuous, pattern = map(data_continuous) ), tar_target( fit_discrete, data_discrete, pattern = map(data_discrete) ) ) }) tar_make_future(workers = 4, callr_function = NULL) expect_equal(tar_outdated(), character(0)) expect_equal(unname(tar_read(fit_continuous)), seq_len(20)) expect_equal(unname(tar_read(fit_discrete)), seq_len(20)) }) tar_test("profile parallel workload", { skip_if_not_installed("future") skip_if_not_installed("future.callr") tar_script({ library(targets) future::plan(future.callr::callr) list( tar_target( index_batch, seq_len(10), ), tar_target( data_continuous, index_batch, pattern = map(index_batch) ), tar_target( data_discrete, index_batch, pattern = map(index_batch) ), tar_target( fit_continuous, data_continuous, pattern = map(data_continuous) ), tar_target( fit_discrete, data_discrete, pattern = map(data_discrete) ) ) }) # Should deploy targets in a timely manner. proffer::pprof(tar_make_future(workers = 4, callr_function = NULL)) }) tar_test("prevent high-memory data via target objects", { skip_if_not_installed("future") skip_if_not_installed("future.callr") tar_runtime$fun <- "tar_make_future" on.exit(tar_runtime$fun <- NULL) # Run this test once inside tar_test() (test environment) # and once outside tar_test() global environment. future::plan(future.callr::callr) t <- list(tar_target(x, runif(1e7), deployment = "main", format = "qs")) pipeline <- pipeline_init(list(t[[1]], tar_target(y, x))) algo <- future_init(pipeline) debug(algo$ensure_exports) tar_option_set(envir = environment()) # should enter a debugger: algo$run() # In the debugger verify that the exported data is much smaller than # the value of x because we cloned the target objects in pipeline_init(). o <- self$produce_exports(tar_option_get("envir"), path_store_default()) # Exported data should be small: pryr::object_size(o) # The target object should not be in the environment. expect_true(inherits(tar_option_get("envir")$t[[1]], "tar_target")) pryr::object_size(tar_option_get("envir")$t[[1]]) # The pipeline's copy of the target object should be much larger: pryr::object_size(pipeline_get_target(self$pipeline, "x")$value$object) # The algorithm object itself should be large too, and it is not exported. pryr::object_size(self) })