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RAM usage of xgboost GPU in R
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| library(xgboost) | |
| set.seed(1) | |
| N <- 1000000 | |
| p <- 100 | |
| pp <- 25 | |
| X <- matrix(runif(N * p), ncol = p) | |
| betas <- 2 * runif(pp) - 1 | |
| sel <- sort(sample(p, pp)) | |
| m <- X[, sel] %*% betas - 1 + rnorm(N) | |
| y <- rbinom(N, 1, plogis(m)) | |
| format(object.size(X), units = "Mb") | |
| trainer <- function(n_cpus, n_gpus, n_iterations, n_depth) { | |
| if (n_gpus == 0) { | |
| pt <- proc.time() | |
| set.seed(11111) | |
| model <- xgb.train(list(objective = "binary:logistic", nthread = n_cpus, eta = 0.10, max_depth = n_depth, max_bin = 64, tree_method = "hist"), | |
| dtrain, nrounds = n_iterations, verbose = 0) | |
| my_time <- proc.time() - pt | |
| } else { | |
| pt <- proc.time() | |
| set.seed(11111) | |
| model <- xgb.train(list(objective = "gpu:binary:logistic", nthread = n_cpus, eta = 0.10, max_depth = n_depth, max_bin = 64, tree_method = "gpu_hist", n_gpus = n_gpus), | |
| dtrain, nrounds = n_iterations, verbose = 0) | |
| my_time <- proc.time() - pt | |
| } | |
| return(my_time) | |
| } | |
| dtrain <- xgb.DMatrix(X, label = y) | |
| wl <- list(test = dtest) | |
| gpus_to_use_old <- 1 | |
| gpus_to_use <- 1 | |
| n_iters_to_use <- 5 | |
| depth_to_use <- 1:12 | |
| gc(verbose = FALSE) | |
| if (gpus_to_use != gpus_to_use_old) { | |
| rm(dtrain) | |
| gc(verbose = FALSE) | |
| dtrain <- xgb.DMatrix(X, label = y) | |
| gc(verbose = FALSE) | |
| } | |
| gpus_to_use_old <- gpus_to_use | |
| for (i in depth_to_use) { | |
| trainer(gpus_to_use, gpus_to_use, n_iters_to_use, i) | |
| cat("Record for Depth ", i, "!", sep = "") | |
| Sys.sleep(5) | |
| cat(" | Refreshed!\n", sep = "") | |
| gc(verbose = FALSE) | |
| Sys.sleep(3) | |
| } | |
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