R 4.0 (Fri, 20 Mar 2020) #1
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Use the following to install xgboost with R 4.0 and Intel MKL: git clone --recursive https://github.com/dmlc/xgboost
cd xgboost
mkdir build
cd build
cmake .. -DR_LIB=ON -DUSE_CUDA=ON -DCMAKE_C_COMPILER=/usr/bin/gcc-7 -DCMAKE_CXX_COMPILER=/usr/bin/g++-7 -DUSE_NCCL=ON -DNCCL_ROOT=/usr/lib/x86_64-linux-gnu
make -j 36
make install -I/opt/intel/compilers_and_libraries_2019.4.243/linux/mkl/lib/intel64_lin/
sudo R CMD INSTALL ./R-packageTest case: library(xgboost)
set.seed(1)
N <- 500000
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))
tr <- sample.int(N, N * 0.90)
trainer <- function(n_cpus, n_gpus, n_iterations) {
dtrain <- xgb.DMatrix(X[tr,], label = y[tr])
dtest <- xgb.DMatrix(X[-tr,], label = y[-tr])
wl <- list(test = dtest)
if (n_gpus == 0) {
pt <- proc.time()
model <- xgb.train(list(objective = "reg:logistic", eval_metric = "logloss", subsample = 0.8, nthread = n_cpus, eta = 0.10,
max_bin = 64, tree_method = "hist"),
dtrain, watchlist = wl, nrounds = n_iterations)
my_time <- proc.time() - pt
} else {
pt <- proc.time()
model <- xgb.train(list(objective = "reg:logistic", eval_metric = "logloss", subsample = 0.8, nthread = n_cpus, eta = 0.10,
max_bin = 64, tree_method = "gpu_hist", n_gpus = n_gpus),
dtrain, watchlist = wl, nrounds = n_iterations)
my_time <- proc.time() - pt
}
rm(model, dtrain, dtest)
gc(verbose = FALSE)
return(my_time)
}
trainer(1, 1, 50)
trainer(1, 0, 50)Results: R Under development (unstable) (2020-03-27 r78091) -- "Unsuffered Consequences"
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> library(xgboost)
>
> set.seed(1)
> N <- 500000
> 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))
>
> tr <- sample.int(N, N * 0.90)
>
> trainer <- function(n_cpus, n_gpus, n_iterations) {
+
+ dtrain <- xgb.DMatrix(X[tr,], label = y[tr])
+ dtest <- xgb.DMatrix(X[-tr,], label = y[-tr])
+ wl <- list(test = dtest)
+
+ if (n_gpus == 0) {
+
+ pt <- proc.time()
+ model <- xgb.train(list(objective = "reg:logistic", eval_metric = "logloss", subsample = 0.8, nthread = n_cpus, eta = 0.10,
+ max_bin = 64, tree_method = "hist"),
+ dtrain, watchlist = wl, nrounds = n_iterations)
+ my_time <- proc.time() - pt
+
+ } else {
+
+ pt <- proc.time()
+ model <- xgb.train(list(objective = "reg:logistic", eval_metric = "logloss", subsample = 0.8, nthread = n_cpus, eta = 0.10,
+ max_bin = 64, tree_method = "gpu_hist", n_gpus = n_gpus),
+ dtrain, watchlist = wl, nrounds = n_iterations)
+ my_time <- proc.time() - pt
+
+ }
+
+ rm(model, dtrain, dtest)
+ gc(verbose = FALSE)
+
+ return(my_time)
+
+ }
>
> trainer(1, 1, 50)
[18:44:17] WARNING: /home/laurae/Downloads/R/xgboost/include/xgboost/generic_parameters.h:36:
n_gpus:
Deprecated. Single process multi-GPU training is no longer supported.
Please switch to distributed training with one process per GPU.
This can be done using Dask or Spark. See documentation for details.
[1] test-logloss:0.687465
[2] test-logloss:0.682720
[3] test-logloss:0.678590
[4] test-logloss:0.675024
[5] test-logloss:0.671988
[6] test-logloss:0.669297
[7] test-logloss:0.666885
[8] test-logloss:0.664741
[9] test-logloss:0.662744
[10] test-logloss:0.660938
[11] test-logloss:0.659223
[12] test-logloss:0.657735
[13] test-logloss:0.656362
[14] test-logloss:0.655067
[15] test-logloss:0.653892
[16] test-logloss:0.652753
[17] test-logloss:0.651746
[18] test-logloss:0.650809
[19] test-logloss:0.649926
[20] test-logloss:0.649096
[21] test-logloss:0.648273
[22] test-logloss:0.647593
[23] test-logloss:0.646822
[24] test-logloss:0.646082
[25] test-logloss:0.645458
[26] test-logloss:0.644867
[27] test-logloss:0.644329
[28] test-logloss:0.643732
[29] test-logloss:0.643188
[30] test-logloss:0.642666
[31] test-logloss:0.642239
[32] test-logloss:0.641737
[33] test-logloss:0.641328
[34] test-logloss:0.640816
[35] test-logloss:0.640435
[36] test-logloss:0.640006
[37] test-logloss:0.639663
[38] test-logloss:0.639272
[39] test-logloss:0.638916
[40] test-logloss:0.638568
[41] test-logloss:0.638268
[42] test-logloss:0.637945
[43] test-logloss:0.637617
[44] test-logloss:0.637404
[45] test-logloss:0.637157
[46] test-logloss:0.636902
[47] test-logloss:0.636716
[48] test-logloss:0.636500
[49] test-logloss:0.636262
[50] test-logloss:0.636045
user system elapsed
3.687 0.852 4.666
> trainer(1, 0, 50)
[1] test-logloss:0.687480
[2] test-logloss:0.682716
[3] test-logloss:0.678664
[4] test-logloss:0.675134
[5] test-logloss:0.672144
[6] test-logloss:0.669425
[7] test-logloss:0.666906
[8] test-logloss:0.664728
[9] test-logloss:0.662718
[10] test-logloss:0.661065
[11] test-logloss:0.659484
[12] test-logloss:0.657958
[13] test-logloss:0.656504
[14] test-logloss:0.655166
[15] test-logloss:0.654018
[16] test-logloss:0.652931
[17] test-logloss:0.651872
[18] test-logloss:0.650910
[19] test-logloss:0.650024
[20] test-logloss:0.649156
[21] test-logloss:0.648393
[22] test-logloss:0.647713
[23] test-logloss:0.646957
[24] test-logloss:0.646182
[25] test-logloss:0.645562
[26] test-logloss:0.644967
[27] test-logloss:0.644405
[28] test-logloss:0.643904
[29] test-logloss:0.643418
[30] test-logloss:0.642868
[31] test-logloss:0.642303
[32] test-logloss:0.641841
[33] test-logloss:0.641390
[34] test-logloss:0.640920
[35] test-logloss:0.640521
[36] test-logloss:0.640097
[37] test-logloss:0.639677
[38] test-logloss:0.639321
[39] test-logloss:0.638976
[40] test-logloss:0.638593
[41] test-logloss:0.638342
[42] test-logloss:0.637964
[43] test-logloss:0.637667
[44] test-logloss:0.637394
[45] test-logloss:0.637112
[46] test-logloss:0.636879
[47] test-logloss:0.636631
[48] test-logloss:0.636388
[49] test-logloss:0.636186
[50] test-logloss:0.635976
user system elapsed
19.236 0.167 19.412 |
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Use the following to install LightGBM with R 4.0: git clone --recursive https://github.com/microsoft/LightGBM
cd LightGBM
nano ./R-package/src/install.libs.R
[change `use_GPU <- FALSE` to `use_GPU <- TRUE`, Ctrl+X + Y + Enter]
sudo Rscript build_r.RTest case: library(lightgbm)
library(Matrix)
data(agaricus.train, package = "lightgbm")
train <- agaricus.train
train$data[, 1] <- 1:6513
dtrain <- lgb.Dataset(train$data, label = train$label)
data(agaricus.test, package = "lightgbm")
test <- agaricus.test
dtest <- lgb.Dataset.create.valid(dtrain, test$data, label = test$label)
valids <- list(test = dtest)
params <- list(objective = "regression",
metric = "rmse",
device = "gpu",
gpu_platform_id = 0,
gpu_device_id = 0,
nthread = 1,
boost_from_average = FALSE,
max_bin = 32)
model <- lgb.train(params,
dtrain,
2,
valids,
min_data = 1,
learning_rate = 1,
early_stopping_rounds = 10) |
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