Hello everyone,
I’ve been trying to install xgboost by clone the souce code from github. I complied code by visual studio 2019.
However, it works well when I trains data by hist tree_method, but it always appears a crack when tree_method is gpu_hist.
Is anyone know how to debug it?
My environment:
- Windows 10
- cuda 10.1
- R 3.6.1
devtools::session_info()
- Session info -----------------------------------------------------------------------------------------------
setting value
version R version 3.6.1 (2019-07-05)
os Windows 10 x64
system x86_64, mingw32
ui RStudio
language (EN)
collate English_United States.1252
ctype English_United States.1252
tz America/New_York
date 2019-08-02
- Packages ---------------------------------------------------------------------------------------------------
package * version date lib source
assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.1)
backports 1.1.4 2019-04-10 [1] CRAN (R 3.6.0)
callr 3.3.1 2019-07-18 [1] CRAN (R 3.6.1)
cli 1.1.0 2019-03-19 [1] CRAN (R 3.6.1)
crayon 1.3.4 2017-09-16 [1] CRAN (R 3.6.1)
data.table 1.12.2 2019-04-07 [1] CRAN (R 3.6.1)
desc 1.2.0 2018-05-01 [1] CRAN (R 3.6.1)
devtools * 2.1.0 2019-07-06 [1] CRAN (R 3.6.1)
digest 0.6.20 2019-07-04 [1] CRAN (R 3.6.1)
fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.1)
glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.1)
lattice 0.20-38 2018-11-04 [2] CRAN (R 3.6.1)
magrittr 1.5 2014-11-22 [1] CRAN (R 3.6.1)
Matrix 1.2-17 2019-03-22 [2] CRAN (R 3.6.1)
memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.1)
pkgbuild 1.0.3 2019-03-20 [1] CRAN (R 3.6.1)
pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.1)
prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.6.1)
processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.1)
ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.1)
R6 2.4.0 2019-02-14 [1] CRAN (R 3.6.1)
Rcpp 1.0.1 2019-03-17 [1] CRAN (R 3.6.0)
remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.1)
rlang 0.4.0 2019-06-25 [1] CRAN (R 3.6.1)
rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.6.1)
rstudioapi 0.10 2019-03-19 [1] CRAN (R 3.6.1)
sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.1)
stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
testthat 2.2.1 2019-07-25 [1] CRAN (R 3.6.1)
usethis * 1.5.1 2019-07-04 [1] CRAN (R 3.6.1)
withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.1)
xgboost * 1.0.0.1 2019-08-02 [1] local
[1] C:/Users/alfre/Documents/R/win-library/3.6
[2] C:/Program Files/R/R-3.6.1/library
rsession.log
02 Aug 2019 18:16:23 [rsession-alfre] ERROR system error 2 (The system cannot find the file specified) [path=C:/Users/alfre/AppData/Local/RStudio-Desktop/jobs/E9477DBB-output.json]; OCCURRED AT: auto __cdecl rstudio::core::FilePath::open_r::<lambda_7681044d383654bc1b82d8906e771cc1>::operator ()(void) const c:\jenkins\workspace\ide\windows-v1.2\src\cpp\core\filepath.cpp:1092; LOGGED FROM: class std::vector<class json_spirit::Value_impl<struct json_spirit::Config_map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > >,class std::allocator<class json_spirit::Value_impl<struct json_spirit::Config_map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > > __cdecl rstudio::session::modules::jobs::Job::output(int) c:\jenkins\workspace\ide\windows-v1.2\src\cpp\session\modules\jobs\job.cpp:433
02 Aug 2019 18:16:39 [rsession-alfre] ERROR system error 2 (The system cannot find the file specified) [path=C:/Users/alfre/AppData/Local/RStudio-Desktop/jobs/E9477DBB-output.json]; OCCURRED AT: auto __cdecl rstudio::core::FilePath::open_r::<lambda_7681044d383654bc1b82d8906e771cc1>::operator ()(void) const c:\jenkins\workspace\ide\windows-v1.2\src\cpp\core\filepath.cpp:1092; LOGGED FROM: class std::vector<class json_spirit::Value_impl<struct json_spirit::Config_map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > >,class std::allocator<class json_spirit::Value_impl<struct json_spirit::Config_map<class std::basic_string<char,struct std::char_traits<char>,class std::allocator<char> > > > > > __cdecl rstudio::session::modules::jobs::Job::output(int) c:\jenkins\workspace\ide\windows-v1.2\src\cpp\session\modules\jobs\job.cpp:433
02 Aug 2019 19:56:53 [rsession-alfre] ERROR system error 10053 (An established connection was aborted by the software in your host machine) [request-uri=/events/get_events]; OCCURRED AT: void __cdecl rstudio::session::HttpConnectionImpl<class rstudio_boost::asio::ip::tcp>::sendResponse(const class rstudio::core::http::Response &) c:\jenkins\workspace\ide\windows-v1.2\src\cpp\session\http\sessionhttpconnectionimpl.hpp:111; LOGGED FROM: void __cdecl rstudio::session::HttpConnectionImpl<class rstudio_boost::asio::ip::tcp>::sendResponse(const class rstudio::core::http::Response &) c:\jenkins\workspace\ide\windows-v1.2\src\cpp\session\http\sessionhttpconnectionimpl.hpp:116
02 Aug 2019 20:27:48 [rsession-alfre] ERROR system error 10053 (An established connection was aborted by the software in your host machine) [request-uri=/events/get_events]; OCCURRED AT: void __cdecl rstudio::session::HttpConnectionImpl<class rstudio_boost::asio::ip::tcp>::sendResponse(const class rstudio::core::http::Response &) c:\jenkins\workspace\ide\windows-v1.2\src\cpp\session\http\sessionhttpconnectionimpl.hpp:111; LOGGED FROM: void __cdecl rstudio::session::HttpConnectionImpl<class rstudio_boost::asio::ip::tcp>::sendResponse(const class rstudio::core::http::Response &) c:\jenkins\workspace\ide\windows-v1.2\src\cpp\session\http\sessionhttpconnectionimpl.hpp:116
Here is my R code
library('xgboost')
# Simulate N x p random matrix with some binomial response dependent on pp columns
set.seed(111)
N <- 1000000
p <- 50
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 * 0.75)
dtrain <- xgb.DMatrix(X[tr,], label = y[tr])
dtest <- xgb.DMatrix(X[-tr,], label = y[-tr])
wl <- list(train = dtrain, test = dtest)
param <- list(objective = 'reg:logistic', eval_metric = 'auc', subsample = 0.5, nthread = 4,
max_bin = 64, tree_method = 'gpu_hist')
pt <- proc.time()
bst_gpu <- xgb.train(param, dtrain, watchlist = wl, nrounds = 5)
proc.time() - pt
# Compare to the 'hist' algorithm:
param <- list(objective = 'reg:logistic', eval_metric = 'auc', subsample = 0.5, nthread = 4,
max_bin = 64, tree_method = 'hist')
pt <- proc.time()
bst_hist <- xgb.train(param, dtrain, watchlist = wl, nrounds = 5)
proc.time() - pt
