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#' MXNet: Flexible and Efficient GPU computing and Deep Learning.
#' MXNet is a flexible and efficient GPU computing and deep learning framework.
#' It enables you to write seamless tensor/matrix computation with multiple GPUs in R.
#' It also enables you construct and customize the state-of-art deep learning models in R,
#' and apply them to tasks such as image classification and data science challenges.
#' @docType package
#' @name mxnet
#' @import methods Rcpp
.MXNetEnv <- new.env()
.onLoad <- function(libname, pkgname) {
# Require methods for older versions of R
library.dynam("libmxnet", pkgname, libname, local=FALSE)
library.dynam("mxnet", pkgname, libname)
loadModule("mxnet", TRUE)
.onUnload <- function(libpath) {
message("Start unload")
library.dynam.unload("mxnet", libpath)
library.dynam.unload("libmxnet", libpath)
message("MXNet shutdown")
.onAttach <- function(...) {
if (!interactive() || stats::runif(1) > 0.1) return()
tips <- c(
"Need help? Feel free to open an issue on",
"For more documents, please visit",
"Use suppressPackageStartupMessages() to eliminate package startup messages."
tip <- sample(tips, 1)
packageStartupMessage(paste(strwrap(tip), collapse = "\n"))