I was only able to find below two posts on this topic -- barely any conversation. Is there any support or documentation for tidymodels
on using GPU?
When I try
library(tidymodels)
xg_model <-
boost_tree(
# trees = 1000,
# tree_depth = tune(), min_n = tune(),
# loss_reduction = tune(), ## first three: model complexity
# sample_size = tune(), mtry = tune(), ## randomness
# learn_rate = tune(), ## step size
) %>%
set_engine("xgboost", tree_method = 'gpu_hist') %>%
set_mode("classification") %>% fit(Species ~.,data=iris)
I get
Error in xgb.iter.update(bst$handle, dtrain, iteration - 1, obj) :
[09:00:18] amalgamation/../src/metric/../common/common.h:156: XGBoost version not compiled with GPU support.
Timing stopped at: 0 0 0
which would imply to me that it would use gpu, if my xgboost was compiled with that ability.
Right
Do you happen to know if there is a list of methods within tidymodels
that would use the GPU? It could be resampling methods, modelling, recipes, etc.
sorry, I have no idea about that.
probably another good place to ask might be the issues board on Issues · tidymodels/tidymodels (github.com)
You have to compile xgboost yourself if you want GPU support. CRAN doesn't build the package for that. See this thread.
tidymodels just calls the function so, I believe , that it should just work as long as the package itself can do it.
My understanding is the GPU will really shine when using deep learning models or XGBoost and other tree models (if they have GPU support). It's because you need thousands of processes that can run in parallel. I don't think GPU will provide any boost for data pre-processing or resampling.
Not it is easier to use XGBoost with GPU support in R.
Here more info: https://xgboost.readthedocs.io/en/latest/install.html#r