cuml_fil_load_model: Load a XGBoost or LightGBM model file.
In cuml: R Interface for the RAPIDS cuML Suite of Libraries

Description Usage Arguments Value Examples

Load a XGBoost or LightGBM model file using Treelite. The resulting model object can be used to perform high-throughput batch inference on new data points using the GPU acceleration functionality from the CuML Forest Inference Library (FIL).

cuml_fil_load_model(
  filename,
  mode = c("classification", "regression"),
  model_type = c("xgboost", "lightgbm"),
  algo = c("auto", "naive", "tree_reorg", "batch_tree_reorg"),
  threshold = 0.5,
  storage_type = c("auto", "dense", "sparse"),
  threads_per_tree = 1L,
  n_items = 0L,
  blocks_per_sm = 0L
)

`filename`	Path to the saved model file.
`mode`	Type of task to be performed by the model. Must be one of "classification", "regression".
`model_type`	Format of the saved model file. Notice if `filename` ends with ".json" and `model_type` is "xgboost", then cuml will assume the model file is in XGBoost JSON (instead of binary) format. Default: "xgboost".
`algo`	Type of the algorithm for inference, must be one of the following. - "auto": Choose the algorithm automatically. Currently 'batch_tree_reorg' is used for dense storage, and 'naive' for sparse storage. - "naive": Simple inference using shared memory. - "tree_reorg": Similar to naive but with trees rearranged to be more coalescing- friendly. - "batch_tree_reorg": Similar to 'tree_reorg' but predicting multiple rows per thread block. Default: "auto".
`threshold`	Class probability threshold for classification. Ignored for regression tasks. Default: 0.5.
`storage_type`	In-memory storage format of the FIL model. Must be one of the following. - "auto": Choose the storage type automatically, - "dense": Create a dense forest, - "sparse": Create a sparse forest. Requires `algo` to be 'naive' or 'auto'.
`threads_per_tree`	If >1, then have multiple (neighboring) threads infer on the same tree within a block, which will improve memory bandwith near tree root (but consuming more shared memory). Default: 1L.
`n_items`	Number of input samples each thread processes. If 0, then choose (up to 4) that fit into shared memory. Default: 0L.
`blocks_per_sm`	Indicates how CuML should determine the number of thread blocks to lauch for the inference kernel. - 0: Launches the number of blocks proportional to the number of data points. - >= 1: Attempts to lauch `blocks_per_sm` blocks for each streaming multiprocessor. This will fail if `blocks_per_sm` blocks result in more threads than the maximum supported number of threads per GPU. Even if successful, it is not guaranteed that `blocks_per_sm` blocks will run on an SM concurrently.

A GPU-accelerated FIL model that can be used with the 'predict' S3 generic to make predictions on new data points.

library(cuml)
library(xgboost)

model_path <- file.path(tempdir(), "xgboost.model")

model <- xgboost(
  data = as.matrix(mtcars[which(names(mtcars) != "mpg")]),
  label = as.matrix(mtcars["mpg"]),
  max.depth = 6,
  eta = 1,
  nthread = 2,
  nrounds = 20,
  objective = "reg:squarederror"
)

xgb.save(model, model_path)

model <- cuml_fil_load_model(
  model_path,
  mode = "regression",
  model_type = "xgboost"
)

preds <- predict(model, mtcars[which(names(mtcars) != "mpg")])

print(preds)