gpu_xgboost

xgb_params = {
  "booster": "gbtree",
  "objective": "binary:logistic",
  "eta": 0.1,
  "gamma": 0,
  "min_child_weight": 200,
  "max_depth": 6,
  "eval_train": 1,
  "tree_method": "gpu_hist",
  "eval_metric": "auc",
  "colsample_bylevel": 0.3,
  "silent": False,
}

xgb_model = None

for features, labels in dataset:
  flat_features = flatten(features)
  flat_labels = flatten(labels)
  
  dtrain = xgb.DMatrix(flat_features, flat_labels)
  
  xgb_model = xgb.train(
    params=xgb_params, 
    dtrain=dtrain,
    num_boost_round=1000,
    xgb_model=xgb_model)

  # Update xgb params for future
  xgb_params.update({
  "updater": "grow_gpu_hist",
  })