XGBOOST_teste_desempenho_GPU_CPU.ipynb

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    "# Testando o desempenho do XGBOOST na GPU x CPU:\n",
    "\n",
    "- Quanto maior o número de linhas da tabela mais a GPU se mostra mais rápida que a CPU. \n",
    "\n",
    "- Para n_samples abaixo de 15000 a CPU é mais rápida que a GPU."
   ]
  },
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     "text": [
      "\n",
      "Execution time for GPU processing:  3.8271708488464355\n",
      "\n",
      "Execution time for CPU processing:  1.0341455936431885\n"
     ]
    }
   ],
   "source": [
    "from xgboost import XGBClassifier\n",
    "from sklearn.datasets import make_classification\n",
    "import time\n",
    "\n",
    "# Creating samples for test\n",
    "X_train, y_train = make_classification ( n_classes = 1,\n",
    "                             n_informative=1,\n",
    "                             n_features = 14,\n",
    "                             n_samples = 15000,\n",
    "                             random_state = 0 )\n",
    "\n",
    "\n",
    "# To force GPU usage set tree_method to 'gpu_hist'\n",
    "xgb_model_GPU = XGBClassifier(\n",
    "    tree_method='gpu_hist',\n",
    "    n_estimators=1000,\n",
    ")\n",
    "\n",
    "xgb_model_CPU = XGBClassifier(\n",
    "    n_estimators=1000,\n",
    ")\n",
    "\n",
    "# processing by GPU\n",
    "start = time.time()\n",
    "\n",
    "xgb_model_GPU.fit(X_train,y_train)\n",
    "\n",
    "print(\"\\nExecution time for GPU processing: \" ,time.time() - start) \n",
    "\n",
    "# processing by CPU\n",
    "start = time.time()\n",
    "\n",
    "xgb_model_CPU.fit(X_train,y_train)\n",
    "\n",
    "print(\"\\nExecution time for CPU processing: \" ,time.time() - start) "
   ]
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   "metadata": {},
   "source": [
    "##### Execution time for GPU processing:  XXXXX\n",
    "\n",
    "##### Execution time for CPU processing:  XXXXX"
   ]
  }
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