list.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-18 16:55:33.427987: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-04-18 16:55:34.288203: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tidak ada GPU yang tersedia\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-18 16:55:35.253446: I external/local_xla/xla/stream_executor/cuda/cuda_executor.cc:998] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero. See more at https://github.com/torvalds/linux/blob/v6.0/Documentation/ABI/testing/sysfs-bus-pci#L344-L355\n",
      "2024-04-18 16:55:35.258694: W tensorflow/core/common_runtime/gpu/gpu_device.cc:2251] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform.\n",
      "Skipping registering GPU devices...\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import os; os.environ['TF_CPP_MIN_LOG_LEVEL'] = '1'\n",
    "\n",
    "# Cek apakah GPU tersedia\n",
    "print(\"GPU tersedia:\" if tf.config.list_physical_devices('GPU') else \"Tidak ada GPU yang tersedia\")\n",
    "\n",
    "# Tampilkan informasi tentang GPU yang digunakan\n",
    "if tf.config.list_physical_devices('GPU'):\n",
    "    physical_devices = tf.config.list_physical_devices('GPU')\n",
    "    for device in physical_devices:\n",
    "        print(\"Nama GPU:\", device.name)\n",
    "        print(\"Spesifikasi GPU:\", device)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Perangkat CUDA 0: NVIDIA GeForce MX250\n",
      "  Versi CUDA: (6, 1)\n"
     ]
    }
   ],
   "source": [
    "import pycuda.driver as cuda\n",
    "\n",
    "# Inisialisasi CUDA\n",
    "cuda.init()\n",
    "\n",
    "# Dapatkan jumlah perangkat CUDA yang tersedia\n",
    "num_devices = cuda.Device.count()\n",
    "\n",
    "# Cetak informasi tentang setiap perangkat CUDA\n",
    "for i in range(num_devices):\n",
    "    device = cuda.Device(i)\n",
    "    print(\"Perangkat CUDA {}: {}\".format(i, device.name()))\n",
    "    print(\"  Versi CUDA:\", device.compute_capability())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "print(torch.cuda.is_available()) # should be True\n",
    "\n",
    "t = torch.rand(10, 10).cuda()\n",
    "print(t.device) # should be CUDA"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}