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{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.analyticsvidhya.com/blog/2021/11/benchmarking-cpu-and-gpu-performance-with-tensorflow/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "(X_train, y_train), (X_test, y_test) = keras.datasets.cifar10.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaling image values between 0-1\n",
    "X_train_scaled = X_train/255\n",
    "X_test_scaled = X_test/255\n",
    "y_train_encoded = keras.utils.to_categorical(y_train, num_classes = 10, dtype = 'float32')\n",
    "y_test_encoded = keras.utils.to_categorical(y_test, num_classes = 10, dtype = 'float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    model = keras.Sequential([\n",
    "        keras.layers.Flatten(input_shape=(32,32,3)),\n",
    "        keras.layers.Dense(3000, activation='relu'),\n",
    "        keras.layers.Dense(1000, activation='relu'),\n",
    "        keras.layers.Dense(10, activation='sigmoid')    \n",
    "    ])\n",
    "    model.compile(optimizer='SGD',\n",
    "              loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-14 08:17:31.149910: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE3 SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2021-12-14 08:17:31.150614: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 7676 MB memory:  -> device: 0, name: AMD Radeon RX 6600 XT, pci bus id: 0000:08:00.0\n",
      "2021-12-14 08:17:32.735544: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "2021-12-14 08:17:32.765031: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n",
      "2021-12-14 08:17:32.768862: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n",
      "2021-12-14 08:17:32.770708: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n",
      "2021-12-14 08:17:32.894871: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.8109 - accuracy: 0.3557\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 24s 15ms/step - loss: 1.6225 - accuracy: 0.4273\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.5435 - accuracy: 0.4561\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 24s 15ms/step - loss: 1.4820 - accuracy: 0.4785\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.4333 - accuracy: 0.4949\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.3896 - accuracy: 0.5116\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.3511 - accuracy: 0.5241\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.3165 - accuracy: 0.5384\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 24s 15ms/step - loss: 1.2830 - accuracy: 0.5497\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 23s 15ms/step - loss: 1.2524 - accuracy: 0.5605\n",
      "3min 55s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "# CPU\n",
    "with tf.device('/CPU:0'):\n",
    "    model_cpu = get_model()\n",
    "    model_cpu.fit(X_train_scaled, y_train_encoded, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2021-12-14 08:21:53.663198: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n",
      "2021-12-14 08:21:53.665473: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n",
      "2021-12-14 08:21:53.667073: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n",
      "2021-12-14 08:21:53.776133: I tensorflow/core/common_runtime/gpu_fusion_pass.cc:507] ROCm Fusion is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1563/1563 [==============================] - 21s 3ms/step - loss: 1.8134 - accuracy: 0.3534\n",
      "Epoch 2/10\n",
      "1563/1563 [==============================] - 7s 4ms/step - loss: 1.6232 - accuracy: 0.4246\n",
      "Epoch 3/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.5409 - accuracy: 0.4573\n",
      "Epoch 4/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.4833 - accuracy: 0.4787\n",
      "Epoch 5/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.4319 - accuracy: 0.4979\n",
      "Epoch 6/10\n",
      "1563/1563 [==============================] - 7s 5ms/step - loss: 1.3883 - accuracy: 0.5114\n",
      "Epoch 7/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.3526 - accuracy: 0.5279\n",
      "Epoch 8/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.3171 - accuracy: 0.5377\n",
      "Epoch 9/10\n",
      "1563/1563 [==============================] - 5s 3ms/step - loss: 1.2844 - accuracy: 0.5471\n",
      "Epoch 10/10\n",
      "1563/1563 [==============================] - 7s 5ms/step - loss: 1.2505 - accuracy: 0.5631\n",
      "1min 13s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%%timeit -n1 -r1\n",
    "# GPU\n",
    "with tf.device('/GPU:0'):\n",
    "    model_gpu = get_model()\n",
    "    model_gpu.fit(X_train_scaled, y_train_encoded, epochs = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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