{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import ray\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "import numpy as np\r\n",
    "from itertools import chain"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "global_random_seed = 3884\r\n",
    "np.random.seed(global_random_seed)\r\n",
    "\r\n",
    "\r\n",
    "def gene_test_data(num_points: int, low_val=-1, high_val=1) -> list:\r\n",
    "    # return [np.random.uniform(low_val, high_val) for _ in range(num_points)]\r\n",
    "    return np.linspace(-1, 1, num_points)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "ray.init()\r\n",
    "test_data = gene_test_data(1000)\r\n",
    "\r\n",
    "@ray.remote\r\n",
    "class Predictor:\r\n",
    "    def __init__(self):\r\n",
    "        self.model = self.get_network(1, 1, 40, 15)\r\n",
    "        self.result = []\r\n",
    "\r\n",
    "    def get_network(self, input_dim, output_dim, width, depth):\r\n",
    "        from tensorflow import keras\r\n",
    "        # Random weight\r\n",
    "        initializer = keras.initializers.RandomUniform(\r\n",
    "            minval=-1., maxval=1., seed=global_random_seed)\r\n",
    "\r\n",
    "        # trying constant weight\r\n",
    "        # initializer = keras.initializers.Constant(1)\r\n",
    "\r\n",
    "        # different activation functions\r\n",
    "        activation_func = keras.activations.tanh\r\n",
    "        # activation_func = keras.activations.relu\r\n",
    "        # activation_func = tf.math.sin\r\n",
    "        hidden_layers = [keras.layers.Dense(\r\n",
    "            width, activation=activation_func, kernel_initializer=initializer) for _ in range(depth-1)]\r\n",
    "        hidden_layers.append(keras.layers.Dense(\r\n",
    "            output_dim, kernel_initializer=initializer))\r\n",
    "        model = keras.Sequential(hidden_layers)\r\n",
    "        return model\r\n",
    "\r\n",
    "    def pred(self, input):\r\n",
    "        for ip in input:\r\n",
    "            self.result.append(self.model.predict([ip]))\r\n",
    "\r\n",
    "    def get_result(self):\r\n",
    "        return self.result\r\n"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\lzl12\\miniconda3\\envs\\tf\\lib\\site-packages\\ray\\_private\\services.py:238: UserWarning: Not all Ray Dashboard dependencies were found. To use the dashboard please install Ray using `pip install ray[default]`. To disable this message, set RAY_DISABLE_IMPORT_WARNING env var to '1'.\n",
      "  warnings.warn(warning_message)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "actor_num = 2\r\n",
    "predictors = [Predictor.remote() for _ in range(actor_num)]\r\n",
    "for i in range(actor_num):\r\n",
    "    data_piece = int((len(test_data)/actor_num))\r\n",
    "    predictors[i].pred.remote(\r\n",
    "        test_data[i*data_piece:(i+1)*data_piece])\r\n",
    "\r\n",
    "join_data = [p.get_result.remote() for p in predictors]\r\n",
    "result = ray.get(join_data)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "\u001b[2m\u001b[36m(pid=11116)\u001b[0m 2021-09-04 23:05:21.099125: 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:  AVX AVX2\n",
      "\u001b[2m\u001b[36m(pid=11116)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "\u001b[2m\u001b[36m(pid=15320)\u001b[0m 2021-09-04 23:05:21.099125: 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:  AVX AVX2\n",
      "\u001b[2m\u001b[36m(pid=15320)\u001b[0m To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "\u001b[2m\u001b[36m(pid=11116)\u001b[0m 2021-09-04 23:05:21.709513: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5979 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n",
      "\u001b[2m\u001b[36m(pid=15320)\u001b[0m 2021-09-04 23:05:21.709140: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1510] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 5979 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 2070 SUPER, pci bus id: 0000:01:00.0, compute capability: 7.5\n",
      "\u001b[2m\u001b[36m(pid=11116)\u001b[0m 2021-09-04 23:05:22.438270: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n",
      "\u001b[2m\u001b[36m(pid=15320)\u001b[0m 2021-09-04 23:05:23.433687: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:185] None of the MLIR Optimization Passes are enabled (registered 2)\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "from tensorflow import keras\r\n",
    "def get_network(input_dim, output_dim, width, depth):\r\n",
    "    # Random weight\r\n",
    "    initializer = keras.initializers.RandomUniform(\r\n",
    "        minval=-1., maxval=1., seed=global_random_seed)\r\n",
    "\r\n",
    "    # trying constant weight\r\n",
    "    # initializer = keras.initializers.Constant(1)\r\n",
    "\r\n",
    "    # different activation functions\r\n",
    "    activation_func = keras.activations.tanh\r\n",
    "    # activation_func = keras.activations.relu\r\n",
    "    # activation_func = tf.math.sin\r\n",
    "    hidden_layers = [keras.layers.Dense(\r\n",
    "        width, activation=activation_func, kernel_initializer=initializer) for _ in range(depth-1)]\r\n",
    "    hidden_layers.append(keras.layers.Dense(\r\n",
    "        output_dim, kernel_initializer=initializer))\r\n",
    "    model = keras.Sequential(hidden_layers)\r\n",
    "    return model\r\n",
    "\r\n",
    "\r\n",
    "model = get_network(1, 1, 40, 15)\r\n",
    "result = list(map(lambda x: model.predict([x]), test_data))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "result2 = list(chain.from_iterable(result))\r\n",
    "fft_result = np.fft.fft(result2)\r\n",
    "print(fft_result)\r\n",
    "plt.figure()\r\n",
    "# plt.plot(range(len(fft_test)), fft_test)\r\n",
    "# # plt.scatter(range(len(test_data)), test_data, s=1)\r\n",
    "plt.scatter(range(len(result2)), np.real(fft_result), s=2)\r\n",
    "plt.title(\"FFT Result\")\r\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
  }
 ],
 "metadata": {
  "orig_nbformat": 4,
  "language_info": {
   "name": "python",
   "version": "3.8.11",
   "mimetype": "text/x-python",
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "pygments_lexer": "ipython3",
   "nbconvert_exporter": "python",
   "file_extension": ".py"
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.11 64-bit ('tf': conda)"
  },
  "interpreter": {
   "hash": "83eb3a94852d7da6265f5c02f9c348bfbb051e60fbf28829dfd0a8aff7ca6bfd"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}