{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.5.5\n",
      "IPython 6.3.0\n",
      "\n",
      "numpy 1.14.2\n",
      "sklearn 0.19.1\n",
      "scipy 1.0.1\n",
      "matplotlib 2.2.2\n",
      "tensorflow 1.7.0\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p numpy,sklearn,scipy,matplotlib,tensorflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**12장 – 분산 텐서플로**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_이 노트북은 11장에 있는 모든 샘플 코드와 연습문제 해답을 가지고 있습니다._"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 설정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "파이썬 2와 3을 모두 지원합니다. 공통 모듈을 임포트하고 맷플롯립 그림이 노트북 안에 포함되도록 설정하고 생성한 그림을 저장하기 위한 함수를 준비합니다:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 파이썬 2와 파이썬 3 지원\n",
    "from __future__ import division, print_function, unicode_literals\n",
    "\n",
    "# 공통\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "# 일관된 출력을 위해 유사난수 초기화\n",
    "def reset_graph(seed=42):\n",
    "    tf.reset_default_graph()\n",
    "    tf.set_random_seed(seed)\n",
    "    np.random.seed(seed)\n",
    "\n",
    "# 맷플롯립 설정\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "# 그림을 저장할 폴더\n",
    "PROJECT_ROOT_DIR = \".\"\n",
    "CHAPTER_ID = \"distributed\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "    path = os.path.join(PROJECT_ROOT_DIR, \"images\", CHAPTER_ID, fig_id + \".png\")\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 로컬 서버"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "c = tf.constant(\"Hello distributed TensorFlow!\")\n",
    "server = tf.train.Server.create_local_server()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "b'Hello distributed TensorFlow!'\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(server.target) as sess:\n",
    "    print(sess.run(c))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 클러스터"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_spec = tf.train.ClusterSpec({\n",
    "    \"ps\": [\n",
    "        \"127.0.0.1:2221\",  # /job:ps/task:0\n",
    "        \"127.0.0.1:2222\",  # /job:ps/task:1\n",
    "    ],\n",
    "    \"worker\": [\n",
    "        \"127.0.0.1:2223\",  # /job:worker/task:0\n",
    "        \"127.0.0.1:2224\",  # /job:worker/task:1\n",
    "        \"127.0.0.1:2225\",  # /job:worker/task:2\n",
    "    ]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "task_ps0 = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=0)\n",
    "task_ps1 = tf.train.Server(cluster_spec, job_name=\"ps\", task_index=1)\n",
    "task_worker0 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=0)\n",
    "task_worker1 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=1)\n",
    "task_worker2 = tf.train.Server(cluster_spec, job_name=\"worker\", task_index=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 여러 디바이스와 서버에 연산을 할당하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "with tf.device(\"/job:ps\"):\n",
    "    a = tf.Variable(1.0, name=\"a\")\n",
    "\n",
    "with tf.device(\"/job:worker\"):\n",
    "    b = a + 2\n",
    "\n",
    "with tf.device(\"/job:worker/task:1\"):\n",
    "    c = a + b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0\n"
     ]
    }
   ],
   "source": [
    "with tf.Session(\"grpc://127.0.0.1:2221\") as sess:\n",
    "    sess.run(a.initializer)\n",
    "    print(c.eval())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "reset_graph()\n",
    "\n",
    "with tf.device(tf.train.replica_device_setter(\n",
    "        ps_tasks=2,\n",
    "        ps_device=\"/job:ps\",\n",
    "        worker_device=\"/job:worker\")):\n",
    "    v1 = tf.Variable(1.0, name=\"v1\")  # /job:ps/task:0 (defaults to /cpu:0) 에 할당\n",
    "    v2 = tf.Variable(2.0, name=\"v2\")  # /job:ps/task:1 (defaults to /cpu:0) 에 할당\n",
    "    v3 = tf.Variable(3.0, name=\"v3\")  # /job:ps/task:0 (defaults to /cpu:0) 에 할당\n",
    "    s = v1 + v2            # /job:worker (defaults to task:0/cpu:0) 에 할당\n",
    "    with tf.device(\"/task:1\"):\n",
    "        p1 = 2 * s         # /job:worker/task:1 (defaults to /cpu:0) 에 할당\n",
    "        with tf.device(\"/cpu:0\"):\n",
    "            p2 = 3 * s     # /job:worker/task:1/cpu:0 에 할당\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.log_device_placement = True\n",
    "\n",
    "with tf.Session(\"grpc://127.0.0.1:2221\", config=config) as sess:\n",
    "    v1.initializer.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 리더"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No more files to read\n",
      "[array([[ 4.0000000e+00,  5.0000000e+00],\n",
      "       [ 1.0000000e+00, -3.9627734e+03]], dtype=float32), array([1, 0], dtype=int32)]\n",
      "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
      "No more training instances\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "test_csv = open(\"my_test.csv\", \"w\")\n",
    "test_csv.write(\"x1, x2 , target\\n\")\n",
    "test_csv.write(\"1.,    , 0\\n\")\n",
    "test_csv.write(\"4., 5. , 1\\n\")\n",
    "test_csv.write(\"7., 8. , 0\\n\")\n",
    "test_csv.close()\n",
    "\n",
    "filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
    "filename = tf.placeholder(tf.string)\n",
    "enqueue_filename = filename_queue.enqueue([filename])\n",
    "close_filename_queue = filename_queue.close()\n",
    "\n",
    "reader = tf.TextLineReader(skip_header_lines=1)\n",
    "key, value = reader.read(filename_queue)\n",
    "\n",
    "x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
    "features = tf.stack([x1, x2])\n",
    "\n",
    "instance_queue = tf.RandomShuffleQueue(\n",
    "    capacity=10, min_after_dequeue=2,\n",
    "    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
    "    name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
    "enqueue_instance = instance_queue.enqueue([features, target])\n",
    "close_instance_queue = instance_queue.close()\n",
    "\n",
    "minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
    "    sess.run(close_filename_queue)\n",
    "    try:\n",
    "        while True:\n",
    "            sess.run(enqueue_instance)\n",
    "    except tf.errors.OutOfRangeError as ex:\n",
    "        print(\"더 이상 읽을 파일이 없습니다\")\n",
    "    sess.run(close_instance_queue)\n",
    "    try:\n",
    "        while True:\n",
    "            print(sess.run([minibatch_instances, minibatch_targets]))\n",
    "    except tf.errors.OutOfRangeError as ex:\n",
    "        print(\"더 이상 훈련 샘플이 없습니다\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#coord = tf.train.Coordinator()\n",
    "#threads = tf.train.start_queue_runners(coord=coord)\n",
    "#filename_queue = tf.train.string_input_producer([\"test.csv\"])\n",
    "#coord.request_stop()\n",
    "#coord.join(threads)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# QueueRunner와 Coordinator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 4.0000000e+00,  5.0000000e+00],\n",
      "       [ 1.0000000e+00, -3.9627734e+03]], dtype=float32), array([1, 0], dtype=int32)]\n",
      "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
      "더 이상 훈련 샘플이 없습니다\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
    "filename = tf.placeholder(tf.string)\n",
    "enqueue_filename = filename_queue.enqueue([filename])\n",
    "close_filename_queue = filename_queue.close()\n",
    "\n",
    "reader = tf.TextLineReader(skip_header_lines=1)\n",
    "key, value = reader.read(filename_queue)\n",
    "\n",
    "x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
    "features = tf.stack([x1, x2])\n",
    "\n",
    "instance_queue = tf.RandomShuffleQueue(\n",
    "    capacity=10, min_after_dequeue=2,\n",
    "    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
    "    name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
    "enqueue_instance = instance_queue.enqueue([features, target])\n",
    "close_instance_queue = instance_queue.close()\n",
    "\n",
    "minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
    "\n",
    "n_threads = 5\n",
    "queue_runner = tf.train.QueueRunner(instance_queue, [enqueue_instance] * n_threads)\n",
    "coord = tf.train.Coordinator()\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
    "    sess.run(close_filename_queue)\n",
    "    enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)\n",
    "    try:\n",
    "        while True:\n",
    "            print(sess.run([minibatch_instances, minibatch_targets]))\n",
    "    except tf.errors.OutOfRangeError as ex:\n",
    "        print(\"더 이상 훈련 샘플이 없습니다\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[ 4.0000000e+00,  5.0000000e+00],\n",
      "       [ 1.0000000e+00, -3.9627734e+03]], dtype=float32), array([1, 0], dtype=int32)]\n",
      "[array([[7., 8.]], dtype=float32), array([0], dtype=int32)]\n",
      "더 이상 훈련 샘플이 없습니다\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "def read_and_push_instance(filename_queue, instance_queue):\n",
    "    reader = tf.TextLineReader(skip_header_lines=1)\n",
    "    key, value = reader.read(filename_queue)\n",
    "    x1, x2, target = tf.decode_csv(value, record_defaults=[[-1.], [-1.], [-1]])\n",
    "    features = tf.stack([x1, x2])\n",
    "    enqueue_instance = instance_queue.enqueue([features, target])\n",
    "    return enqueue_instance\n",
    "\n",
    "filename_queue = tf.FIFOQueue(capacity=10, dtypes=[tf.string], shapes=[()])\n",
    "filename = tf.placeholder(tf.string)\n",
    "enqueue_filename = filename_queue.enqueue([filename])\n",
    "close_filename_queue = filename_queue.close()\n",
    "\n",
    "instance_queue = tf.RandomShuffleQueue(\n",
    "    capacity=10, min_after_dequeue=2,\n",
    "    dtypes=[tf.float32, tf.int32], shapes=[[2],[]],\n",
    "    name=\"instance_q\", shared_name=\"shared_instance_q\")\n",
    "\n",
    "minibatch_instances, minibatch_targets = instance_queue.dequeue_up_to(2)\n",
    "\n",
    "read_and_enqueue_ops = [read_and_push_instance(filename_queue, instance_queue) for i in range(5)]\n",
    "queue_runner = tf.train.QueueRunner(instance_queue, read_and_enqueue_ops)\n",
    "\n",
    "with tf.Session() as sess:\n",
    "    sess.run(enqueue_filename, feed_dict={filename: \"my_test.csv\"})\n",
    "    sess.run(close_filename_queue)\n",
    "    coord = tf.train.Coordinator()\n",
    "    enqueue_threads = queue_runner.create_threads(sess, coord=coord, start=True)\n",
    "    try:\n",
    "        while True:\n",
    "            print(sess.run([minibatch_instances, minibatch_targets]))\n",
    "    except tf.errors.OutOfRangeError as ex:\n",
    "        print(\"더 이상 훈련 샘플이 없습니다\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 타임아웃 지정하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0\n",
      "6.0\n",
      "3.0\n",
      "4.0\n",
      "dequeue 타임 아웃\n"
     ]
    }
   ],
   "source": [
    "reset_graph()\n",
    "\n",
    "q = tf.FIFOQueue(capacity=10, dtypes=[tf.float32], shapes=[()])\n",
    "v = tf.placeholder(tf.float32)\n",
    "enqueue = q.enqueue([v])\n",
    "dequeue = q.dequeue()\n",
    "output = dequeue + 1\n",
    "\n",
    "config = tf.ConfigProto()\n",
    "config.operation_timeout_in_ms = 1000\n",
    "\n",
    "with tf.Session(config=config) as sess:\n",
    "    sess.run(enqueue, feed_dict={v: 1.0})\n",
    "    sess.run(enqueue, feed_dict={v: 2.0})\n",
    "    sess.run(enqueue, feed_dict={v: 3.0})\n",
    "    print(sess.run(output))\n",
    "    print(sess.run(output, feed_dict={dequeue: 5}))\n",
    "    print(sess.run(output))\n",
    "    print(sess.run(output))\n",
    "    try:\n",
    "        print(sess.run(output))\n",
    "    except tf.errors.DeadlineExceededError as ex:\n",
    "        print(\"dequeue 타임 아웃\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 연습문제 해답"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Coming soon**"
   ]
  }
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