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