takuya-andou

apiVersion: batch/v1
kind: Job
metadata:
  name: job-wq
spec:
  # 並列実行数
  parallelism: 3650
  template:
    metadata:
      name: job-wq

takuya-andou

# 参考：https://tkzo.jp/blog/use-persistent-volumes-in-gke/
# volume.yaml
apiVersion: v1 
kind: PersistentVolume 
metadata: 
  # 任意のPV名を設定します
  name: persistvol
spec: 
  capacity: 
    # 作成したボリュームの容量と合わせます

takuya-andou

# キューを扱うNodeのためのNodePool
gcloud container clusters create autoscaled-cluster \
    --num-nodes 2 \
    --machine-type=n1-standard-1 \
    --zone us-west1-a --disk-size=10\

# キューを処理するためのNodePool。preemptibleにしている。
gcloud container node-pools create preemptible-pool96 \
    --cluster autoscaled-cluster --preemptible --num-nodes 0 \
    --enable-autoscaling --min-nodes 0 --max-nodes 960 \

takuya-andou

#!/bin/sh

#
# YYYY-MM-DD
#

STARTDATE=20100101
ENDDATE=20191005

CURRENTDATE=$STARTDATE

takuya-andou

# -*- coding: utf-8 -*-
import sys
import qrcode

# QRコードを作る場合
URLarray = ['http://google.com','http://yahoo.co.jp']
for i, URL in enumerate(URLarray):
    img = qrcode.make(URL)  # ''内の文字をQRコードに変換
    img.save('qr_img' + str(i) + '.png')  # QRコードに名前をつけて保存

takuya-andou

# coding:utf-8
import sys
import re
from urllib2 import urlopen as U, Request as R
from json import loads as J

API_KEY = "ここはGoogleのAPIを取得して使う"

API = "https://www.googleapis.com/urlshortener/v1/url?key=" + API_KEY

takuya-andou

const DashButton = require('dash-button');
const {exec} = require('child_process');

const do1 = new DashButton("xx:xx:xx:xx:xx:xx"); //じゃがりこ
const re = new DashButton("xx:xx:xx:xx:xx:xx"); //パンテーン
const mi = new DashButton("xx:xx:xx:xx:xx:xx"); //ヘルシア
const fa = new DashButton("xx:xx:xx:xx:xx:xx"); //カロリーメイト
const so = new DashButton("xx:xx:xx:xx:xx:xx"); //エヴィアン
const ra = new DashButton("xx:xx:xx:xx:xx:xx"); //アマゾンベーシック
const si = new DashButton("xx:xx:xx:xx:xx:xx"); //シーブリーズ

takuya-andou

# coding: utf-8
from scapy.all import *
from picraft import World, Vector, Block
world = World("MineCraftServer IP")

def arp_display(pkt):
  if pkt[ARP].op == 1:
    if pkt[ARP].hwsrc == 'Your AmazonDashButton's Mac Address':
            world.player.pos = Vector(0, 300, 0)

takuya-andou

<?php

$json_string = file_get_contents('php://input');
$json_object = json_decode($json_string);
$AndoUserID = '<<LineUserID>>';
error_log("callback start.");
foreach ($json_object->events as $event) {
    if('message' == $event->type){
        if ($event->source->userId == $AndoUserID) {
            if ($event->message->text =='おは'){

takuya-andou

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.

It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data

In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/