slaysd

#!/bin/bash

CLUSTER_NAME={{TYPE YOUR CLUSTER NAME}}
REGION={{ TYPE YOUR AWS EKS REGION NAME }}

echo "AWS CLI 설치"
pip install awscli --upgrade --user
export PATH=$HOME/.local/bin:$PATH
echo 'export PATH=$HOME/.local/bin:$PATH' >> $HOME/.zshrc

nix010

from bs4 import BeautifulSoup as BS
import requests

class BaseCrawler(object):

    api_url         = None

    default_headers = {
        'Accept-Language'   :'en-US,en,q=0.9,vi;q=0.8',

mjdietzx

#
# written for Amazon Linux AMI
# creates an AWS Lambda deployment package for pytorch deep learning models (Python 3.6.1)
# assumes lambda function defined in ~/main.py
# deployment package created at ~/waya-ai-lambda.zip
#

#
# install python 3.6.1
#

zhanwenchen

Updated 4/11/2018

Here's my experience of installing the NVIDIA CUDA kit 9.0 on a fresh install of Ubuntu Desktop 16.04.4 LTS.

Table of Contents

• Install NVIDIA Graphics Driver via apt-get
• Install CUDA
• Install cuDNN

Table of contents generated with markdown-toc

Integralist

import socket

hostname, sld, tld, port = 'www', 'integralist', 'co.uk', 80
target = '{}.{}.{}'.format(hostname, sld, tld)

# create an ipv4 (AF_INET) socket object using the tcp protocol (SOCK_STREAM)
client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)

# connect the client
# client.connect((target, port))

xnuk

sq2 :: Integer -> Integer -> [Integer]
sq2 left divider = let a = takeWhile (<= left) [(divider+i)*i | i<-[1..9]]
                   in if null a
                        then 0:sq2 (left*100) (divider*10)
                        else fromIntegral (length a):sq2 ((left - last a)*100) ((divider + fromIntegral (length a) * 2)*10)

main :: IO ()
main = print (sqrt (2 :: Double)) >> mapM_ (putStr . show) (sq2 2 0)

summerlight

import z3

z3.init("some_path")

def flatten(v, i, j):
    formula = 0
    for k in range(0, j - i):
        formula += pow(10, k) * v[j - k - 1]
    return formula

joseraya

var Browser = require('zombie'),
    url     = require('url'),
    fs      = require('fs'),
    $q 		= require('Q'),
    saveDir = __dirname + '/_snapshots';


var scriptTagRegex = /<script\b[^<]*(?:(?!<\/script>)<[^<]*)*<\/script>/gi;

var stripScriptTags = function(html) {