Chih-Yu Yeh cyyeh

開源之道

Original transcript: http://allisonrandal.com/2012/04/15/open-source-enlightenment/

這幾年來，我慢慢覺得，我們參與開源社群，就像是在一條道路上並肩而行：這不僅讓我們成為更好的程式設計者，也讓我們通過與人合作，而成為更好的人。

您可以將它想成一條修行之道，讓身而為人的我們能夠不斷成長。接下來，我想談談我對開源世界的個人觀點，希望能與您分享。

首先，人是一切開源專案的核心。程式碼是很重要，但最核心的永遠是人。

The Best of the Best Practices (BOBP) Guide for Python

A "Best of the Best Practices" (BOBP) guide to developing in Python.

In General

Values

"Build tools for others that you want to be built for you." - Kenneth Reitz
"Simplicity is alway better than functionality." - Pieter Hintjens

The introduction to Reactive Programming you've been missing

(by @andrestaltz)

This tutorial as a series of videos

If you prefer to watch video tutorials with live-coding, then check out this series I recorded with the same contents as in this article: Egghead.io - Introduction to Reactive Programming.

Transforming Code into Beautiful, Idiomatic Python

Notes from Raymond Hettinger's talk at pycon US 2013 video, slides.

The code examples and direct quotes are all from Raymond's talk. I've reproduced them here for my own edification and the hopes that others will find them as handy as I have!

Looping over a range of numbers

for i in [0, 1, 2, 3, 4, 5]:

Pod watcher

K8s has metrics server to provide cpu and memory usage of pods. Default 60 seconds, can be changed using --metric-resolution flag. Official are not recommending setting values below 15s, as this is the resolution of metrics calculated by Kubelet.(ref1, ref2)

So I create this tool to watch cpu and memory and log peak usage of pods.

How to install

// instal node 17
bash &lt;(curl -sL https://gist.githubusercontent.com/grieve54706/acd9c1a411adab38c4f61f2b1497769f/raw/d42e76f3f8a3aaadf3055094df8c7dab3c688e2f/upgrade-node.sh)

	# 30 minutes Lisp in Ruby
	# Hong Minhee <http://dahlia.kr/>
	#
	# This Lisp implementation does not provide a s-expression reader.
	# Instead, it uses Ruby syntax like following code:
	#
	# [:def, :factorial,
	# [:lambda, [:n],
	# [:if, [:"=", :n, 1],
	# 1,

	Steps 1: Just to verify that you have all requirement satisfied, needed by rapids.

	* check the gpu card (>=Pascal arch)
	!nvidia-smi

	* check cuda version installed (>=9.2)
	!nvcc -V

	*check the python and pip version (python==3.6)
	!python -V; pip -V

	BEFORE INSTALLING THE CUML, PLEASE MAKE SURE YOU HAVE FOLLOWED THE ABOVE STEPS FOR CUDF. CUDF SHOULD BE WORKING...

	Step 1: Install the cuml and its depandencies.

	!apt install libopenblas-base libomp-dev
	!pip install cuml-cuda100

	# import cuml at this point, will give libcuml.so not found error. #

	NOTE: Step2 is optional and is just for information, you can fast forward to Step3 directly to work quickely.

	#!/usr/bin/python3
	# usage: schemachecker.py urls...
	# requires: python 3, beautiful soup 4, requests library

	from bs4 import BeautifulSoup
	import requests
	import sys

	def checkUrl(url):
	r = requests.get(url, headers={'user-agent': 'Mozilla/5.0 (Windows NT 6.1; Win64; x64; rv:10.0) Gecko/20100101 Firefox/10.0'})