Leo Zhou glasslion

Docker Hub 镜像加速器

国内从 Docker Hub 拉取镜像有时会遇到困难，此时可以配置镜像加速器。

Dockerized 实践 https://github.com/y0ngb1n/dockerized

1️⃣ Docker daemon 配置代理（推荐）

alembic is great but lacks an out of the box way to set up running migrations against a specific database (e.g. development, test, production). The following adjustments to its env.py and alembic.ini allow us to target a specific database:

Example:

alembic -x db=development upgrade head

env.py:

Last update: Feb 3, 2015

Flask-SQLAlchemy has some nice built-ins (e.g. accessing query directly on classes). To continue leveraging these nicities while still inside of a Celery worker, we need to make sure we setup/teardown in a similar fashion to Flask-SQLAlchemy does on Flask.

Setup

Flask-SQLAlchemy uses create_scoped_session at startup which avoids any setup on a per-request basis.

https://github.com/mitsuhiko/flask-sqlalchemy/blob/2.0/flask_sqlalchemy/__init__.py#L668

This means Celery can piggyback off of this initialization.

	This document describes about installation and configuration of IPMI simulator.
	We need: qemu-kvm, OpenIPMI, OpenIPMI-tools
	1) Install the qemu-kvm. We need the qemu, which have the IPMI pacthes.
	Use the source https://github.com/cminyard/qemu/tree/stable-2.2-ipmi
	./configure, make and make install

	2) Download the OpenIPMI libraries, from http://sourceforge.net/projects/openipmi/
	Follow the process documented in lanserv/README.vm
	./configure --prefix=/opt/openipmi/usr --sysconfdir=/opt/openipmi/etc \
	--with-perlinstall=/opt/openipmi/usr/lib/perl \

	# ASIA
	_ = '173.194.36.%s' # del 印度新德里
	SG3 = '173.194.38.%s' # 新加坡
	TW3 = '173.194.72.%s'
	SG4 = '173.194.117.%s'
	JP2 = '173.194.120.%s'
	_ = '173.194.123.%s'
	JP1 = '173.194.126.%s'
	HK1 = '173.194.127.%s'
	TW1 = '74.125.23.%s'

	"""
	Add copy to clipboard from IPython!
	To install, just copy it to your profile/startup directory, typically:

	~/.ipython/profile_default/startup/

	Example usage:

	%clip hello world
	# will store "hello world"

	#! /usr/bin/env python

	"""{escher} -- one-file key-value storage.

	What?
	This is a toy application to manage persistent key-value string data.
	The file {escher} is both the application and its data.
	When you run any of the commands below, the file will be executed and,
	after data change, it will rewrite itself with updated data.
	You can copy the file with whatever name to create multiple datasets.

	#include <vector>
	#include <list>
	#include <map>
	#include <set>
	#include <deque>
	#include <stack>
	#include <bitset>
	#include <algorithm>
	#include <functional>
	#include <numeric>

	(function($){

	$(document).ready(function(){

	$('.section-header').each(function(){
	var $this = $(this);
	// use .add() and .nextUntil() to get both the .section-header
	// and .section-item elements into a single set for our .wrapAll() call
	$this.add($this.nextUntil('.section-header', '.section-item'))
	.wrapAll('<div class="section-container"></div>');

	'''
	Cache the generic relation field of all the objects
	in the queryset, using larger bulk queries ahead of time.

	Improved from original by Daniel Roseman:
	http://blog.roseman.org.uk/2010/02/22/django-patterns-part-4-forwards-generic-relations/
	'''


	def cache_generics(queryset):