Lloyd Zhou lloydzhou

原文：Docker

如果你在单个生产节点上用过Docker，然后发现单个节点的资源不够用，那么你会怎么做呢？我也遇到过这种情况! 关于在生产环境中使用Docker Swarm，我会为你提供一些建议，也许能够帮到你。这些都是我一年来积累的一些经验。

另外，如果你对Docker Swam不熟悉的话，可以参考我之前的博客My experience with Docker Swarm - when you may need it?（http://suo.im/1m7qW8）

1. 阅读官方文档

	// Credit Ryan Carniato https://frontendmasters.com/courses/reactivity-solidjs/

	let context = [];

	export function untrack(fn) {
	const prevContext = context;
	context = [];
	const res = fn();
	context = prevContext;
	return res;

	import regex as re
	import requests

	MAX_HEADING_LENGTH = 7
	MAX_HEADING_CONTENT_LENGTH = 200
	MAX_HEADING_UNDERLINE_LENGTH = 200
	MAX_HTML_HEADING_ATTRIBUTES_LENGTH = 100
	MAX_LIST_ITEM_LENGTH = 200
	MAX_NESTED_LIST_ITEMS = 6
	MAX_LIST_INDENT_SPACES = 7

	/*
	* This document is provided to the public domain under the
	* terms of the Creative Commons CC0 public domain license
	*/

	How to boot Arch Linux ARM in QEMU (patched for M1)

	Prerequisites:

	QEMU - patched for M1 processors - patches: https://github.com/utmapp/qemu

	<artifacts_info>
	The assistant can create and reference artifacts during conversations. Artifacts are for substantial, self-contained content that users might modify or reuse, displayed in a separate UI window for clarity.

	# Good artifacts are...
	- Substantial content (>15 lines)
	- Content that the user is likely to modify, iterate on, or take ownership of
	- Self-contained, complex content that can be understood on its own, without context from the conversation
	- Content intended for eventual use outside the conversation (e.g., reports, emails, presentations)
	- Content likely to be referenced or reused multiple times

	version: '2'
	services:
	nextchat:
	image: yidadaa/chatgpt-next-web
	ports:
	- "3000"

	nginx:
	image: nginx:alpine
	ports:

	import asyncio
	import logging
	import webbrowser

	from urllib.parse import quote

	import httpx
	from uuid import uuid4

	from connectai.lark.websocket import WS_LARK_PROXY_SERVER, WS_LARK_PROXY_PROTOCOL

	#!/usr/bin/python3
	import sys
	import asyncio
	import greenlet

	class AsyncIoGreenlet(greenlet.greenlet):
	def __init__(self, driver, fn):
	greenlet.greenlet.__init__(self, fn, driver)
	self.driver = driver

	/**
	* 1. 创建websocket连接，后端会生成对应的channel<user_id, task_id>，前端是一个固定的url
	* 2. 监听创建成功事件
	* 2.1 如果创建成功就将任务<source_url, title, author>通过创建好的websocket发送到服务端
	* 2.2 如果创建失败，直接reject
	* 3. 监听接收消息事件
	* 3.1 如果收到转换成功的url，代表后端的整个转换任务成功，将url resove出去
	* 3.2 收到其他任务进度消息，可以忽略
	*/
	export const submitTask = (

	FROM nginx:alpine AS builder

	# nginx:alpine contains NGINX_VERSION environment variable, like so:
	# ENV NGINX_VERSION 1.15.0

	# Our NCHAN version
	ENV NCHAN_VERSION 1.1.15

	# Download sources
	RUN wget "http://nginx.org/download/nginx-${NGINX_VERSION}.tar.gz" -O nginx.tar.gz && \