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今日推荐开源项目：《开源评论系统 ISSO》
推荐理由：ISSO 是一个由 python 编写服务端的开源的轻量级评论系统，用来代替 Disqus. 从 git 记录来看该项目已经有近 4 年的历史，但是还在持续更新维护中。用户可以自己搭建和管理评论系统，网页评论端的样式可定制，可以和 Disqus 等一类评论系统一样轻松部署在自己的博客与网页当中，还能使用 Markdown 进行评论。
安装使用
在它的官网 https://posativ.org/isso/docs/extras/deployment/ 可以查看相关部署和使用文档，但是官网好像被墙了，不翻的话无法访问。网上有一些介绍如何部署安装的中文文档在此推荐 https://sb.sb/debian-8-ubuntu-16-04-install-isso/。安装的配置项还是比较多的，如果看了安装教程有感想的，也可以自己写一个自动安装部署脚本~，奈何小编有心但无时间啊。。。部署好服务端后，在网页端插入 js 脚本和相关 html 标签，UI 展示效果如下。纵观各类评论系统其实 UI 上长得都类似，因为就几个功能，发表评论、展示评论、回复等。
开源评论系统
随着多说的下线， Disqus 在国内无法稳定使用，很多博主寻求稳定可靠的评论系统。因此很多开源的评论系统被挖掘和提及，在知乎上也有很多的讨论 https://www.zhihu.com/question/57426274。
有 PHP 编写的 hashover, 有利用 github issue 来做评论系统的 gitment 等等。当然，您可以使用 WordPress 嘛，就不用操怎么多心了。
今日推荐英文原文：《Train Your Machine Learning Models on Google’s GPUs for Free — Forever》作者：Nick Bourdakos
推荐理由：如果你关心机器学习的话，可以试着上手这篇教程，大家都知道 Google 在机器学习领域耕耘颇深，站在巨人的肩膀上，自然能让你前行更快，而这篇文章就是带你在 Google GPU 上训练你的机器学习模型，记得，看完文章后，动手试一试哟。
Train Your Machine Learning Models on Google’s GPUs for Free — Forever
Training your model is hands down the most time consuming and expensive part of machine learning. Training your model on a GPU can give you speed gains close to 40x, taking 2 days and turning it into a few hours. However, this normally comes at a cost to your wallet.
The other day I stumbled upon a great tool called Google Colab. I would describe Colab as the google docs equivalent of Jupyter notebooks. Colab is aimed at being an education and research tool for collaborating on machine learning projects. The great part is, that it’s completely free forever.
There is no setup to use it. I didn’t even need to log in. (I was already logged into my google account)
The best part is that you get an unlimited supply of 12 hours of continuous access to a k80 GPU, which is pretty powerful stuff. (You get disconnected after 12 hours, but you can use it as many times as you want)
I want our focus to be training on a GPU and Colab specific so the notebook is extremely bare bones.
The first step is to download the notebook (or another notebook of your choice)
Then, head over to Google Colab, sign into your google account (or create one if you somehow made it this far through life without one)
Now you should be able to run your notebooks how you normally would. The only difference is the very last part at the end. If you want to download your model or any other files via the browser, you can use their python library:
from google.colab import files
files.download("PATH/TO/FILE")
Final Thoughts
This was a pretty short post, but hopefully it ends the painful days of training your models on your poor little old laptop for days at a time or dropping a ton of ? on AWS bills.
每天推荐一个 GitHub 优质开源项目和一篇精选英文科技或编程文章原文，欢迎关注开源日报。交流QQ群：202790710；电报群 https://t.me/OpeningSourceOrg
