I was recently working on a side project that involves communication between binaries written in Rust and web interfaces written in Python. Moving a part of my project onto a language like Rust is under several considerations: 1) the logic is all about manipulating byte arrays, where Python has deficit and system language like Rust is superior; 2) the logic happens to be complicated, I need a static type system to ensure the correctness, and also the match expression of Rust is found helpful in getting things concise; 3) I was planning to develop CLI tools with Rust, which calls this fraction of functionality, and I don’t want to rewrite the stuff in the future.

I am a heavy user of Mathjax. Mathjax is a library that renders Tex-compatible syntax into pretty equations in web scenarios. Hence I am always mixing up Markdown and Tex snippets in my writing. The annoying part is Tex snippets have low priority in my Markdown renderer, and are sometimes incorrectly rendered into Markdown elements. For instance, $a_1, a_2$ becomes $a<em>1, a</em>2$, where underscores within $...$ are mistakenly recognized as an emphasis element. A bunch of escaping is required to avoid the situation, which drives me mad. So I got to seek a permanant solution.

I’ve been struggling to choose a handy tool for blogging about seven or eight years ago. Before the day I’ve tried building my own blog system using Django. It was great proudness and excitement to see the first “Hello World” post appeared in my browser, but soon I realized that was far from a ready-to-use product. The editor on the admin site was less functional than Sublime Text or VSCode, and sometimes buggy. The rendered content would mess up and out of my control from time to time. And most importantly, I had to pay for a VPS (or PaaS, still costly) to run the site. I was in high school at the time, and no much income for the bills. Too much trivia to care about just for a perfect writing experience. So I gave up.

It was then I read about the concept of static site generators. I love the idea that separates writing from post rendering and publishing. One will have enough freedom to pick the most suitable tool in either stage. No more need to endure the shitty web editors and I can embrace my favourite local ones. Also the renderer is highly customizable, plus lots of fabulous themes to choose from.

At this era you may recommend Hugo, but I chose Hexo then, partly because of the Node.js booming at that time. It was hardly said to be perfect initially, but as years passed I’ve made it much more handy, by developing plugins to meet my own requirements. I have bundled them in this repository hexo-enhanced. Some of them are short in source code, but greatly improve my experience during writing. I am going to open up a new series to share the story behind the plugin.

Table of Contents

• Part 1 - Better Mathjax Rendering

Today a user of our GPU cluster ran into a problem where executing python -c 'import torch; torch.tensor(1).cuda() would hang forever and could not be killed. The problem occured on a rather old Docker image (with torch == 0.4.0), and would disappear if newer images were used. It was caused by some far less known coincidents, which surprised me and I want to share in this post.

The Problem

The hanging program is spawned by following command:

/usr/bin/docker run --rm -u 1457:1457 \
    --gpus '"device='0,1,2,3'"' \
    -v /ghome/username:/ghome/username -v /gdata/username:/gdata/username \
    -it --ipc=host --shm-size 64G \
    -v /gdata1/username:/gdata1/username -v /gdata2/username:/gdata2/username \
    -e HOME=/ghome/username \
    -m 48G --memory-swap 48G --cpus 5 \
    --name username2 \
    bit:5000/deepo_9 \
    python3 -c 'import torch; torch.tensor(1).cuda()'

the Docker image bit:5000/deepo_9 he used was built with CUDA-9, while the host has multiple 1080Ti GPU cards and CUDA upgraded to 11.4. Looks like there’s some binary incompatibility, considering the fact that the problem would gone with newer images.

I came across a piece of interesting vulnerable script from a post on V2EX 收到条阿里云的告警，看不懂是做什么用的，请教一下 - V2EX on V2EX. A bash function named __curl inside the file retrieves contents over HTTP as a simple alternative for command curl or wget, in scenarios where no such utilities available.

#!/bin/bash
function __curl() {
  read proto server path <<<$(echo ${1//// })
  DOC=/${path// //}
  HOST=${server//:*}
  PORT=${server//*:}
  [[ x"${HOST}" == x"${PORT}" ]] && PORT=80

  exec 3<>/dev/tcp/${HOST}/$PORT
  echo -en "GET ${DOC} HTTP/1.0\r\nHost: ${HOST}\r\n\r\n" >&3
  (while read line; do
   [[ "$line" == $'\r' ]] && break
  done && cat) <&3
  exec 3>&-
}

The function makes use of certain less known features of Linux and the Bash language.

The first one is communicating over TCP through files. Linux employs a design philosophy of “everything are files”. One could find some special device files in directory /dev, through which we can manipulate the underlying devices. Specifically, manipulating a TCP socket connecting ${HOST}:${PORT} could be achieved by accessing device file /dev/tcp/${HOST}/${PORT}. Since HTTP is a text-based protocol over TCP, working with it is no more difficult than reading / writing a text file. Line exec 3<>$FILENAME opens file $FILENAME under read-write mode and binds it to descriptor 3. The next line then manually composes an HTTP payload and writes out to &3, which is in fact requesting the URL http ://${HOST}:${PORT}. By reading the same file descriptor, we retrieve the response content from the service. The trick serves as a primitive workaround for retrieving contents from web.

Another one is parameter substitution in Bash. The expression ${var//PATTERN/REPL} substitutes all occurrences of PATTERN in var into REPL. If REPL omitted, the matched substrings will be deleted. For example, in this script, ${1//// } would replace all slashes / into white spaces in variable $1.

References

1. Parameter Substitution

I was once working as an intern at MSRA around two years ago, at which I joined a research project and started developing upon a large codebase. It’s a practice in ML research fields to adopt an existing code repository as codebase, instead of crafting everything from scratch. Such codebases usually come with convenient “infrastructures” Loaders and preprocessors for various datasets, training loop, and evaluation metrics, etc., so researchers would not have to implement them once again, which could be time-wasting and error-prone. All we need is to write our models and losses, and put them into experiments.

The flow works just fine if you are proposing minor improvement on algorithms. The codebase provides an easy approach to prove and iterate your idea. But things would get worse if your work goes beyond it, especially touching the encapsulated infrastructures. Those convenient parts would constraint you and enforce your code into spaghetti.

The early idea of hsfzxjy/mocona was come up with in late April. It was not until July that I figure out a reasonable design for the project. I finished most of my idea and released the first version approaching August. Nevertheless, there’s no chance for me to share the story behind the library. Now another month gone, it’s time to do some writing.

The series, as I planning, would cover the motivation of creating mocona, some technical details and usage, along with some critical thinking on the creative process. For whom interested in CPython internals or would like to extend the language, it is worth to read through.

Table of Contents

• Part 1 - Verbosity or Anti-Pattern

The module pickle shipped in Python could be used for generic-purpose object serialization and de-serialization. It’s been widely adopted or recommended as backend in scenarios like persisting states or IPC.

Employed by many famous frameworks, though, the magic behind it still seems to be vague for daily users, especially guys fresh to the language. People come across “unpicklable” errors from time to time, but don’t know the reason; or re-invent state persistence by themselves, even if pickle could be competent. People sometimes write error-prone codes, merely because they are afraid of or unaware of pickle.

This post thus attempts to clarify the usage of pickle module in an easy understanding way, by answering three questions.

I’ve been struggling for years on two things: synchronize passwords and blog posts I have read across devices. The problem kills me so much since my devices, an Android mobile, an Ubuntu laptop and an iPad, are less supported by big App companies. Aside, I want to gain control for all my data, so there should better exist a self-hosted solution. The problem are partially solved recently by deploying Vaultwarden and NextCloud on VPS. This blog post dictates the setup process and problems I met, in case anyone searching for this topic.

Install Vaultwarden and NextCloud on VPS

The two services are both luckily dockerized. To install there’s nothing more complicated than a command: