CPython internals: A ten-hour codewalk through the Python interpreter source code

“Here are nine lectures walking through the internals of CPython, the canonical Python interpreter implemented in C. They were from a dynamic programming languages course that I taught in Fall 2014 at the University of Rochester. The format isn’t ideal, but I haven’t seen this level of detail about CPython presented online, so I wanted to share these videos…”

http://pgbovine.net/cpython-internals.htm
http://talkpython.fm/episodes/show/22/cpython-internals-and-learning-python-with-pythontutor.com

Python, Machine Learning, and Language Wars. A Highly Subjective Point of View

“In the following paragraphs, I really don’t mean to tell you why you or anyone else should use Python. To be honest, I really hate those types of questions: “Which * is the best?” (* insert “programming language, text editor, IDE, operating system, computer manufacturer” here). This is really a nonsense question and discussion. Sometimes it can be fun and entertaining though, but I recommend saving this question for our occasional after-work beer or coffee with friends and colleagues…”

http://sebastianraschka.com/Articles/2015_why_python.html

How To Use Journalctl to View and Manipulate Systemd Logs

“Some of the most compelling advantages of systemd are those involved with process and system logging. When using other tools, logs are usually dispersed throughout the system, handled by different daemons and processes, and can be fairly difficult to interpret when they span multiple applications.Systemd attempts to address these issues by providing a centralized management solution for logging all kernel and userland processes. The system that collects and manages these logs is known as the journal.

The journal is implemented with the journald daemon, which handles all of the messages produced by the kernel, initrd, services, etc. In this guide, we will discuss how to use the journalctl utility, which can be used to access and manipulate the data held within the journal…”

https://www.digitalocean.com/community/tutorials/how-to-use-journalctl-to-view-and-manipulate-systemd-logs

System calls in the Linux kernel

“This post opens new chapter in linux-insides book and as you may understand from the title, this chapter will devoted to the System call concept in the Linux kernel. The choice of the topic for this chapter is not accidental. In the previouschapter we saw interrupts and interrupt handling. Concept of system calls is very similar to interrupts, because the most common way to implement system calls as software interrupts. We will see many different aspects that are related to the system call concept. For example, we will learn what’s happening when a system call occurs from userspace, we will see implementation of a couple system call handlers in the Linux kernel, VDSO and vsyscall concepts and many many more.

Before we will start to dive into the implementation of the system calls related stuff in the Linux kernel source code, it is good to know some theory about system calls. Let’s do it in the following paragraph…”

https://github.com/0xAX/linux-insides/blob/master/SysCall/syscall-1.md

How to create the smallest possible docker container of any image

“Once you start to do some serious work with Docker, you soon find that downloading images from the registry is a real bottleneck in starting applications. In this blog post we show you how you can reduce the size of any docker image to just a few percent of the original. So is your image too fat, try stripping your Docker image! The strip-docker-image utility demonstrated in this blog makes your containers faster and safer at the same time!…”

https://flipboard.com/topic/nginx/how-to-create-the-smallest-possible-docker-container-of-any-image/f-87c3977d9e%2Fxebia.com

Sharding Pinterest: How we scaled our MySQL fleet

“This is a technical dive into how we split our data across many MySQL servers. We finished launching this sharding approach in early 2012, and it’s still the system we use today to store our core data.

Before we discuss how to split the data, let’s be intimate with our data. Mood lighting, chocolate covered strawberries, Star Trek quotes…

Pinterest is a discovery engine for everything that interests you. From a data perspective, Pinterest is the largest human curated interest graph in the world. There are more than 50 billion Pins that have been saved by Pinners onto one billion boards. People repin and like other Pins (roughly a shallow copy), follow other Pinners, boards and interests, and view a home feed of all the Pinners, boards and interests they follow. Great! Now make it scale!

Growing pains

In 2011, we hit traction. By some estimates, we were growing faster than any other previous startup. Around September 2011, every piece of our infrastructure was over capacity. We had several NoSQL technologies, all of which eventually broke catastrophically. We also had a boatload of MySQL slaves we were using for reads, which makes lots of irritating bugs, especially with caching. We re-architected our entire data storage model. To be effective, we carefully crafted our requirements…”

https://engineering.pinterest.com/blog/sharding-pinterest-how-we-scaled-our-mysql-fleet/

Python for Exploratory Computing

“Lots of books are written on scientific computing, but very few deal with the much more commonexploratory computing (a term coined by Fernando Perez), which represents daily tasks of many scientists and engineers that try to solve problems but are not computer scientists. This set of Notebooks is written for scientists and engineers who want to use Python programming for exploratory computing, scripting, data analysis, and visualization. Python makes many of these programming tasks quick and easy and, probably most importantly, fun.

No prior knowledge of computer programming is assumed. Each Notebook covers a specific topic and includes a number of exercises. The exercises should take less than 4 hours to complete for each Notebook. Answers to the exercises are given at the end of the Notebook. There are two versions of each Notebook: one with and one without output cells. The Notebooks without the output cells are intended for people wanting to learn Python. Running the output cells is part of the learning process. For example, Notebook 1 with empty output cells looks as follows. All Notebooks and accompanying data files may be downloaded by clicking on the ‘Download .zip file’ button to the right. Notebooks with output cells cells have the addition _sol and can be viewed by clicking on the links to the Notebook Viewer below…”

http://mbakker7.github.io/exploratory_computing_with_python/

Intermediate Python

“Python is an amazing language with a strong and friendly community of programmers. However, there is a lack of documentation on what to learn after getting the basics of Python down your throat. Through this book I aim to solve this problem. I would give you bits of information about some interesting topics which you can further explore.

The topics which are discussed in this book open up your mind towards some nice corners of Python language. This book is an outcome of my desire to have something like it when I was beginning to learn Python.

If you are a beginner, intermediate or even an advanced programmer there is something for you in this book.

Please note that this book is not a tutorial and does not teach you Python. The topics are not explained in depth, instead only the minimum required information is given.

I am sure you are as excited as I am so let’s start!

Note: This book is a work in progress. If you find anything which you can further improve (I know you will find a lot of stuff) then kindly submit a pull request…”

http://book.pythontips.com/en/latest/

Machine Learning for Programmers: Leap from developer to machine learning practitioner

“The most common question I’m asked by developers on my newsletter is “how do I get started in machine learning?“. I honestly cannot remember how many times I have answered it.

In this post I lay out all of my very best thinking on this topic.

  • You will discover why the traditional approach to teaching machine learning does not work for you.
  • You will discover how to flip the entire model on its head.
  • And you will discover my simple but very effective antidote that you can use to get started.

Let’s get into it…”

http://bit.ly/1IWGMkR