#49: Your technical skills are obsolete: now what?

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Brian #1: Conference videos for DjangoCon 2017 and PyGotham 2017

• PyGotham 2017 videos on pyvideo
• DjangoCon 2017 on YouTube
• One I’ve watched so far:
  • DjangoCon US 2017 - Django vs Flask by David "DB" Baumgold
  • slides
  • Very good introduction to Flask while comparing some of the features of Django to Flask and what the current frequent practices are for doing things in Flask like:
    • Data modeling with SQLAlchemy, MongoEngine, or Peewee
    • User admin with Flask-Security, which wraps Flask-Login, Flask-Permissions, and other commonly used together packages.
    • Blueprints in Flask solve a similar problem as apps in Django.
    • Flask-Marshmallow for APIs

Michael #2: Python 3.6.3 released on Tue. All machines at FB are already running it (3 days)

• Tweet: Did you hear that 3.6.3 was released on Tue? How about that all machines at FB are already running it? Over 36.3% of our Python apps are 3.6 via @llanga
• See Jason Fried’s presentation on culture: Rules for Radicals: Changing the Culture of Python at Facebook
• More Python 3 news
  • Ubuntu 17.10: “Python 2 is no longer installed by default. Python 3 has been updated to 3.6.”
  • PSA: Python 3.3 is end-of-life in 2 days. Are you prepared?

Brian #3: Your technical skills are obsolete: now what?

• by Itamar Turner-Trauring
• We’re big proponents of keeping your skills current, of learning new techniques and technologies. But how does that fit in with life and work.
• This article is an opinion of how to work on new skills while at work, do it quickly, and look good to your manager.
• It starts with a good discussion of real business reasons why some projects use older technology. Basically, cost vs benefit of change.
• Steps to be part of the solution:
  • Identify obsolete and problematic technologies.
  • Identify potential replacements.
  • Get management buy in to get resources (you) to do a pilot project exploring new technology.
• This process will help you be better at identifying problems, even if you don’t get approval to fix it.
• He ends with a comment that if you don’t get approval, all is not lost, you have skills to apply to a new job.
• I’d like to make sue you do a few more steps before giving up and looking for a new job. Before you consider a move to a new team or company, I think…
  • You should give your manager the benefit of the doubt and use this to start a conversation. Make sure you understand their reasons for saying no.
  • Make sure you are not proposing too much time taken away from your primary role in the company.
  • State that you want to improve your skills by providing value for the team and the company.
  • Is the “no” due to just bad timing? Is there a higher priority problem to work on?
  • You’ve just shown that you are someone interested in keeping your skills sharp and helping the company by expanding your role. If you’re still stuck at this point, then consider a move but also, …
• Read this:
  • Team Geek: A Software Developer's Guide to Working Well with Others - Brian Fitzpatrick
  • Especially:
    • pg 117 : “Offensive vs Defensive work”. 50-70% of your time at work needs to be focused on creating new value for your company or your customers. No more than 30-50% on repaying technical debt. (Okken: Limit your process improvement / new technology exploration to no more than 10-20%, but try to never drop it below 5% of your time)
    • pg 113 : “It’s easier to ask for forgiveness than permission.” This is a fine line between doing the right thing and doing something you can get reprimanded for. Use good judgement.
    • Forgotten page number: A big part of your job is making your boss’s job easier and making your boss and your team look good.

Michael #4: Visualizing Garbage Collection Algorithms

• By Ken Fox
• Follow up from the excellent deep dive article in GC from Brian
• Most developers take automatic garbage collection for granted.
• It’s very difficult to see how GCs actually work.
• GCs visualized (click on each image):
  • Cleanup At The End: aka No GC (e.g. Apache web server creates a small pool of memory per request and throws the entire pool away when the request completes)
  • Reference Counting Collector (e.g. Python’s first pass GC, Microsoft COM, C++ Smart Pointers. Memory fragmentation is interesting)
    • The red flashes indicate reference counting activity. A very useful property of reference counting is that garbage is detected as soon as possible — you can sometimes see a flash of red immediately followed by the area turning black.
  • Mark-Sweep Collector (e.g. is this Python’s secondary collector? Probably is my guess)
    • Mark-sweep eliminates some of the problems of reference count. It can easily handle cyclic structures and it has lower overhead since it doesn’t need to maintain counts.
  • Mark-Compact Collector (Oracle’s Hotspot JVM’s tenured object space uses mark-compact)
    • Mark-compact disposes of memory, not by just marking it free, but by moving objects down into the free space
    • The crazy idea of moving objects means that new objects can always just be created at the end of used memory. This is called a “bump” allocator and is as cheap as stack allocation, but without the limitations of stack size.
  • Copying Collector, aka Generational GC
    • The foundation of most high-performance garbage collection systems

Brian #5: pathlib — Filesystem Paths as Objects

• from Doug Hellman’s PyMOTW-3
• pathlib was introduced with Python 3.4
• If you need to work with the file system in Python, you should be using pathlib.
• Doug’s article is a really good overview.
• Features
  • Building paths with overloaded / operator
  • Parsing paths with .parts, .parents, .suffix, .stem
  • Concrete paths such as Path.home(), Path.cwd()
  • Getting directory contents with .iterdir()
  • Pattern matching with .glob() and .rglob()
  • Reading and writing files with path objects.
  • Working with directories and symbolic links
  • File properties, permissions
  • Deleting files and directories
• see also
  • https://docs.python.org/3/library/pathlib.html
  • The official docs are pretty good too

Michael #6: LUMINOTH: Open source Computer Vision toolkit

• Deep Learning toolkit for Computer Vision
• Supports object detection and image classification, but are aiming for much more.
• It is built in Python, using TensorFlow and Sonnet (Google’s Deep Learning framework and DeepMind’s graph library)
• Easily train neural networks to detect and classify objects with custom data.
• Use state of the art models such as Faster R-CNN (Region-based Convolutional Neural Networks)
• Comes with GPGPU support
• Simple training
  • Train your model by just typing lumi train. Do it locally or using Luminoth's built-in Google Cloud Platform support to train in the cloud.
  • Once training is done, you can use our Tensorboard integration to visualize progress and intermediate results.
• Are also working on providing pre-trained checkpoints on popular datasets such as Pascal VOC2012

Bonus article:

The Cleaning Hand of Pytest - My experiences with different approaches to testing

• by Wiktor Żurawik
• Two case studies of having to use unittest after using pytest
• Be sure to check out the “useful links” at the end of the article.

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