Episode #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
- 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?
- 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.
- Building paths with overloaded / operator
- Parsing paths with
- Concrete paths such as
- Getting directory contents with
- Pattern matching with
- Reading and writing files with path objects.
- Working with directories and symbolic links
- File properties, permissions
- Deleting files and directories
- see also
- 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
- 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.