So I see that profile-guided optimization are being used.<p>Profile-guided optimization, in layman terms, means that you run you code under a profiler for a while, see what parts (branches, functions, data structures etc) are being used the most and use this information to make a build of your code that considers the profiler's findings when doing optimization.<p>So what does it mean? It means, basically, that revsys is publishing a python build that is optimized for <i>their</i> use-case. Which may or may not be your use-case. This is not good nor bad.<p>Still, the claim "up to 19% faster" is false in general (but true in a particular case -- their use case).<p>Just keep this in mind, because this python build might perform worse than a regular python build.
I've been using --enable-optimizations for a while, and built my own 3.6 images a couple of weeks back.<p>Here's the Dockerfiles and readme:<p><a href="https://github.com/rcarmo/ubuntu-python" rel="nofollow">https://github.com/rcarmo/ubuntu-python</a>
Am I totally misunderstanding something or there is no Dockerfile referenced? If so, is this just a binary blob I pull from a public registry?<p>I have some trust concerns about basing my infrastructure on something opaque.
This was just mentioned today at north bay python.<p>Another alternative for a fast speed up by changing your base image is trying out the pypy images.