About This Talk
You know the basics of packaging your Python application for Docker, but do you know enough to run that image in production? Bad packaging can result in security and production problems, not to mention wasted time try to debug unreproducible errors.
And even if you figure out the best practices, there’s still a huge number of details to get right, many of which interact with each other in unexpected ways. My personal list includes over 70 Docker packaging best practices, and it keeps growing. So where do you start? What should you do first?
To help you quickly package your application in a production-ready way, this talk will give you a process to help you prioritize and iteratively implement these best practices, by starting with the highest priority best practices (security, automation), moving on the correctness and reproducibility, and finally focusing on optimizing build time and image size.
Itamar helps teams using Python ship faster code. He has been using Python since 1999, and worked on web applications, scientific computing, distributed systems, and more. His open source work includes Fil, a memory profiler data intensive processing, and Eliot, the causal logging library.
You can learn more about Python Docker packaging, performance, and optimizing memory usage at https://pythonspeed.com.