Docker packaging for Python
When you’ve used Docker before, you learned just enough command-line basics to get your work done.
But now you need to start packaging your application, which means doing everything from from writing
Dockerfiles to debugging broken builds—and your basic knowledge isn’t enough.
Quickly learn the mental models and debugging techniques you need to package your Python application in Docker, by reading Just Enough Docker Packaging.
Packaging your Python application for production with Docker requires best practices, so you don’t risk security breaches or waste expensive developer time on slow builds. But doing the research will also use up days of developer time.
Save time and money, by quickly learning the best practices you need—including security, fast builds, small images, reproducability, and much more—from the Python on Docker Production Checklist.
Instead of wasting days of expensive developer time implementing and testing your own Docker packaging infrastructure, you can ship your Docker images with confidence—in just hours!—by using the Production-Ready Python Containers template.
Performance and optimization
Paying too much for you compute resources because your Python batch process uses too much memory? The Fil memory profiler will tell you exactly what you need to know: where your peak memory usage is coming from, so you can optimize your code and lower you costs.