Docker and Python

Tania Allard

July 23, 2020

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1. TANIA ALLARD, PHD Making them play nicely and securely for

   Data Science and Machine Learning DOCKER AND PYTHON Sr. Developer Advocate @Microsoft. ixek | https://bit.ly/europython-ml-docker

2. @ixek @trallard trallard.dev

3. https://bit.ly/europython-ml- docker THESE SLIDES

4. WHAT YOU’LL LEARN TODAY -Why using Docker? -Docker for Data

   Science and Machine Learning -Security and performance -Do not reinvent the wheel, automate -Tips and trick to use Docker ixek | https://bit.ly/europython-ml-docker

5. WHY DOCKER?

6. DEV LIFE WITHOUT DOCKER OR CONTAINERS Your application How are

   your users or colleagues meant to know what dependencies they need? Import Error: no module name x, y, x ixek | https://bit.ly/europython-ml-docker

7. WHAT IS DOCKER? A tool that helps you to create,

   deploy and run your applications or projects by using containers. This is a container ixek | https://bit.ly/europython-ml-docker

8. HOW DO CONTAINERS HELP ME? They provide a solution to

   the problem of how to get software to run reliably when moved from one computing environment to another Your laptop Test environment Staging environment Production environment ixek | https://bit.ly/europython-ml-docker

9. DEV LIFE WITH CONTAINERS Your application Libraries, dependencies, runtime environment,

   configuration files ixek | https://bit.ly/europython-ml-docker

10. THAT SOUNDS A LOT LIKE A VIRTUAL MACHINE Each app

    is containerised INFRASTRUCTURE HOST OPERATING SYSTEM DOCKER APP APP APP APP APP At the app level: Each runs as an isolated process ixek | https://bit.ly/europython-ml-docker

11. THAT SOUNDS A LOT LIKE A VIRTUAL MACHINE CONTAINERS INFRASTRUCTURE

    HOST OPERATING SYSTEM DOCKER APP APP APP APP APP INFRASTRUCTURE HYPERVISOR APP GUEST OS VIRTUAL MACHINE VIRTUAL MACHINE At the hardware level Full OS + app + binaries + libraries APP GUEST OS VIRTUAL MACHINE ixek | https://bit.ly/europython-ml-docker

12. -Image: archive with all the data needed to run the

    app -When you run an image it creates a container IMAGE VS CONTAINER Docker image $ docker run Latest 1.0.2 ixek | https://bit.ly/europython-ml-docker

13. -Complex setups / dependencies -Reliance on data / databases -Fast

    evolving projects (iterative R&D process) -Docker is complex and can take a lot of time to upskill -Are containers secure enough for my data / model /algorithm? COMMON PAIN POINTS IN DS AND ML

14. DOCKER FOR DATA SCIENCE AND MACHINE LEARNING

15. HOW IS IT DIFFERENT FROM WEB APPS FOR EXAMPLE? https://twitter.com/dstufft/status/1095164069802397696

    ixek | https://bit.ly/europython-ml-docker

16. -Not every deliverable is an app -Not every deliverable is

    a model either -Heavily relies on data -Mixture of wheels and compiled packages -Security access levels - for data and software -Mixture of stakeholders: data scientists, software engineers, ML engineers HOW IS IT DIFFERENT FROM WEB APPS FOR EXAMPLE? ixek | https://bit.ly/europython-ml-docker

17. Dockerfiles are used to create Docker images by providing a

    set of instructions to install software, configure your image or copy files BUILDING DOCKER IMAGES ixek | https://bit.ly/europython-ml-docker

18. Base image Main instructions Entry command DISSECTING DOCKER IMAGES ixek

    | https://bit.ly/europython-ml-docker

19. INSTALL PANDAS INSTALL REQUESTS DISSECTING DOCKER IMAGES INSTALL FLASK BASE

    IMAGE Each instruction creates A layer (like an onion) ixek | https://bit.ly/europython-ml-docker

20. CHOOSING THE BEST BASE IMAGE https://github.com/docker-library/docs/tree/master/python If building from scratch

    use the official Python images https://hub.docker.com/_/python ixek | https://bit.ly/europython-ml-docker

21. THE JUPYTER DOCKER STACK Need Conda, notebooks and scientific Python

    ecosystem? Try Jupyter Docker stacks https://jupyter-docker-stacks.readthedocs.io/ ubuntu@SHA base-notebook minimal-notebook scipy-notebook r-notebook tensorflow-notebook datascience-notebook pyspark-notebook all-spark-notebook ixek | https://bit.ly/europython-ml-docker

22. - Always know what you are expecting -Provide context with

    LABELS -Split complex RUN statements and sort them -Prefer COPY to add files BEST PRACTICES https://docs.docker.com/develop/develop-images/dockerfile_best-practices/ ixek | https://bit.ly/europython-ml-docker

23. - Leverage build cache -Install only necessary packages SPEED UP

    YOUR BUILD https://docs.docker.com/develop/develop-images/dockerfile_best-practices/ ixek | https://bit.ly/europython-ml-docker

24. - Leverage build cache -Install only necessary packages -Explicitly ignore

    files https://docs.docker.com/develop/develop-images/dockerfile_best-practices/ SPEED UP YOUR BUILD AND PROOF ixek | https://bit.ly/europython-ml-docker

25. -You can use bind mounts to directories (unless you are

    using a database) -Avoid issues by creating a non-root user https://docs.docker.com/develop/develop-images/dockerfile_best-practices/ MOUNT VOLUMES TO ACCESS DATA ixek | https://bit.ly/europython-ml-docker

26. SECURITY AND PERFORMANCE

27. Lock down your container: - Run as non-root user (Docker

    runs as root by default) - Minimise capabilities MINIMISE PRIVILEGE - FAVOUR LESS PRIVILEGED USER ixek | https://bit.ly/europython-ml-docker

28. Remember Docker images are like onions. If you copy keys

    in an intermediate layer they are cached. Keep them out of your Dockerfile. DON’T LEAK SENSITIVE INFORMATION ixek | https://bit.ly/europython-ml-docker

29. -Fetch and manage secrets in an intermediate layer -Not all

    your dependencies will have been packed as wheels so you might need a compiler - build a compile and a runtime image -Smaller images overall USE MULTI STAGE BUILDS

30. USE MULTI STAGE BUILDS Compile-image Docker image Runtime-image Copy virtual

    Environment $ docker build --pull --rm -f “Dockerfile"\ -t trallard:data-scratch-1.0 "." Docker image

31. USE MULTI STAGE BUILDS Docker image Runtime-image FINAL IMAGE trallard:data-scratch-1.0

32. AUTOMATE

33. PROJECT TEMPLATES Need a standard project template? Use cookie cutter

    data science Or cookie cutter docker science https://github.com/docker-science/cookiecutter-docker-science https://drivendata.github.io/cookiecutter-data-science/

34. DO NOT REINVENT THE WHEEL Leverage the existence and usage

    of tools like repo2docker. Already configured and optimised for Data Science / Scientific computing. https://repo2docker.readthedocs.io/en/latest $ conda install jupyter repo2docker $ jupyter-repo2docker “.” ixek | https://bit.ly/europython-ml-docker

35. DO NOT REINVENT THE WHEEL Leverage the existence and usage

    of tools like repo2docker. Already configured and optimised for Data Science / Scientific computing. https://repo2docker.readthedocs.io/en/latest ixek | https://bit.ly/europython-ml-docker

36. DELEGATE TO YOUR CONTINUOUS INTEGRATION TOOL Set Continuous integration (Travis,

    GitHub Actions, whatever you prefer). And delegate your build - also build often. https://repo2docker.readthedocs.io/en/latest ixek | https://bit.ly/europython-ml-docker

37. THIS WORKFLOW Docker image Docker image -Code in version control

    -Trigger on tag / Also scheduled trigger -Build image -Push image ixek | https://bit.ly/europython-ml-docker

38. TOP TIPS

39. 1. Rebuild your images frequently - get security updates for

    system packages 2. Never work as root / minimise the privileges 3. You do not want to use Alpine Linux (go for buster, stretch or the Jupyter stack) 4. Always know what you are expecting: pin / version EVERYTHING (use pip- tools, conda, poetry or pipenv) 5. Leverage build cache TOP TIPS

40. 6. Use one Dockerfile per project 7. Use multi-stage builds

    - need to compile code? Need to reduce your image size? 8. Make your images identifiable (test, production, R&D) - also be careful when accessing databases and using ENV variables / build variables 9. Do not reinvent the wheel! Use repo2docker 10.Automate - no need to build and push manually 11.Use a linter TOP TIPS

41. THANK YOU @ixek @trallard trallard.dev