Python and Docker for ML and Data Science

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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/pyconturkey-ml

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@ixek @trallard trallard.dev

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https:!//bit.ly/pyconturkey-ml THESE SLIDES

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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/pyconturkey-ml

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WHY DOCKER?

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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/pyconturkey-ml

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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/pyconturkey-ml

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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/pyconturkey-ml

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DEV LIFE WITH CONTAINERS Your application Libraries, dependencies, runtime environment, configuration files ixek |https:!//bit.ly/pyconturkey-ml

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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/pyconturkey-ml

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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/pyconturkey-ml

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-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/pyconturkey-ml

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-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? -Multiple frameworks, data standards and APIs COMMON PAIN POINTS IN DS AND ML ixek |https:!//bit.ly/pyconturkey-ml

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DOCKER FOR DATA SCIENCE AND MACHINE LEARNING

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-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- ixek |https:!//bit.ly/pyconturkey-ml

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Base image Main instructions Entry command DISSECTING DOCKER IMAGES ixek |https:!//bit.ly/pyconturkey-ml

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INSTALL PANDAS INSTALL REQUESTS DISSECTING DOCKER IMAGES INSTALL FLASK BASE IMAGE Each instruction creates A layer (like an onion) ixek |https:!//bit.ly/pyconturkey-ml

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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/pyconturkey-ml

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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/pyconturkey-ml

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- 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/pyconturkey-ml

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- 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/pyconturkey-ml

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- 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/pyconturkey-ml

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-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/pyconturkey-ml

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SECURITY AND PERFORMANCE

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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/pyconturkey-ml

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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/pyconturkey-ml

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-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

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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

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

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AUTOMATE

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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/

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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/pyconturkey-ml

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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/pyconturkey-ml

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UPDATE OFTEN - AND DELEGATE ixek |https:!//bit.ly/pyconturkey-ml https:!//snyk.io/

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THIS WORKFLOW Docker Docker -Code in version control -Trigger on tag / Also scheduled trigger -Build image -Push image ixek |https:!//bit.ly/pyconturkey-ml

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TOP TIPS

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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

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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

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THANK YOU @ixek @trallard trallard.dev