Bring Your Own Container: Using Docker Images In Production
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Condé Nast is a global leader in the media production space housing iconic brands such as The New Yorker, Wired, Vanity Fair, and Epicurious, among many others. Along with our content production, Condé Nast invests heavily in companion products to improve and enhance our audience’s experience. One such product solution is Spire, Condé Nast’s service for user segmentation and targeted advertising for over a hundred million users. While Spire started as a set of databricks notebooks, we later utilized DBFS for deploying Spire distributions in the form of Python Whls, and more recently, we have packaged the entire production environment into docker images deployed onto our Databricks clusters. In this talk, we will walk through the process of evolving our python distributions and production environment into docker images, and discuss where this has streamlined our deployment workflow, where there were growing pains, and how to deal with them.
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Bring Your Own Container: Using Docker Images In Production
- 1. Bring your own container Using Docker images in production Harin Sanghirun & Max Cantor Machine Learning Engineering at Condé Nast
- 2. Introduction
- 3. Condé Nast • Global leader in media featuring many iconic brands: • The New Yorker, WIRED, Vanity Fair, Epicurious, and many more. • In addition to content production, Condé Nast also invests in companion software products to enhance our audience experiences. • Examples: Content Recommendations, Audience Segmentation
- 4. Spire Audience Segmentation • Spire is a platform for user segmentation and targeted advertising • Analyzes over one-hundred million users • See our Spark+AI Summit 2020 talk for more background on Spire
- 5. Presentation Overview • 1000-Foot View of Spire’s Architecture • What Spire looked like before Docker • How Docker helped streamline Spire’s deployment • Containerization of Spire on Databricks • Walkthrough of Spire’s containerization strategy in production • Learning from Experience: Pros & Cons • What we’ve learned from containerizing Spire on Databricks
- 6. How Docker streamlined Spire’s deployment
- 7. The 1000-Foot View Ad Targeting Content Recommendations Conde Nast’s Platform Dataset source(s) (1st, 2nd, and 3rd party)
- 8. Components of Spire ▪ Model interface standardization ▪ Model serialization ▪ Model versioning & tracking ▪ Hyperparameter tuning • Database Management • Scheduling and Orchestration • Spire Core Library • Kalos • Utility Methods • Data Science Common Library
- 11. Recap • Pre-packaged all dependencies • Each image represents a tested combination of packages • Linked to a specific Databricks Runtime and Spire • Fewer pipelines to manage • Explicit and upfront control over dependency versions
- 12. Custom Containers on Databricks, 101
- 13. The Basics • Step 1 - Choosing a base image • Step 2 - Adding your dependency • Step 3 - Push to a Docker Registry • Step 4 - Launching a cluster
- 14. Step 1 - Choosing a Base Image See their content at: https://github.com/databricks/containers • Available Images: • Standard • Minimal • Python • R • DBFS FUSE • SSH • GPU
- 15. Step 2 - Adding Dependency and build • Standard Docker workflow: • “Do what you want” • Things to watch out for: • Make sure to match package version listed in your target Runtime Version # select base image FROM databricksruntime/standard:latest # install pip libraries RUN pip install pandas urllib3 # installing binaries RUN apt-get install git
- 16. Step 3 - Pushing to a Docker Registry ● The recommended way: ○ AWS ECR ○ Azure Container Registry ● Also support any registry with basic authentication ● What we do: ○ GitHub Container Registry + Basic Authentication
- 17. Step 4 - Launching Your Cluster
- 18. Containerization of Spire on Databricks
- 19. Containerization of Spire on Databricks: Docker Image Goal: Produce a Docker image w/ DBR + Spire • The image is built from databricks-minimal • Ubuntu 18.04 • Custom DBR 7.x functionality • Spire package and sub-package install • Dependency management
- 20. Containerization of Spire on Databricks: Hosting Goal: Host image on GitHub Packages (ghcr.io) • Manage production and development packages • Can host multiple images per package • Image Tagging: • GitHub release tag • Spire package version (version.py)
- 21. Containerization of Spire on Databricks: CI/CD Goal: Integrate images creation with CI/CD pipeline • GitHub Actions CI/CD Integration • ubuntu-latest and macOS-10.15 • Automated pytest testing on push • Automated build and deployment of Docker image on release tag
- 22. What We Learned: Pros & Cons
- 23. What We Learned: Pro 1 • Library customization • Automatic and simplified control of the Spire module • Integration of module dependencies
- 24. What We Learned: Pro 2 • Fluid integration with existing deployment pipeline • GitHub Actions CI/CD • Test database integration • Pytest integration • Multiple OS support (Ubuntu and macOS) • Release tagging • Databricks Jobs
- 25. What We Learned: Pro 3 • Ease of debugging • ssh into container via docker run -it <package> bash • pdb set trace support • Test database Docker volume for pytest integrations testing
- 26. What We Learned: Con 1 • DBR Version Incompatibility • DBR 8.x not currently supported, only DBR 6.x and 7.x • Need to create custom base image
- 27. What We Learned: Con 2 • Pip Package Management • Must match target runtime specifications
- 28. What We Learned: Con 3 • Large image size • Base images are large compared to Docker images used for deployment $ docker images REPOSITORY TAG IMAGE ID CREATED SIZE databricksruntime/standard latest fd383efa2fcc 19 months ago 1.84GB
- 29. What We Learned: Con 4 • Requires prior Docker experience • Local memory space requirement for local builds grow quickly • Need to continually prune these resources • Deployment pipeline is slower • More data is uploaded Delete all docker images docker rmi $(docker images -q) Delete all docker containers docker rm $(docker ps --filter status=exited -q) Pruning docker image prune -f docker container prune -f docker builder prune -f
- 30. ● Credentials for Basic Auth are stored of plaintext basic auth stored as plain text What We Learned: Con 5
- 31. ● Each usage requires a pull of the container ○ Image pulls grow quickly What We Learned: Con 6
- 32. Summary ● What Databricks’ Containers offer: ● Streamlining of dependency management ● Streamlining of CI/CD ● More predictable runtime behavior ● Things to consider: ● Need careful synchronization of dependencies ○ Between your image and your target runtime ● Security concern for Basic Auth ● Additional overhead of Docker ● Each usage requires a pull of the container
- 33. THE END
- 39. Fonts
- 40. Agenda ▪ First Presenter ▪ Topic Lorem ipsum dolor sit amet consectetur. ▪ Second Presenter ▪ Topic Lorem ipsum dolor sit amet. ▪ Third Presenter
- 41. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.
- 42. Colors
- 44. Content Slides
- 45. Basic Slide • Bullet 1 • Sub-bullet • Sub-bullet • Bullet 2 • Sub-bullet • Sub-bullet
- 46. Reduce Long Titles • Bullet 1 • Sub-bullet • Sub-bullet • Bullet 2 • Sub-bullet • Sub-bullet By splitting them into a short title, and a more detailed subtitle using this slide format that includes a subtitle area
- 47. Two Columns ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format ▪ Bulleted list format • Headline Format Headline Format
- 48. Two Box ▪ Bulleted list ▪ Bulleted list ▪ Bulleted list ▪ Bulleted list • Category • Category
- 49. Three Box ▪ Bulleted list ▪ Bulleted list • Bulleted list • Bulleted list • Category • Category • Bulleted list • Bulleted list • Category
- 50. Four Box ▪ Bulleted list ▪ Bulleted list ▪ Bulleted list ▪ Bulleted list • Category • Category ▪ Bulleted list ▪ Bulleted list • Category ▪ Bulleted list ▪ Bulleted list • Category
- 51. Shapes
- 52. Shapes Rounded corner rectangle Double corner rectangle Double corner rectangle
- 54. Table Column Column Column Row Value Value Value Row Value Value Value Row Value Value Value Row Value Value Value Row Value Value Value Row Value Value Value Row Value Value Value
- 55. Bar chart
- 56. Line chart
- 57. Pie Chart
- 58. Quotes and Text Callouts
- 59. Attribution Format Second line of attribution This is a template for a quote slide. This is where the quote goes. Attribute the source below.
- 60. Logos
- 61. Data + AI Summit Logos
- 62. Databricks Logos