Scaling your data infrastructure

Scaling your data infrastructure @ PyConNove

1. Scaling your data infrastructure C H R I S T I A N B A R R A @ P Y C O N N O V E
2. THE AGENDA 2 3 START THE DATA SCIENCE WORKFLOW SCALING IS NOT JUST A MATTER OF MACHINE WHEN THE SIZE OF YOUR DATA MATTERS 1
3. THE AGENDA 4 5 CONTAINERIZED DATA SCIENCE CASSINY: PUT ALL THE THINGS TOGETHER END
4. THE DATA SCIENCE WORKFLOW
5. HEXAGON PRESENTATION TEMPLATE
6. HOW YOU BUILD, ITERATE AND SHARE DEPENDS ON MANY THINGS Your Users Your Product Your Team Your Company Your Tech Stack Your Domain
7. SCIKIT-LEARN DOCKER DATA SCIENCE TOOLBELT PANDAS JUPYTER RAY
8. SCALING IS NOT JUST A MATTER OF MACHINES
9. We all use it.
10. We really care about versioning. We have Untitled_1.ipynb, Untitled_2.ipynb and Untitled_3.ipynb. HOMER SIMPSON C H I E F D A T A S C I E N T I S T D A T A B E E R I N C
11. Since JSON is a plain text format, they can be version-controlled and shared with colleagues. E X I P Y T H O N N O T E B O O K D O C U M E N T A T I O N
12. THEY GOT IT RIGHT
13. BUT WE KEEP IMPROVING
14. 90% OF JUPITER IS MADE BY HYDROGEN
15. THE HARD THING ABOUT STORAGE
16. PARQUET P A R Q U E T + O B J E C T S T O R A G E = YO U C A N Q U E R Y I T U S I N G S Q L PA N DA S H A S N AT I V E S U P P O R T F O R G E T A B O U T C S V
17. WHEN THE SIZE OF YOUR DATA MATTERS
18. IT’S TOO SLOW DOESN’T FIT IN YOUR RAM
19. CODE OPTIMIZATION APPROACH SCALING FROM DIFFERENT SIDES A BIGGER MACHINE USE MULTIPLE CORES MORE MACHINES FRAMEWORKS: DASK RAY SPARK PANDAS: READ BY CHUNKS SCIKIT-LEARN: PARTIAL FIT
20. chunks & partial_fit 1 M A C H I N E
21. Multiple machines. n M A C H I N E S
22. I don’t want to use Spark/JVM, what do you have for me? H A P P Y P Y T H O N U S E R
23. WHAT IS RAY?
24. A high-performance distributed execution engine REDIS SCHEDULER WORKER ARROW & PLASMA
25. Use pandas through ray to query parquet files in an object storage. W O R K I N P R O G R E S S
26. CONTAINERIZED DATA SCIENCE
27. If you trained a model with scikit-learn 0.18.1, will the same model work with 0.19.1? P R O B L E M # 1
28. How do you share your models? P R O B L E M # 2
29. How do you put your models in production? P R O B L E M # 3
30. Containerize everything. T H E A N S W E R
31. 1. It’s damn easy to move things around 2. You get versioning for free 3. Stack agnostic 4. Move Docker images around T O R E C A P
32. CASSINY: PUT ALL THE THINGS TOGETHER
33. CLEAR REQUIREMENTS CONTAINERIZED EASY OBJECT STORAGE JUPYTER + IPYTHON PLATFORM AGNOSTIC
34. OPEN SOURCE
35. DEMO
36. TAKEAWAYS UNIFIED DATA WAREHOUSE KEEP YOUR CODE RUNNING ON ONE MACHINE USE DOCKER TRY RAY BRING CI/CD TO YOUR DATASCIENCE WORKFLOW OBJECT STORAGE IS COOL DISTRIBUTED COMPUTING IS HARD I DIDN’T HAVE ANOTHER POINT