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Kubernetes for machine learning: productivity over primitives - PowerPoint PPT Presentation

Mar 22, 2023 •218 likes •1.24k views

Kubernetes for machine learning: productivity over primitives Sophie Watson @sophwats sophie@redhat.com William Benton @willb willb@redhat.com Kubernetes for machine learning: productivity over primitives Sophie

  1. Stateless microservices @sophwats @willb

  2. Stateless microservices @sophwats @willb

  3. Stateless microservices @sophwats @willb

  4. Stateless microservices @sophwats @willb

  5. Stateless microservices @sophwats @willb

  6. Stateless microservices @sophwats @willb

  7. Stateless microservices @sophwats @willb

  8. Declarative app configuration @sophwats @willb

  9. Integration and deployment OK! @sophwats @willb

  10. Integration and deployment application code OK! configuration and installation recipes base image @sophwats @willb

  11. Integration and deployment application code OK! configuration and installation recipes base image @sophwats @willb

  12. Integration and deployment application code configuration and installation recipes base image @sophwats @willb

  13. What containers offer for machine learning workflows @sophwats @willb

  14. FROM centos:centos7 RUN yum install -y \ python python-pip \ java java-devel git ENTRYPOINT /bin/bash @sophwats @willb

  15. FROM centos:centos7 RUN yum install -y \ python python-pip \ java java-devel git ENTRYPOINT /bin/bash @sophwats @willb

  16. @sophwats @willb

  17. @sophwats @willb

  18. 0.13 0.13 0 0 0 1 1 0 1 0 1 0 0 0 1 0 0 0 1 1 0 0 0.06 0.07 1 0 1 1 0 1 0 0 0 0 0.07 0.06 0 0 0 0 0 0 1 1 0 1 0.02 0.08 0 1 0 0 1 0 0 1 0 0 0.17 0.11 * 1 0 0 0 0 1 0 1 1 0 0.11 0.09 0 0 1 0 1 0 1 0 0 0 0.04 0.18 0 1 0 0 0 1 0 0 1 1 0.13 0.04 0 0 0 0 1 0 0 1 0 1 0.13 0.21 1 1 0 0 0 0 0 0 0 1 0.14 0.03 @sophwats @willb

  19. Self-service environments more CPUs better GPUs more storage sensitive data @sophwats @willb

  22. @sophwats @willb

  23. @sophwats @willb

  24. O K ! O K ! @sophwats @willb

  25. No friction: mybinder.org @sophwats @willb

  28. More flexible: source-to-image % @sophwats @willb

  29. More flexible: source-to-image % builder image application image https://github.com/openshift/source-to-image @sophwats @willb

  30. oc new-app getwarped/s2i-minimal-notebook:latest~\ https://github.com/willb/probabilistic-structures \ -e JUPYTER_NOTEBOOK_PASSWORD=developer @sophwats @willb

  31. oc new-app getwarped/s2i-minimal-notebook:latest~\ oc new-app getwarped/s2i-minimal-notebook:latest https://github.com/willb/probabilistic-structures \ -e JUPYTER_NOTEBOOK_PASSWORD=developer @sophwats @willb

  32. oc new-app getwarped/s2i-minimal-notebook:latest~\ oc new-app https://github.com/willb/probabilistic-structures \ https://github.com/willb/probabilistic-structures -e JUPYTER_NOTEBOOK_PASSWORD=developer @sophwats @willb

  33. oc new-app getwarped/s2i-minimal-notebook:latest~\ oc new-app https://github.com/willb/probabilistic-structures \ -e JUPYTER_NOTEBOOK_PASSWORD=developer -e JUPYTER_NOTEBOOK_PASSWORD=developer @sophwats @willb

  34. @sophwats @willb

  35. @sophwats @willb

  36. @sophwats @willb

  39. codifying data model model feature feature model model model monitoring, monitoring, problem 
 collection training training engineering engineering validation deployment deployment validation validation and metrics and cleaning and tuning and tuning @sophwats @willb

  40. codifying data model model feature feature model model model monitoring, monitoring, problem 
 collection training training engineering engineering validation deployment deployment validation validation and metrics and cleaning and tuning and tuning @sophwats @willb

  43. m A @sophwats @willb

  44. m A @sophwats @willb

  45. (joint) distribution of input data? distribution of predictions? m A distribution of number of multiplications while scoring? @sophwats @willb

  48. @sophwats @willb

  49. Where from here? @sophwats @willb

  50. transform developer UI web and mobile events transform federate databases archive file, object transform reporting storage train management models codifying data feature model model codifying model data model monitoring, feature model model monitoring, problem 
 collection engineerin training deploymen problem 
 collection training validation validation engineering validation deployment validation and metrics and cleaning and g and tuning t and metrics and tuning @sophwats @willb

  51. application developers data engineers web and transform developer UI events mobile transform federate databases archive file, object transform reporting storage train management models data scientists @sophwats @willb

  52. application developers data engineers web and transform developer UI events mobile transform federate databases archive file, object transform reporting storage train management models machine learning engineers data scientists @sophwats @willb

  53. opendatahub.io @sophwats @willb

  54. radanalytics.io @sophwats @willb

  55. Kubeflow @sophwats @willb

  56. What did we talk about today? @sophwats @willb

  57. @sophwats @willb

  58. @sophwats @willb

  59. @sophwats @willb

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