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Authors: Hritik Vijay, Philippe Ombredanne
2022-06-22

tldr - powered by Generative AI

The presentation discusses the challenges of package and dependency management in software development and proposes solutions such as using package URLs and a universal versioning system.
  • The complexity of package and dependency management in software development makes it difficult to express boundaries between dependencies and automate the process.
  • Solutions proposed include providing installation prerequisites, using a single package manager, and using general-purpose package managers such as Spack, Conda, Nix, and Guix.
  • Package URLs can be used to name packages and a universal versioning system can be used to deal with version ranges.
  • The universal versioning system can accommodate different versioning schemes and express version ranges in a universal way.
Authors: Harsh Thakur
2021-10-13

tldr - powered by Generative AI

The talk explains the lifecycle of Custom Resource Definitions (CRDs) and the challenges of versioning them. It provides insights on how to upgrade CRDs seamlessly, with zero downtime and backwards compatibility.
  • CRDs are used to extend Kubernetes and as projects grow, the definitions of the resource start to evolve and may completely change, requiring versioning of CRDs
  • Versioning of CRDs can be tough as end users need to be provided with seamless upgrades, zero downtime and backwards compatibility
  • There are two views of CRD versioning: server version and stored version
  • There are two conversion strategies: last mile change and full conversion
  • The conversion package simplifies conversion functions between API versions
  • The storage version migrator component bumps up existing objects to the new desired storage version
  • Lossless conversion is followed where users can roll back to the older version
  • Annotations of the field of the CR are used to store functions which lose data between versions
Conference:  Transform X 2021
Authors: Chun Jiang, Alessya (Labzhinova) Visnjic, Adrian Macneil, Ville Tuulos, Elliot Branson
2021-10-07

tldr - powered by Generative AI

Importance of structured and quality data cataloging for machine learning in production
  • Structured and easily queryable location for data cataloging is important
  • Quality of data should be known to avoid wasting time on processing and feature processing
  • Catch regressions early by putting checks upstream in the build process
  • Lock device version for on-device logging
  • Record metadata for debugging purposes
  • Involve subject matter experts for debugging machine learning models