algovault

Experimentation tracking

Features

  • Fully embrace the relational model
    • i.e. run is many-to-many with experiment, so reuse of run results across experiments is possible
  • all operations idempotent
    • Suitable for use in a workflow orchestration system
  • built-in checking for presence of results
    • can be used to cache runs
  • high performance read and write
    • I'm looking at you, mlflow.get_metric_history
  • dead-simple integration points
    • The data model is simply sqlite files
  • serverless
  • built-in aggregations
    • computing common aggregates is crazy fast and requires little memory
  • no magic
    • no global context means you can paralellize fearlessly

Design

  • writers write to a local copy of sqlite database (maybe in-memory?)
  • runs end and the databases sent to checkpoint location
  • upon read, compact checkpoints to a single database instance (read replica)
  • read replica knows which instances have already been ingested, incremental update

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.1.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for ecurtin2 from gravatar.com ecurtin2

Requires

Python >=3.7

Classifiers

Report project as malware

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