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

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Release history Release notifications | RSS feed

0.1.0

Dec 18, 2022

0.0.314159

Dec 18, 2022

This release

0.0.3

Dec 21, 2022

0.0.2

Dec 18, 2022

algovault 0.0.3

algovault

Features

Design

Key dates

1 maintainer

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