Skip to main content

A model lifecycle tracker database backed by postgres

Project description


A simple data store keeping track of models and things

Running locally

  • Get postgres up and running for local development
 docker pull postgres
 docker run --rm   --name pg-docker -e POSTGRES_PASSWORD=docker -d -p 5432:5432
  • Flash database for quick dev
PGPASSWORD='docker' psql -h localhost -U postgres -c "drop database modeltracker" 
  && PGPASSWORD='docker' psql -h localhost -U postgres -c "create database modeltracker"
  • Populate the basic type tables
python -m modeltracker.main
  • Suppose you have been developing and wish to destroy all table contents:
python -m modeltracker.main -r

Database dict

To keep track of what is being produced by the modeltracker

datastore_type : Describes the datastore types, BQ or GCS for instance.

feature_store_metrics : Describes the metrics relating to each model in the model_catalog_id

job : Describes tasks that have been run

model_catalog : describes models and links to state_id

model_output : describes location and datastore type of model_output

state : Catalogue of states

task_type : Tracks tasks that occurr

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release. See tutorial on generating distribution archives.

Built Distribution

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Huawei Huawei PSF Sponsor Microsoft Microsoft PSF Sponsor NVIDIA NVIDIA PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page