The Model Store Structure

This library’s model store interacts with a backend of your chosing. The library currently supports:

This library stores models in cloud buckets using a pre-defined structure.

Model Archive & Meta Data

When you use create_archive(), an artifacts.tar.gz file is created in the current working directory. This archive contains:

  1. Any files that were dumped from your model,

  2. A "python-info.json" file that enumerates the version of the Python library of the model you are exporting.

  3. Files containing any additional data you want to add to the archive.

When you upload() a model archive, this library uploads the archive to cloud storage, and creates and returns a dictionary containing meta-data about the model.

The meta-data includes:

  • A unique UUID4 for your model;

  • Details about where the model is being uploaded to (the bucket and prefix);

  • The Python runtime that was used (e.g., “python:3.7.0”)

  • The user who ran the training.

  • Versions for the Python library and key dependencies.

Model Domains

A domain is the word we use to group models, that are all intended for the same end-usage, together.

Under the hood, this is just a string, so it is up to you how you would like to use it; it is required because this library stores models by domain.

File Storage Structure

When you pick a backend that stores data in files (e.g., Cloud Storage Buckets), the files are stored with a pre-defined structure.

The top-level, root prefix that this library hard-codes is operatorai-model-store.

When you create and upload a model archive, this library will upload three files to different places in the bucket.

1. The artifacts archive will be uploaded to: root/<domain>/<datetime>/archive.tar.gz, where the datetime has the form "%Y/%m/%d/%H:%M:%S" - denoting the time when the model was uploaded. 2. The library creates a dictionary of meta-data about your model. This will be uploaded to root/<domain>/versions/<model-id>.json. 3. This same meta-data is also stored in root/<domain>/latest.json, which tracks the _last_ model that was uploaded to the model store.


Let’s imagine you’re training a text classifier to detect whether some customer text is about “refunds.”

Over time, you may end up re-training this classifier several times, with newer data or different models types; however, you still need a way to denote that all of these models were about detecting refund requests.

In this case, you could set the domain="customer-refunds".

Models that are exported in this domain will be stored to: