Quick Start

This library’s ModelStore enables you to export trained ML models and store it to your choice of storage.

Create a model store instance

modelstore currently supports storing models to:

  • Your local file system

  • Google Cloud buckets: The library assumes that you already have set up a Google Cloud project and have created a cloud storage bucket.

  • AWS S3 buckets: As above, this library assumes that you have already set up a project and have created an s3 bucket.

To save your models, create a model store instance with:

from modelstore import ModelStore

ms = ModelStore.from_gcloud(

For AWS, use ModelStore.from_aws_s3(bucket_name="<s3-bucket>")

For your local file system, use ModelStore.from_file_system(root="<path>")

Export a model

The modelstore library detects which types of machine learning libraries you have installed, and automatically sets up functions that enable you to export a trained model.

All of the functions have the form ms.<library-name>.create_archive().

For example, to export a scikit-learn model, use:

archive = ms.sklearn.create_archive(model=my_model)

This function will create a file called artifacts.tar.gz in your current working directory, which contains the exported model.

To read more about the supported libraries, see: Supported Machine Learning Libraries.

Upload the model to cloud storage

To save your models into your chosen storage, use upload():

rsp = ms.upload(domain="example-model", archive)

When you upload a model, you need to specify a domain. This is the word we use to group several models together. For example, let’s assume you are training several models to predict whether an email is spam. Setting domain="spam-detection" will store all of those models together.

To read more about how this library organises models, see The Model Store Structure.

Download a model from cloud storage

To retrieve a model from your chosen storage, use download():

file_path = ms.download(local_path=".", domain="example-model", model_id="model-id")

This function will download the artifacts.tar.gz that you stored, extract all the files from it, and remove the tar file.

If you do not provide a model_id parameter, the download() function will default to the last model that was stored for the given domain.