Quick Start

Install using pip

The model store library is available via Pypi:

pip install modelstore

Create a model store instance and point it to your storage

The model store library supports storing models to blob storage across different cloud providers:

To start, import ModelStore with:

from modelstore import ModelStore

And then create a model store instance by using one of the following factory methods.

File System Storage

model_store = ModelStore.from_file_system(root_directory="/path/to/directory")

Google Cloud Storage Bucket

model_store = ModelStore.from_gcloud(

AWS s3 Bucket

model_store = ModelStore.from_aws_s3(

Azure Blob Storage

model_store = ModelStore.from_azure(container_name="my-container-name")

Upload a model to the model store

Model store has an upload() function that will create an archive containing your model and upload it to your storage.

Whenever you upload a model, you need to specify which domain it belongs to. A “domain” is a string that model store uses to group several models that are for the same end-usage together.

For example, let’s say you’ve trained a scikit-learn model (which is stored in a variable called clf) that is going to be used in a spam classifier domain.

To store the model, use:

meta_data = model_store.upload("spam-detection", model=clf)

The upload() function returns a dictionary containing meta data about your model - including the id that has been assigned to it, which is in meta_data["model"]["model_id"].

Load a model from the model store

Once a model has been stored, you can load it straight from storage back into memory using model store’s load() function.

clf = model_store.load("spam-detection", model_id="abcd-abcd-abdc")

Download a model from the model store

If you would rather download the model, and not load it into memory, you can use model store’s download() function.

file_path = model_store.download(
   local_path=".", # Where to download the model to
   domain="example-model", # The model's domain
   model_id="model-id"  # Optional; the ID of the specific model