Quick Start

Install using pip, import in your code

The model store library is available via Pypi:

pip install modelstore

In your code, import ModelStore with:

from modelstore import 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:

• A file system;

• Google Cloud storage buckets

• AWS s3 buckets

• Azure blob storage containers

• MinIO object storage

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(
   project_name="my-project",
   bucket_name="my-bucket",
)

AWS s3 Bucket

model_store = ModelStore.from_aws_s3(
   bucket_name="my-bucket",
)

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")