# Model Repository API¶

MLServer supports loading and unloading models dynamically from a models repository. This allows you to enable and disable the models accessible by MLServer on demand. This extension builds on top of the support for Multi-Model Serving, letting you change at runtime which models is MLServer currently serving.

The API to manage the model repository is modelled after Triton’s Model Repository extension to the V2 Dataplane and is thus fully compatible with it.

This notebook will walk you through an example using the Model Repository API.

## Training¶

First of all, we will need to train some models. For that, we will re-use the models we trained previously in the Multi-Model Serving example. You can check the details on how they are trained following that notebook.

!cp -r ../mms/models/* ./models


## Serving¶

Next up, we will start our mlserver inference server. Note that, by default, this will load all our models.

mlserver start .


## List available models¶

Now that we’ve got our inference server up and running, and serving 2 different models, we can start using the Model Repository API. To get us started, we will first list all available models in the repository.

import requests

response = requests.post("http://localhost:8080/v2/repository/index", json={})
response.json()


As we can, the repository lists 2 models (i.e. mushroom-xgboost and mnist-svm). Note that the state for both is set to READY. This means that both models are loaded, and thus ready for inference.

## Unloading our mushroom-xgboost model¶

We will now try to unload one of the 2 models, mushroom-xgboost. This will unload the model from the inference server but will keep it available on our model repository.

requests.post("http://localhost:8080/v2/repository/models/mushroom-xgboost/unload")


If we now try to list the models available in our repository, we will see that the mushroom-xgboost model is flagged as UNAVAILABLE. This means that it’s present in the repository but it’s not loaded for inference.

response = requests.post("http://localhost:8080/v2/repository/index", json={})
response.json()


## Loading our mushroom-xgboost model back¶

We will now load our model back into our inference server.

requests.post("http://localhost:8080/v2/repository/models/mushroom-xgboost/load")


If we now try to list the models again, we will see that our mushroom-xgboost is back again, ready for inference.

response = requests.post("http://localhost:8080/v2/repository/index", json={})
response.json()