> For the complete documentation index, see [llms.txt](https://docs.seldon.ai/mlserver/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.seldon.ai/mlserver/examples/model-repository.md).

# 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](/mlserver/examples/mms.md), 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](https://github.com/triton-inference-server/server/blob/master/docs/protocol/extension_model_repository.md) 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](/mlserver/examples/mms.md). You can check the details on how they are trained following that notebook.

```python
!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**.

```shell
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.

```python
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.

```python
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.

```python
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.

```python
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.

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

```python
```