---
title: wrap_mlmodel (Python agent API)
source: https://docs.newrelic.com/docs/apm/agents/python-agent/python-agent-api/wrapmlmodel-python-agent-api
---

## Syntax

```py
newrelic.agent.wrap_mlmodel(model, name=None, version=None, feature_names=None, label_names=None, metadata=None)
```

Enables manual instrumentation of machine learning models.

## Requirements

Python agent version 9.1.0 or higher.

## Description

This allows for manual instrumentation of machine learning models.

## Parameters

| Parameter                          | Description                                               |
| ---------------------------------- | --------------------------------------------------------- |
| `model` _object_                   | Required. `model` is the fitted machine learning model.   |
| `name` _string_                    | Optional. The name of the custom model.                   |
| `version` _string_                 | Optional.  The release version of the custom model.       |
| `feature_names` _list_ of _string_ | Optional. A list of strings denoting the feature name(s). |
| `label_names` _list_ of _string_   | Optional. A list of strings denoting the label name(s).   |
| `metadata` _dict_                  | Optional.  Metadata to attach to the model.               |

## Return values

None.

## Examples

### Wrap machine learning model [#wrap-ml-model]

An example of instrumenting a custom machine learning model:

```py
def wrap_ml_example():
    x_train = [[0, 0], [1, 1]]
    y_train = [0, 1]
    x_test = [[1.0, 2.0]]

    model = CustomTestModel().fit(x_train, y_train)
    wrap_mlmodel(
        model,
        name="MyCustomModel",
        version="1.2.3",
        feature=["feature0", "feature1"],
        label=["label0"],
        metadata={"metadata1": "value1", "metadata2": "value2"},
    )

    labels = model.predict(x_test)

    return model
```