This tutorial and the assets can be downloaded as part of the Wallaroo Tutorials repository.

## How to Convert XGBoost to ONNX

The following tutorial is a brief example of how to convert a XGBoost ML model to the ONNX standard. This allows organizations that have trained XGBoost models to convert them and use them with Wallaroo.

This tutorial assumes that you have a Wallaroo instance and are running this Notebook from the Wallaroo Jupyter Hub service.

This tutorial provides the following:

`housing_model_xgb.pkl`

: A pretrained model used as part of the Notebooks in Production tutorial. This model has a total of 18 columns.

## Conversion Process

### Libraries

The first step is to import our libraries we will be using.

```
import onnx
import pickle
from onnxmltools.convert import convert_xgboost
from skl2onnx.common.data_types import FloatTensorType, DoubleTensorType
```

### Set Variables

The following variables are required to be known before the process can be started:

**number of columns**: The number of columns used by the model.**TARGET_OPSET**: Verify the TARGET_OPSET value taht will be used in the conversion process matches the current Wallaroo model uploads requirements.

```
# set the number of columns
ncols = 18
TARGET_OPSET = 15
```

### Load the XGBoost Model

Next we will load our model that has been saved in the `pickle`

format and unpickle it.

```
# load the xgboost model
with open("housing_model_xgb.pkl", "rb") as f:
xgboost_model = pickle.load(f)
```

### Conversion Inputs

The `convert_xgboost`

method has the following format and requires the following inputs:

```
convert_xgboost({XGBoost Model},
{XGBoost Model Type},
[
('input',
{Tensor Data Type}([None, {ncols}]))
],
target_opset={TARGET_OPSET})
```

**XGBoost Model**: The XGBoost Model to convert.**XGBoost Model Type**: The type of XGBoost model. In this example is it a`tree-based classifier`

.**Tensor Data Type**: Either`FloatTensorType`

or`DoubleTensorType`

from the`skl2onnx.common.data_types`

library.**ncols**: Number of columns in the model.**TARGET_OPSET**: The target opset which can be derived in code showed below.

## Convert the Model

With all of our data in place we can now convert our XBBoost model to ONNX using the `convert_xgboost`

method.

```
onnx_model_converted = convert_xgboost(xgboost_model, 'tree-based classifier',
[('input', FloatTensorType([None, ncols]))],
target_opset=TARGET_OPSET)
```

## Save the Model

With the model converted to ONNX, we can now save it and use it in a Wallaroo pipeline.

```
onnx.save_model(onnx_model_converted, "housing_model_xgb.onnx")
```