mlserver-xgboost 1.5.0

XGBoost runtime for MLServer

XGBoost runtime for MLServer

This package provides a MLServer runtime compatible with XGBoost.

Usage

You can install the runtime, alongside mlserver, as:

pip install mlserver mlserver-xgboost

For further information on how to use MLServer with XGBoost, you can check out this worked out example.

XGBoost Artifact Type

The XGBoost inference runtime will expect that your model is serialised via one of the following methods:

Extension	Docs	Example
*.json	JSON Format	booster.save_model("model.json")
*.ubj	Binary JSON Format	booster.save_model("model.ubj")
*.bst	(Old) Binary Format	booster.save_model("model.bst")

By default, the runtime will look for a file called `model.[json | ubj | bst]`.
However, this can be modified through the `parameters.uri` field of your
{class}`ModelSettings <mlserver.settings.ModelSettings>` config (see the
section on [Model Settings](../../docs/reference/model-settings.md) for more
details).

```{code-block} json
---
emphasize-lines: 3-5
---
{
  "name": "foo",
  "parameters": {
    "uri": "./my-own-model-filename.json"
  }
}
```

Content Types

If no content type is present on the request or metadata, the XGBoost runtime will try to decode the payload as a NumPy Array. To avoid this, either send a different content type explicitly, or define the correct one as part of your model's metadata.

Model Outputs

The XGBoost inference runtime exposes a number of outputs depending on the model type. These outputs match to the predict and predict_proba methods of the XGBoost model.

Output	Returned By Default	Availability
predict	✅	Available on all XGBoost models.
predict_proba	❌	Only available on non-regressor models (i.e. XGBClassifier models).

By default, the runtime will only return the output of predict. However, you are able to control which outputs you want back through the outputs field of your {class}InferenceRequest <mlserver.types.InferenceRequest> payload.

For example, to only return the model's predict_proba output, you could define a payload such as:

---
emphasize-lines: 10-12
---
{
  "inputs": [
    {
      "name": "my-input",
      "datatype": "INT32",
      "shape": [2, 2],
      "data": [1, 2, 3, 4]
    }
  ],
  "outputs": [
    { "name": "predict_proba" }
  ]
}