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" }
]
}
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Release files for mlserver-xgboost 1.7.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mlserver_xgboost-1.7.1.tar.gz | 6.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mlserver_xgboost-1.7.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 14.9 kB
Release files / mlserver_xgboost-1.7.1.tar.gz
| Download URL | mlserver_xgboost-1.7.1.tar.gz |
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| Tags | Python 3 |
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