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<!--
******************************************************************************
* Copyright by Contributors 2017-2023
*******************************************************************************/-->
# SYCL-based Algorithm for Tree Construction
This plugin adds support of SYCL programming model for prediction algorithms to XGBoost.
## Usage
Specify the 'device' parameter as described in the table below to offload model training and inference on SYCL device.
### Algorithms
| device | Description |
| --- | --- |
sycl | use default sycl device |
sycl:gpu | use default sycl gpu |
sycl:cpu | use default sycl cpu |
sycl:gpu:N | use sycl gpu number N |
sycl:cpu:N | use sycl cpu number N |
Python example:
```python
param['device'] = 'sycl:gpu:0'
```
Note: 'sycl:cpu' devices have full functional support but can't provide good enough performance. We recommend use 'sycl:cpu' devices only for test purposes.
Note: if device is specified to be 'sycl', device type will be automatically chosen. In case the system has both sycl GPU and sycl CPU, GPU will on use.
## Dependencies
To build and use the plugin, install [Intel® oneAPI DPC++/C++ Compiler](https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler.html).
See also [Intel® oneAPI Programming Guide](https://www.intel.com/content/www/us/en/docs/oneapi/programming-guide/2024-0/overview.html).
## Build
From the ``xgboost`` directory, run:
```bash
$ cmake -B build -S . -DPLUGIN_SYCL=ON
$ cmake --build build -j
```
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