sample.json

{
  "guid": "61290EED-2CEF-4F59-AA62-DB6DC74F6421",
  "name": "Intel® Python XGBoost Performance",
  "categories": ["Toolkit/oneAPI AI And Analytics/Features And Functionality"],
  "description": "This sample code illustrates how to analyze the performance benefit from using Intel training optimizations upstreamed by Intel to latest XGBoost compared to un-optimized XGBoost 0.81 ",
  "builder": ["cli"],
  "languages": [{"python":{}}],
  "dependencies": ["intelpython"], 
  "os":["linux"],
  "targetDevice": ["CPU"],
  "ciTests": {
    "linux": [
    {
      "env": [
        "source /intel/oneapi/intelpython/bin/activate",
        "conda activate base"
      ],
      "id": "idp_xgb_performance",
      "steps": [
        "python IntelPython_XGBoost_Performance.py",
        "pip install uv",
        "uv python pin $(which python)",
        "uv venv --system-site-packages",
        "uv sync",
        "uv run python IntelPython_XGBoost_Performance.py"
      ]
    }
    ]
},
"expertise": "Code Optimization"
}