4aecd469a1
- Add Prometheus, Grafana, and Loki monitoring stack - Include pre-configured dashboards for ML metrics and logs - Add Podman container support with security policies - Implement ML runtime environments for multiple frameworks - Add containerized ML project templates (PyTorch, TensorFlow, etc.) - Include secure runner with isolation and resource limits - Add comprehensive log aggregation and alerting
297 B
297 B
XGBoost Experiment
Gradient boosting experiment using XGBoost for binary classification.
Usage
python train.py --n_estimators 100 --max_depth 6 --learning_rate 0.1 --output_dir ./results
Results
Results are saved in JSON format with accuracy metrics and XGBoost model file.