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fetch_ml/podman/workspace/xgboost_project
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Jeremie Fraeys 4aecd469a1 feat: implement comprehensive monitoring and container orchestration 
- 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
2025-12-04 16:54:49 -05:00
..
README.md feat: implement comprehensive monitoring and container orchestration 2025-12-04 16:54:49 -05:00
requirements.txt feat: implement comprehensive monitoring and container orchestration 2025-12-04 16:54:49 -05:00
train.py feat: implement comprehensive monitoring and container orchestration 2025-12-04 16:54:49 -05:00

README.md

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.