- 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
291 B
291 B
Standard ML Experiment
Minimal PyTorch neural network classification experiment.
Usage
python train.py --epochs 5 --batch_size 32 --learning_rate 0.001 --output_dir ./results
Results
Results are saved in JSON format with training metrics and PyTorch model checkpoint.