check-experiments

You can monitor ML experiment progress by collecting tmux session outputs and parsing terminal text for epoch, step, and metric values. This generates a concise status report without attaching to or altering the active sessions.

To check for CUDA OOM and Traceback errors across multiple sweeps, scan recent terminal outputs and log files for specific failure patterns. This detects crashes across multi-GPU training runs and flags them in a status table.

Yes, you can read recent CSV log rows to extract loss and metric values from running experiments. Parsing the latest CSV entries captures current training metrics and integrates them into a concise status summary.

Yes, tmux experiment monitoring works for multi-GPU training and ablation studies by enumerating active sessions and parsing their outputs. It reports progress and detects failures across multiple concurrent runs without interrupting them.

The best way to summarize experiment status and ETAs for documentation is to parse terminal outputs and CSV logs into a structured table. This formatted report includes progress, key metrics, and failure states for easy sharing.