Skill File

Check Experiments

Name: Check Experiments
Author: Clay-HHK

Standardized tmux experiment monitoring with progress parsing, CSV log reading, and status reporting

Clay-HHK0 starsApr 12, 2026

Occupation: Data Scientists
Categories: Lab Tools

Skill Content

Standardized workflow for checking all running ML experiments via tmux sessions.

When to Use

Checking experiment progress across multiple GPU runs
Resuming a session and needing status of long-running experiments
Monitoring hyperparameter sweeps or ablation studies
Before starting new experiments (check resource availability)

Workflow

Step 1: List All tmux Sessions

tmux ls

Report each session: name, creation time, attached/detached status.

Step 2: Capture Recent Output

For each session, capture the last 30-50 lines:

tmux capture-pane -t <session-name> -p | tail -50

Related Skills

Check Experiments | Skills Pool

# Find recent CSV logs
find . -name "*.csv" -newer <start-time> -type f

# Read last few rows
tail -5 <log-file.csv>

## Experiment Status Report

| Session | Status | Progress | Metric | ETA |
|---------|--------|----------|--------|-----|
| sweep-lr | Running | 45/100 epochs | loss: 0.234 | ~2h |
| ablation-1 | Running | 78% | acc: 0.891 | ~30min |
| baseline | Completed | 100/100 | loss: 0.198 | - |
| sweep-wd | Error | 23/100 | CUDA OOM | - |

Check Experiments

When to Use

Workflow

Step 1: List All tmux Sessions

Step 2: Capture Recent Output

Check Experiments

When to Use

Workflow

Step 1: List All tmux Sessions

Step 2: Capture Recent Output

Step 3: Parse Progress

Step 4: Check CSV/Log Files

Step 5: Check for Errors

Step 6: Generate Status Report

Step 7: Update experiments.md (if exists)

Important Rules

Integration

Automation Audit Ops

Github Qa Labels

Jupyter Notebook

Tidb Integrationtest Recorder

Quality Nonconformance

Hugging Face Trackio