wanshuiyin/monitor-experiment

Monitor Experiment Results

Monitor: $ARGUMENTS

Workflow

Step 1: Check What's Running

First identify the backend from AGENTS.md, run notes, or launch summary: local, SSH, Vast.ai, or Modal. Monitor the backend that was actually used; do not assume a plain SSH screen session when the run was launched through Vast.ai or Modal.

ssh <server> "screen -ls"

For Vast.ai, also check instance state, SSH reachability, hourly cost, and whether auto_destroy is pending. For Modal, check the Modal run/app logs, function status, timeout, volume outputs, and cloud cost exposure.

Step 2: Collect Output from Each Screen

For each screen session, capture the last N lines:

ssh <server> "screen -S <name> -X hardcopy /tmp/screen_<name>.txt && tail -50 /tmp/screen_<name>.txt"

If hardcopy fails, check for log files or tee output.

Step 3: Check for JSON Result Files

ssh <server> "ls -lt <results_dir>/*.json 2>/dev/null | head -20"

If JSON results exist, fetch and parse them:

ssh <server> "cat <results_dir>/<latest>.json"

Step 3.5: Pull W&B Metrics (when wandb: true in AGENTS.md)

If the project enables W&B, pull metrics before interpreting results. Prefer W&B as the source of training curves and recent eval state, while still checking logs for crashes.

List recent runs:

python3 - <<'PY'
import wandb
api = wandb.Api()
for run in api.runs("<entity>/<project>", per_page=20):
    print(run.name, run.state, run.url)
PY

Pull recent history for a specific run:

python3 - <<'PY'
import wandb
api = wandb.Api()
run = api.run("<entity>/<project>/<run_id>")
for row in run.history(samples=50, keys=["train/loss", "eval/loss", "eval/accuracy", "train/lr"]):
    print(row)
print("summary:", dict(run.summary))
PY

If W&B is configured but unavailable, report the connectivity problem and fall back to screen/log/json evidence. Do not interpret missing W&B data as experiment failure by itself.

Always include W&B dashboard links (run.url) when available so later review and paper-writing agents can inspect the exact training curves.

Step 4: Summarize Results

Present results in a comparison table:

| Experiment | Metric | Delta vs Baseline | Status |
|-----------|--------|-------------------|--------|
| Baseline  | X.XX   | —                 | done   |
| Method A  | X.XX   | +Y.Y              | done   |

Step 5: Interpret

• Compare against known baselines
• Flag unexpected results (negative delta, NaN, divergence)
• Suggest next steps based on findings

Step 6: Feishu Notification (if configured)

After results are collected, check ~/.codex/feishu.json:

• Send experiment_done notification: results summary table, delta vs baseline
• If config absent or mode "off": skip entirely (no-op)

Key Rules

• Always show raw numbers before interpretation
• Compare against the correct baseline (same config)
• Note if experiments are still running (check progress bars, iteration counts)
• If results look wrong, check training logs for errors before concluding
• Include backend cost/risk notes for long-running Vast.ai or Modal jobs