shaun-z/training-check

Training Check

Periodically read WandB metrics during training to catch problems early. Do not wait until training finishes to discover it was a waste of GPU time.

Context: $ARGUMENTS

Constants

• WANDB_ENTITY and WANDB_PROJECT: read from CLAUDE.md or passed as argument (format: entity/project/run_id)
• CHECK_INTERVAL: starts at 10 minutes, then gradually increases if consistently healthy: 10 min → 20 min → 30 min → 60 min (cap)
• REVIEWER_MODEL = gpt-5.4 — used via Codex MCP for ambiguous cases only

When to Use

• After training is confirmed running (session alive, loss decreasing for first few steps)
• Set up via CronCreate to fire periodically during training
• This skill checks training QUALITY, not process HEALTH. Process health (session alive, GPU utilization) is watchdog.py's job.

Workflow

Step 1: Read WandB Metrics

import wandb
api = wandb.Api()
run = api.run("<entity>/<project>/<run_id>")
history = run.history()

If WandB is unreachable (API error, network issue), fall back to reading the log file directly via SSH:

ssh server "tail -100 /path/to/training.log"

Check these signals:

• Loss trend: Is training loss decreasing over the last N steps?
• Eval metrics: Are evaluation metrics improving (or at least not degrading)?
• NaN / Inf: Any NaN or Inf values in loss or gradients?
• Spikes: Sudden large jumps in loss (>10x normal variance)?
• Learning rate: Is the schedule behaving as expected?
• Gradient norm: Exploding or vanishing?

Step 2: Judgment

| Signal | Judgment | Action | |--------|----------|--------| | NaN/Inf in loss | Clearly bad | Stop training, investigate | | Loss diverging (increasing for >N steps) | Clearly bad | Stop training, investigate | | Eval metrics significantly worse than baseline | Clearly bad | Stop training, investigate | | Loss decreasing, metrics improving | Clearly fine | Continue, increase check interval | | Loss flat but not diverging | Unsure | → Step 3 (Codex judgment) | | Metrics noisy, can't tell trend | Unsure | → Step 3 (Codex judgment) | | Slightly worse than baseline but still early | Unsure | → Step 3 (Codex judgment) |

Step 3: Codex Judgment (only when unsure)

Only escalate to Codex when the signal is ambiguous. For clearly good or clearly bad signals, act directly.

mcp__codex__codex:
  config: {"model_reasoning_effort": "high"}
  prompt: |
    TRAINING HEALTH CHECK — need your judgment on ambiguous metrics.

    Run: <entity>/<project>/<run_id>
    Current epoch/step: X / Y total
    Training loss (last 10 checkpoints): [values]
    Eval metrics (last 3 evals): [values]
    Baseline reference: [numbers from paper/reproduction]

    What I'm unsure about: [specific concern]

    Please respond with exactly one of:
    - STOP: clearly problematic, should kill training
    - CONTINUE: looks fine, check again next interval
    - WAIT: not enough data to judge, check again sooner

Step 4: Act

| Decision | Action | |----------|--------| | Stop | Kill the training session. Save the WandB run URL, key metrics, and reason for stopping. Log to project notes for debugging. | | Continue | Do nothing. Will be invoked again at next interval (increase interval if consistently healthy). | | Wait | Do nothing but keep the current short interval (don't increase). |

Integration with Watchdog

Training-check and watchdog.py operate at different levels:

| Layer | Tool | What it checks | Frequency | |-------|------|----------------|-----------| | Process health | watchdog.py | Session alive? GPU active? | Every 60s (continuous) | | Training quality | training-check | Loss trend? Metrics improving? | Every 10-60 min (periodic) |

Use both together:

• Watchdog catches crashes and idle GPUs immediately
• Training-check catches subtle quality issues (loss plateau, metric degradation)

Rules

• Do not stop training on first sign of noise — some loss spikes are normal. Look at trends over multiple checkpoints.
• When stopping training, always save the WandB run URL and key metrics as evidence.
• If both WandB and log files are unreachable, report the connectivity issue and try again next interval. Do not assume training is broken.
• Gradually increase check interval when healthy (10 → 20 → 30 → 60 min). Reset to 10 min after any anomaly.
• This skill is meant to be automated via CronCreate — do not ask the user whether to set it up. Just set it.

CronCreate Setup Example

After training is confirmed stable:
  CronCreate (recurring, every 10 minutes initially):
    "Run /training-check for wandb run <entity>/<project>/<run_id>"

As the check interval increases, delete the old CronCreate job and create a new one with the longer interval.