Monitor Training

Monitor Training — Autonomous Training Pipeline

Set up cron-based monitoring for long-running YOLO training sessions. Automatically detects completion, reports results, and continues to the next pipeline phase.

When to Use

• After launching a training run that will take hours
• When you want to chain multiple pipeline phases autonomously (train → analyze → clean → retrain → tune → export)
• To monitor training progress without manual polling

Workflow

1. Identify Training State

Determine what's currently running:

• Check for active GPU processes: nvidia-smi --query-compute-apps=pid,used_memory --format=csv,noheader
• Check for results CSV: tail -5 /results.csv
• Check background task status if task ID is known

2. Create Monitoring Cron Job

Use CronCreate with a 5-minute interval to poll training status:

CronCreate:
  cron: "*/5 * * * *"
  recurring: true
  prompt: 

The monitoring prompt should include:

• Where to check: Path to results.csv and/or background task ID
• What to report: Current epoch, mAP50, mAP50-95, training losses
• Completion criteria: Task finished, early stopping triggered, or target metric reached
• Next phase actions: Detailed steps to execute when training completes

3. Monitor Prompt Template

Build the cron prompt with these sections:

1. CHECK STATUS
   - Read results.csv (tail -5) for latest metrics
   - Check if training process is still running
   - Report: epoch, mAP50, mAP50-95, whether still active

2. ON COMPLETION — Execute next phase:
   [Phase-specific instructions]

3. AFTER NEXT PHASE — Chain to following phase:
   - Delete current cron job (CronDelete)
   - Create new cron job for the next long-running phase

4. PLAN REFERENCE
   - Read plan file for full pipeline context

4. Pipeline Chaining Pattern

Each cron job monitors one phase and launches the next:

Cron 1: Monitor baseline training
  → On complete: run analysis + dataset cleanup
  → Launch retrain
  → Delete self, create Cron 2

Cron 2: Monitor retrain
  → On complete: launch model.tune()
  → Delete self, create Cron 3

Cron 3: Monitor tune
  → On complete: launch final training with best params
  → Delete self, create Cron 4

Cron 4: Monitor final training
  → On complete: export model, generate final report
  → Delete self

5. Completion Actions

When a training phase completes:

1. Report final metrics — epoch, per-class mAP, precision, recall
2. Save best model — copy best.pt to a named location
3. Run per-class validation — use a free GPU for detailed evaluation
4. Update plan file — mark phase complete, record metrics
5. Update tasks — mark current task complete, create next
6. Launch next phase — start the next pipeline step
7. Create new monitor — set up cron for the new phase

Guidelines

• Cron interval: 5 minutes is good for training. Use 2 minutes for shorter operations like analysis.
• GPU awareness: Before running validation or analysis, check GPU memory. Use a different GPU than the one training, or wait for training to finish.
• Error handling: If a training crashes (OOM, data error), report the error and stop — don't auto-retry without understanding why.
• Plan file: Always reference the plan file for pipeline context. Update it as phases complete.
• Session lifetime: Cron jobs expire after 7 days or when the session ends. For multi-day pipelines, document the current state in the plan file so a new session can resume.
• Idempotency: The cron prompt may fire multiple times while a phase is still running. Only trigger next-phase actions on actual completion, not on intermediate checks.

Example

User: "Lance le training et surveille-le automatiquement"

1. Launch training in background
2. Create cron:
   CronCreate("*/5 * * * *", "Check training at /results.csv.
     If still running: report epoch and metrics.
     If complete: run validation, save model, launch next phase...")
3. Training runs autonomously
4. Cron detects completion → runs analysis → launches retrain → creates new cron
5. Pipeline continues without user intervention

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: MacroMan5
• Source: MacroMan5/autotrain-yolo
• License: MIT

Install and usage instructions live in the source repository linked above.