heimdall

Pause/Resume Training Jobs - Implementation Complete

Feature: Pause and resume ML training jobs at epoch boundaries
Status: ✅ IMPLEMENTATION COMPLETE - Ready for Testing
Date: 2025-11-02

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📋 Summary

Successfully implemented pause/resume functionality for training jobs in the Heimdall ML pipeline. Training jobs can now be temporarily paused at epoch boundaries and later resumed from the exact state where they left off, preserving all model, optimizer, and scheduler state.

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✅ What Was Implemented

1. Database Layer ✅

File: db/migrations/020-add-pause-resume-training.sql

• Added pause_checkpoint_path column to store checkpoint location
• Updated status constraint to include 'paused' state
• Created index for efficient querying of paused jobs
• Status: Migration applied to database successfully

2. Backend Models ✅

File: services/backend/src/models/training.py:25

• Added PAUSED = "paused" to TrainingStatus enum
• Integrated with existing training job models
• Status: Complete

3. Training Task Logic ✅

File: services/training/src/tasks/training_task.py

Resume Logic (lines 177-213):

• Check for pause checkpoint at training start
• Download checkpoint from MinIO
• Restore model, optimizer, scheduler states
• Resume from resume_epoch + 1
• Clear pause checkpoint path after successful load

Pause Detection (lines 452-503):

• Check database status after each epoch
• When status is ‘paused’, save full training state:
  • Model weights
  • Optimizer state (momentum, learning rate)
  • Scheduler state
  • Best model tracking (best_val_loss, best_epoch)
  • Early stopping counter
• Upload checkpoint to MinIO
• Update database with checkpoint path
• Gracefully exit training loop

Status: Complete with full state preservation

4. Backend API Endpoints ✅

File: services/backend/src/routers/training.py

POST /v1/training/jobs/{job_id}/pause (lines 424-501):

• Validates job is in ‘running’ state
• Prevents pausing synthetic data generation jobs
• Sets status to ‘paused’ in database
• Broadcasts WebSocket update to connected clients
• Returns informative message about epoch boundary behavior

POST /v1/training/jobs/{job_id}/resume (lines 504-594):

• Validates job is in ‘paused’ state
• Verifies pause checkpoint exists
• Queues new Celery task with same job_id
• Sets status to ‘queued’, clears pause checkpoint path
• Broadcasts WebSocket update
• Returns new task ID and resume status

Status: Complete with error handling and WebSocket integration

5. Frontend API Service ✅

File: frontend/src/services/api/training.ts

• Updated TrainingJob type to include 'paused' status (line 19)
• Added pauseTrainingJob(jobId) function (lines 122-125)
• Added resumeTrainingJob(jobId) function (lines 127-130)
• Status: Complete

6. Frontend UI ✅

File: frontend/src/pages/TrainingDashboard.tsx

Event Handlers (lines 246-268):

• handlePauseJob() - Confirms with user, calls pause API
• handleResumeJob() - Confirms with user, calls resume API

Status Badge (line 375):

• Added paused: { variant: 'dark', icon: <Pause size={14} /> }

Action Buttons (lines 560-590):

• Pause button: Shows for running training jobs only (excludes synthetic data)
• Resume button: Shows only for paused jobs
• Delete button: Disabled for paused jobs
• Cancel button: Hidden for paused jobs

Status: Complete with proper visibility logic

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🎯 Key Design Decisions

1. Pause Timing: At epoch boundary (graceful, not mid-batch)
   • Prevents data corruption
   • Ensures clean state preservation
   • User receives informative message about timing
2. Scope: Training jobs only (excludes synthetic data generation)
   • Synthetic data jobs have total_epochs = 0
   • These jobs are typically faster and don’t benefit from pause/resume
3. Checkpoint Strategy: Separate pause checkpoint from best model checkpoint
   • Best model: checkpoints/{job_id}/best_model.pth
   • Pause checkpoint: checkpoints/{job_id}/pause_checkpoint.pth
   • Prevents confusion between model checkpoints and pause state
4. Resume Behavior: Creates new Celery task with same job_id
   • Allows clean restart of worker process
   • Loads full training state from checkpoint
   • Preserves job history and metrics
5. State Preservation: Full training state saved in pause checkpoint
   • Model weights
   • Optimizer state (Adam momentum, etc.)
   • Learning rate scheduler state
   • Best validation loss and epoch tracking
   • Early stopping patience counter
   • Training configuration

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📁 Files Modified

1. ✅ db/migrations/020-add-pause-resume-training.sql - New migration
2. ✅ services/backend/src/models/training.py - Added PAUSED enum
3. ✅ services/training/src/tasks/training_task.py - Resume + pause logic
4. ✅ services/backend/src/routers/training.py - Pause/resume endpoints
5. ✅ frontend/src/services/api/training.ts - Frontend API functions
6. ✅ frontend/src/pages/TrainingDashboard.tsx - UI buttons and status

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🧪 Testing

Automated Test Script

File: scripts/test_pause_resume.py

Comprehensive test script that:

1. Creates a training job
2. Waits for it to start running
3. Pauses at epoch 2+
4. Verifies pause checkpoint
5. Resumes training
6. Verifies continuation from correct epoch
7. Waits for completion

Run: python scripts/test_pause_resume.py

Manual Test Plan

File: docs/TESTING.md

Comprehensive test plan covering:

• ✅ Basic pause/resume flow
• ✅ Pause at different epochs
• ✅ Multiple pause/resume cycles
• ✅ Edge cases (invalid states, missing checkpoints, etc.)
• ✅ UI/UX verification
• ✅ Training state preservation
• ✅ Performance and timing

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🚀 Next Steps

1. Apply Migration (✅ DONE)

docker exec -i heimdall-postgres psql -U heimdall_user -d heimdall \
  < db/migrations/020-add-pause-resume-training.sql

2. Restart Services (✅ DONE)

docker compose restart backend training

3. Run Tests

# Automated test
python scripts/test_pause_resume.py

# Manual testing
# Follow test plan in docs/TESTING.md

4. Verify in UI

• Navigate to http://localhost:3000/training
• Create a training job
• Test pause/resume buttons
• Verify WebSocket real-time updates

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📊 Implementation Status

Component	Status	Notes
Database Schema	✅ Complete	Migration applied
Backend Models	✅ Complete	PAUSED status enum added
Training Task	✅ Complete	Resume & pause logic implemented
Backend API	✅ Complete	Endpoints with validation
Frontend API	✅ Complete	TypeScript types & functions
Frontend UI	✅ Complete	Buttons, handlers, status badge
Documentation	✅ Complete	Test plan & implementation docs
Testing	⏳ Pending	Ready to test

Overall Progress: 7/8 complete (87.5%)

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🎓 Implementation Highlights

Robust State Management

# Full training state preserved in pause checkpoint
pause_checkpoint = {
    'epoch': epoch,
    'model_state_dict': model.state_dict(),
    'optimizer_state_dict': optimizer.state_dict(),
    'scheduler_state_dict': scheduler.state_dict(),
    'best_val_loss': best_val_loss,
    'best_epoch': best_epoch,
    'patience_counter': patience_counter,
    'train_loss': train_loss,
    'val_loss': val_loss,
    'val_rmse': val_rmse,
    'config': config
}

Clean Resume Logic

# Resume from pause checkpoint
if pause_checkpoint_path:
    checkpoint = torch.load(checkpoint_buffer)
    model.load_state_dict(checkpoint['model_state_dict'])
    optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
    scheduler.load_state_dict(checkpoint['scheduler_state_dict'])
    resume_epoch = checkpoint['epoch']
    best_val_loss = checkpoint.get('best_val_loss', float('inf'))
    # ... restore all state
    
# Training continues from next epoch
for epoch in range(resume_epoch + 1, epochs + 1):
    # ... training loop

Graceful Pause Detection

# Check for pause request after each epoch
with db_manager.get_session() as session:
    status_result = session.execute(
        text("SELECT status FROM heimdall.training_jobs WHERE id = :job_id"),
        {"job_id": job_id}
    ).fetchone()
    
if status_result[0] == 'paused':
    # Save checkpoint and exit gracefully
    save_pause_checkpoint()
    return {"status": "paused", "paused_at_epoch": epoch}

User-Friendly UI

// Pause button only for running training jobs (not synthetic data)
{job.status === 'running' && !isSyntheticDataJob(job) && (
  <Button onClick={() => handlePauseJob(job.id)}>
    <Pause size={14} />
  </Button>
)}

// Resume button only for paused jobs
{job.status === 'paused' && (
  <Button onClick={() => handleResumeJob(job.id)}>
    <Play size={14} />
  </Button>
)}

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🐛 Known Limitations

1. Pause Timing: Not immediate, happens at epoch boundary
   • This is intentional to preserve data integrity
   • User is informed via message
2. Scope: Only training jobs, not synthetic data generation
   • Synthetic data jobs are typically fast
   • Would add unnecessary complexity
3. Single Pause Checkpoint: Only one pause checkpoint per job
   • Previous pause checkpoints are overwritten
   • This is sufficient for the use case

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📚 References

• Training Architecture
• API Documentation
• Development Guide
• Test Plan

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Implementation By: OpenCode AI Assistant
Reviewed By: _____
Tested By: _____
Date: 2025-11-02