HF_SPACES_GPU_FIX.md

🚀 HF Spaces GPU Acceleration Fix

❌ Problem Identified:

Your T4 GPU wasn't being used because:

1. Dockerfile disabled CUDA: ENV CUDA_VISIBLE_DEVICES=""
2. Environment variable issues: OMP_NUM_THREADS causing warnings
3. App running on CPU: Despite having T4 GPU hardware

✅ Complete Fix Applied:

1. Dockerfile Changes

# REMOVED this line that was disabling GPU:
# ENV CUDA_VISIBLE_DEVICES=""  

# Fixed environment variables:
ENV OMP_NUM_THREADS=2
ENV MKL_NUM_THREADS=2

2. App.py Improvements

• ✅ Fixed OMP_NUM_THREADS early: Set before any imports
• ✅ Improved GPU detection: Better logging and detection
• ✅ Cache directories: Moved setup to very beginning

3. Environment Variable Priority

Environment variables are now set in this order:

1. Dockerfile - Base container settings
2. app.py top - Python-level fixes (before imports)
3. HF Spaces - Runtime overrides

🎯 Expected Results After Fix:

Before (CPU mode):

INFO:app:Using device: cpu
INFO:app:CUDA not available, using CPU - this is normal for HF Spaces free tier
CPU 56%
GPU 0%
GPU VRAM 0/16 GB

After (GPU mode):

INFO:app:Using device: cuda
INFO:app:CUDA available: True
INFO:app:GPU device count: 1
INFO:app:Current GPU: Tesla T4
INFO:app:GPU memory: 15.1 GB
INFO:app:🚀 GPU acceleration enabled!

Performance Improvement:

• CPU usage: Should drop to ~20-30%
• GPU usage: Should show 10-50% during AI inference
• GPU VRAM: Should show 2-4GB usage
• AI FPS: Should increase from ~2 FPS to 10+ FPS

📋 Deployment Steps:

1. Commit and push changes:

   git add .
   git commit -m "Enable GPU acceleration for HF Spaces T4"
   git push
   

2. Wait for rebuild (HF Spaces will restart automatically)

3. Check new logs for GPU detection:

   INFO:app:🚀 GPU acceleration enabled!
   

4. Monitor system stats:

   • GPU usage should now show activity
   • GPU VRAM should show memory allocation
   • Overall performance should be much faster

🔍 Debugging Commands:

Check CUDA in container:

import torch
print(f"CUDA available: {torch.cuda.is_available()}")
print(f"GPU count: {torch.cuda.device_count()}")
if torch.cuda.is_available():
    print(f"GPU name: {torch.cuda.get_device_name(0)}")

Check environment variables:

import os
print(f"CUDA_VISIBLE_DEVICES: {os.environ.get('CUDA_VISIBLE_DEVICES', 'Not set')}")
print(f"OMP_NUM_THREADS: {os.environ.get('OMP_NUM_THREADS')}")

🚨 If GPU Still Not Working:

1. Verify HF Spaces Hardware:

• Check your Space settings
• Ensure "T4 small" or "T4 medium" is selected
• Free tier doesn't have GPU access

2. Check Container Logs:

Look for these messages:

• ✅ "🚀 GPU acceleration enabled!"
• ❌ "CUDA not available"

3. Alternative: Force GPU Detection

If needed, add this debug code to app.py:

# Debug GPU detection
logger.info(f"Environment CUDA_VISIBLE_DEVICES: {os.environ.get('CUDA_VISIBLE_DEVICES', 'Not set')}")
logger.info(f"PyTorch CUDA compiled: {torch.version.cuda}")
logger.info(f"PyTorch version: {torch.__version__}")

⚡ Performance Optimization Tips:

For T4 GPU:

1. Enable model compilation (optional):

   # Set environment variable in HF Spaces settings:
   ENABLE_TORCH_COMPILE=1
   

2. Increase AI FPS (if needed):

   # In app.py, line ~86:
   self.ai_fps = 15  # Increase from 10 to 15
   

3. Monitor GPU memory:

   • T4 has 16GB VRAM
   • App should use 2-4GB
   • Leave headroom for other processes

🎮 Expected User Experience:

1. Faster loading: Models load to GPU memory
2. Responsive gameplay: AI inference runs at 10+ FPS
3. Smoother visuals: Display updates without lag
4. Better AI performance: GPU acceleration improves model inference

Your HF Spaces deployment should now fully utilize the T4 GPU! 🚀

📊 Monitor These Metrics:

• GPU Utilization: 10-50% during gameplay
• GPU Memory: 2-4GB allocated
• AI FPS: 10-15 FPS (displayed in web interface)
• CPU Usage: Should decrease to 20-30%

The game should feel much more responsive now! 🎉