add more checks + logging + upgrade peft

entrypoint.sh

@@ -15,4 +15,104 @@ export TORCH_ALLOW_TF32_CUDNN=1
 export SDL_AUDIODRIVER=dummy
 export PULSE_RUNTIME_PATH=/tmp/pulse-runtime
 
+# ═══════════════════════════ CUDA DEBUG CHECKS ═══════════════════════════
+
+echo "🔍 CUDA Environment Debug Information:"
+echo "═══════════════════════════════════════════════════════════════════════"
+
+# Check CUDA driver on host (if accessible)
+if command -v nvidia-smi >/dev/null 2>&1; then
+    echo "✅ nvidia-smi available"
+    echo "📊 GPU Information:"
+    nvidia-smi --query-gpu=name,driver_version,memory.total,memory.free --format=csv,noheader,nounits 2>/dev/null || echo "❌ nvidia-smi failed to query GPU"
+    echo "🏃 Running Processes:"
+    nvidia-smi --query-compute-apps=pid,name,used_memory --format=csv,noheader,nounits 2>/dev/null || echo "ℹ️  No running CUDA processes"
+else
+    echo "❌ nvidia-smi not available in container"
+fi
+
+# Check CUDA runtime libraries
+echo ""
+echo "🔧 CUDA Runtime Check:"
+if ls /usr/local/cuda*/lib*/libcudart.so* >/dev/null 2>&1; then
+    echo "✅ CUDA runtime libraries found:"
+    ls /usr/local/cuda*/lib*/libcudart.so* 2>/dev/null
+else
+    echo "❌ CUDA runtime libraries not found"
+fi
+
+# Check CUDA devices
+echo ""
+echo "🖥️  CUDA Device Files:"
+if ls /dev/nvidia* >/dev/null 2>&1; then
+    echo "✅ NVIDIA device files found:"
+    ls -la /dev/nvidia* 2>/dev/null
+else
+    echo "❌ No NVIDIA device files found - Docker may not have GPU access"
+fi
+
+# Check CUDA environment variables
+echo ""
+echo "🌍 CUDA Environment Variables:"
+echo "   CUDA_HOME: ${CUDA_HOME:-not set}"
+echo "   CUDA_ROOT: ${CUDA_ROOT:-not set}"
+echo "   CUDA_PATH: ${CUDA_PATH:-not set}"
+echo "   LD_LIBRARY_PATH: ${LD_LIBRARY_PATH:-not set}"
+echo "   TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST:-not set}"
+echo "   CUDA_VISIBLE_DEVICES: ${CUDA_VISIBLE_DEVICES:-not set}"
+
+# Check PyTorch CUDA availability
+echo ""
+echo "🐍 PyTorch CUDA Check:"
+python3 -c "
+import sys
+try:
+    import torch
+    print('✅ PyTorch imported successfully')
+    print(f'   Version: {torch.__version__}')
+    print(f'   CUDA available: {torch.cuda.is_available()}')
+    if torch.cuda.is_available():
+        print(f'   CUDA version: {torch.version.cuda}')
+        print(f'   cuDNN version: {torch.backends.cudnn.version()}')
+        print(f'   Device count: {torch.cuda.device_count()}')
+        for i in range(torch.cuda.device_count()):
+            props = torch.cuda.get_device_properties(i)
+            print(f'   Device {i}: {props.name} (SM {props.major}.{props.minor}, {props.total_memory//1024//1024}MB)')
+    else:
+        print('❌ CUDA not available to PyTorch')
+        print('   This could mean:')
+        print('   - CUDA runtime not properly installed')
+        print('   - GPU not accessible to container')
+        print('   - Driver/runtime version mismatch')
+except ImportError as e:
+    print(f'❌ Failed to import PyTorch: {e}')
+except Exception as e:
+    print(f'❌ PyTorch CUDA check failed: {e}')
+" 2>&1
+
+# Check for common CUDA issues
+echo ""
+echo "🩺 Common Issue Diagnostics:"
+
+# Check if running with proper Docker flags
+if [ ! -e /dev/nvidia0 ] && [ ! -e /dev/nvidiactl ]; then
+    echo "❌ No NVIDIA device nodes - container likely missing --gpus all or --runtime=nvidia"
+fi
+
+# Check CUDA library paths
+if [ -z "$LD_LIBRARY_PATH" ] || ! echo "$LD_LIBRARY_PATH" | grep -q cuda; then
+    echo "⚠️  LD_LIBRARY_PATH may not include CUDA libraries"
+fi
+
+# Check permissions on device files
+if ls /dev/nvidia* >/dev/null 2>&1; then
+    if ! ls -la /dev/nvidia* | grep -q "rw-rw-rw-\|rw-r--r--"; then
+        echo "⚠️  NVIDIA device files may have restrictive permissions"
+    fi
+fi
+
+echo "═══════════════════════════════════════════════════════════════════════"
+echo "🚀 Starting application..."
+echo ""
+
 exec su -p user -c "python3 wgp.py --listen $*"

requirements.txt

@@ -18,7 +18,7 @@ numpy>=1.23.5,<2
 einops
 moviepy==1.0.3
 mmgp==3.5.1
-peft==0.15.0
+peft==0.17.0
 mutagen
 pydantic==2.10.6
 decord

run-docker-cuda-deb.sh

@@ -114,6 +114,25 @@ map_gpu_to_profile() {
 
 # ───────────────────────── main ────────────────────────────
 
+echo "🔧 NVIDIA CUDA Setup Check:"
+
+# NVIDIA driver check
+if command -v nvidia-smi &>/dev/null; then
+    DRIVER_VERSION=$(nvidia-smi --query-gpu=driver_version --format=csv,noheader,nounits | head -1)
+    echo "✅ NVIDIA Driver: $DRIVER_VERSION"
+    
+    # Quick CUDA 12.4 compatibility check
+    if [[ "$DRIVER_VERSION" =~ ^([0-9]+) ]]; then
+        MAJOR=${BASH_REMATCH[1]}
+        if [ "$MAJOR" -lt 520 ]; then
+            echo "⚠️  Driver $DRIVER_VERSION may not support CUDA 12.4 (need 520+)"
+        fi
+    fi
+else
+    echo "❌ nvidia-smi not found - no NVIDIA drivers"
+    exit 1
+fi
+
 GPU_NAME=$(detect_gpu_name)
 echo "🔍 Detected GPU: $GPU_NAME"
 
@@ -156,6 +175,14 @@ else
     echo "✅ NVIDIA Docker runtime found."
 fi
 
+# Quick NVIDIA runtime test
+echo "🧪 Testing NVIDIA runtime..."
+if timeout 15s docker run --rm --gpus all --runtime=nvidia nvidia/cuda:12.4-runtime-ubuntu22.04 nvidia-smi >/dev/null 2>&1; then
+    echo "✅ NVIDIA runtime working"
+else
+    echo "❌ NVIDIA runtime test failed - check driver/runtime compatibility"
+fi
+
 # Prepare cache dirs & volume mounts
 cache_dirs=(numba matplotlib huggingface torch)
 cache_mounts=()