mirror of
https://github.com/Wan-Video/Wan2.1.git
synced 2025-11-04 06:15:17 +00:00
119 lines
4.4 KiB
Bash
Executable File
119 lines
4.4 KiB
Bash
Executable File
#!/usr/bin/env bash
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export HOME=/home/user
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export PYTHONUNBUFFERED=1
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export HF_HOME=/home/user/.cache/huggingface
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export OMP_NUM_THREADS=$(nproc)
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export MKL_NUM_THREADS=$(nproc)
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export OPENBLAS_NUM_THREADS=$(nproc)
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export NUMEXPR_NUM_THREADS=$(nproc)
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export TORCH_ALLOW_TF32_CUBLAS=1
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export TORCH_ALLOW_TF32_CUDNN=1
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# Disable audio warnings in Docker
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export SDL_AUDIODRIVER=dummy
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export PULSE_RUNTIME_PATH=/tmp/pulse-runtime
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# ═══════════════════════════ CUDA DEBUG CHECKS ═══════════════════════════
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echo "🔍 CUDA Environment Debug Information:"
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echo "═══════════════════════════════════════════════════════════════════════"
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# Check CUDA driver on host (if accessible)
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if command -v nvidia-smi >/dev/null 2>&1; then
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echo "✅ nvidia-smi available"
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echo "📊 GPU Information:"
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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"
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echo "🏃 Running Processes:"
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nvidia-smi --query-compute-apps=pid,name,used_memory --format=csv,noheader,nounits 2>/dev/null || echo "ℹ️ No running CUDA processes"
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else
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echo "❌ nvidia-smi not available in container"
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fi
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# Check CUDA runtime libraries
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echo ""
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echo "🔧 CUDA Runtime Check:"
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if ls /usr/local/cuda*/lib*/libcudart.so* >/dev/null 2>&1; then
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echo "✅ CUDA runtime libraries found:"
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ls /usr/local/cuda*/lib*/libcudart.so* 2>/dev/null
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else
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echo "❌ CUDA runtime libraries not found"
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fi
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# Check CUDA devices
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echo ""
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echo "🖥️ CUDA Device Files:"
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if ls /dev/nvidia* >/dev/null 2>&1; then
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echo "✅ NVIDIA device files found:"
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ls -la /dev/nvidia* 2>/dev/null
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else
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echo "❌ No NVIDIA device files found - Docker may not have GPU access"
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fi
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# Check CUDA environment variables
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echo ""
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echo "🌍 CUDA Environment Variables:"
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echo " CUDA_HOME: ${CUDA_HOME:-not set}"
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echo " CUDA_ROOT: ${CUDA_ROOT:-not set}"
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echo " CUDA_PATH: ${CUDA_PATH:-not set}"
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echo " LD_LIBRARY_PATH: ${LD_LIBRARY_PATH:-not set}"
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echo " TORCH_CUDA_ARCH_LIST: ${TORCH_CUDA_ARCH_LIST:-not set}"
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echo " CUDA_VISIBLE_DEVICES: ${CUDA_VISIBLE_DEVICES:-not set}"
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# Check PyTorch CUDA availability
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echo ""
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echo "🐍 PyTorch CUDA Check:"
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python3 -c "
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import sys
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try:
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import torch
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print('✅ PyTorch imported successfully')
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print(f' Version: {torch.__version__}')
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print(f' CUDA available: {torch.cuda.is_available()}')
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if torch.cuda.is_available():
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print(f' CUDA version: {torch.version.cuda}')
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print(f' cuDNN version: {torch.backends.cudnn.version()}')
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print(f' Device count: {torch.cuda.device_count()}')
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for i in range(torch.cuda.device_count()):
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props = torch.cuda.get_device_properties(i)
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print(f' Device {i}: {props.name} (SM {props.major}.{props.minor}, {props.total_memory//1024//1024}MB)')
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else:
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print('❌ CUDA not available to PyTorch')
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print(' This could mean:')
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print(' - CUDA runtime not properly installed')
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print(' - GPU not accessible to container')
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print(' - Driver/runtime version mismatch')
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except ImportError as e:
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print(f'❌ Failed to import PyTorch: {e}')
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except Exception as e:
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print(f'❌ PyTorch CUDA check failed: {e}')
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" 2>&1
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# Check for common CUDA issues
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echo ""
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echo "🩺 Common Issue Diagnostics:"
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# Check if running with proper Docker flags
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if [ ! -e /dev/nvidia0 ] && [ ! -e /dev/nvidiactl ]; then
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echo "❌ No NVIDIA device nodes - container likely missing --gpus all or --runtime=nvidia"
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fi
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# Check CUDA library paths
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if [ -z "$LD_LIBRARY_PATH" ] || ! echo "$LD_LIBRARY_PATH" | grep -q cuda; then
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echo "⚠️ LD_LIBRARY_PATH may not include CUDA libraries"
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fi
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# Check permissions on device files
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if ls /dev/nvidia* >/dev/null 2>&1; then
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if ! ls -la /dev/nvidia* | grep -q "rw-rw-rw-\|rw-r--r--"; then
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echo "⚠️ NVIDIA device files may have restrictive permissions"
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fi
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fi
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echo "═══════════════════════════════════════════════════════════════════════"
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echo "🚀 Starting application..."
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echo ""
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exec su -p user -c "python3 wgp.py --listen $*"
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