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+FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
+
+# Set environment variables
+ENV DEBIAN_FRONTEND=noninteractive
+ENV PYTHONUNBUFFERED=1
+ENV HF_HOME=/app/.cache/huggingface
+ENV TRANSFORMERS_CACHE=/app/.cache/huggingface/transformers
+ENV PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:128
+
+# Create necessary directories with proper permissions
+RUN mkdir -p /app/.cache/huggingface/transformers && \
+    chmod -R 777 /app
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    git \
+    curl \
+    ca-certificates \
+    cmake \
+    python3-pip \
+    python3-dev \
+    ninja-build \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create a working directory
+WORKDIR /app
+
+# Install core requirements
+COPY requirements.txt .
+RUN pip3 install --no-cache-dir --upgrade pip && \
+    pip3 install --no-cache-dir -r requirements.txt
+
+# Install basic dependencies specifically for InternViT
+RUN pip3 install --no-cache-dir \
+    transformers==4.37.2 \
+    timm==0.9.11 \
+    accelerate==0.30.0 \
+    safetensors==0.4.1 \
+    einops
+
+# Install flash-attn with specific CUDA compatibility
+# Flash Attention requires CUDA Toolkit, so we install a version that's pre-built
+RUN pip3 install --no-cache-dir \
+    ninja \
+    packaging \
+    "flash-attn<2.0.0" --no-build-isolation
+
+# Copy the application
+COPY simple_internvit_test.py .
+
+# Add GPU diagnostic script
+RUN echo '#!/bin/bash \n\
+echo "Starting GPU diagnostics..." \n\
+echo "===== System Information =====" \n\
+python3 -c "import sys; print(f\"Python version: {sys.version}\")" \n\
+python3 -c "import torch; print(f\"PyTorch version: {torch.__version__}\")" \n\
+echo "\n===== CUDA Information =====" \n\
+python3 -c "import torch; print(f\"CUDA available: {torch.cuda.is_available()}\")" \n\
+if [ $(python3 -c "import torch; print(torch.cuda.is_available())") = "True" ]; then \n\
+    python3 -c "import torch; print(f\"CUDA version: {torch.version.cuda}\")" \n\
+    python3 -c "import torch; print(f\"GPU count: {torch.cuda.device_count()}\")" \n\
+    python3 -c "import torch; for i in range(torch.cuda.device_count()): print(f\"GPU {i}: {torch.cuda.get_device_name(i)}\")" \n\
+    python3 -c "import torch; print(f\"Allocated memory: {torch.cuda.memory_allocated() / 1024 / 1024:.2f} MB\")" \n\
+    python3 -c "import torch; print(f\"Reserved memory: {torch.cuda.memory_reserved() / 1024 / 1024:.2f} MB\")" \n\
+fi \n\
+echo "\n===== Package Information =====" \n\
+pip3 list | grep -E "transformers|einops|torch|timm|flash|accelerate|safetensors" \n\
+echo "\n===== Testing Simple CUDA Operation =====" \n\
+python3 -c "import torch; a = torch.randn(1000, 1000).cuda(); b = torch.randn(1000, 1000).cuda(); t0 = torch.cuda.Event(enable_timing=True); t1 = torch.cuda.Event(enable_timing=True); t0.record(); c = torch.matmul(a, b); t1.record(); torch.cuda.synchronize(); print(f\"Matrix multiplication completed in {t0.elapsed_time(t1):.2f} ms\")" \n\
+echo "\n===== NVIDIA System Information =====" \n\
+if command -v nvidia-smi &> /dev/null; then \n\
+    nvidia-smi \n\
+else \n\
+    echo "nvidia-smi not found" \n\
+fi \n\
+echo "\n===== Starting Application =====" \n\
+exec "$@"' > /entrypoint.sh && \
+chmod +x /entrypoint.sh
+
+# Expose port 7860 for Gradio
+EXPOSE 7860
+
+# Use our enhanced diagnostic entrypoint script
+ENTRYPOINT ["/entrypoint.sh"]
+
+# Start the application
+CMD ["python3", "simple_internvit_test.py"]