Update Dockerfile

Dockerfile

@@ -20,22 +20,37 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
     && apt-get clean \
     && rm -rf /var/lib/apt/lists/*
 
-# Python environment configuration
 RUN ln -sf /usr/bin/python3 /usr/bin/python && \
     curl -sS https://bootstrap.pypa.io/get-pip.py | python3 && \
     python3 -m pip install --no-cache-dir pip==23.3.1 setuptools==69.0.3 wheel==0.42.0
 
+# Application directory structure
 WORKDIR /app
+RUN mkdir -p /app/cache /app/src && \
+    chmod 777 /app/cache
 
-# Copy requirements first to leverage Docker cache
-COPY requirements.txt .
+# Dependencies installation (two-stage for better caching)
+COPY requirements.txt /app/src/
+WORKDIR /app/src
+RUN python3 -m pip install --no-cache-dir -r requirements.txt
 
-# Install PyTorch first (separately to avoid conflicts)
-RUN pip3 install --no-cache-dir torch==2.1.2 torchvision==0.16.2 --index-url https://download.pytorch.org/whl/cu121
+# Application code
+COPY . /app/src/
 
-# Then install other requirements
-RUN pip3 install --no-cache-dir -r requirements.txt
+# Runtime environment
+ENV MODEL_PATH=/app/cache \
+    PYTHONPATH=/app/src:$PYTHONPATH \
+    OMP_NUM_THREADS=1 \
+    MKL_NUM_THREADS=1
 
-# Create directories after all installations
-RUN mkdir -p /app/cache /app/src && \
-    chmod 777 /app/cache
+# # GPU verification with comprehensive diagnostics
+RUN python3 -c 'import torch; \
+    assert torch.cuda.is_available(), "CUDA unavailable"; \
+    print(f"PyTorch: {torch.__version__}"); \
+    print(f"CUDA: {torch.version.cuda}"); \
+    print(f"GPU: {torch.cuda.get_device_name()}"); \
+    print(f"Arch: {torch.cuda.get_device_capability()}");'
+
+EXPOSE 8000
+
+CMD ["python", "server.py"]