feat: add Dockerfile with CPU-only torch, .dockerignore, and requirements-cpu.txt
- Multi-stage Dockerfile with CPU-only PyTorch - .dockerignore to exclude build artifacts - requirements-cpu.txt for CPU-only dependencies - Health check endpoint for container monitoring
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.dockerignore
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.dockerignore
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__pycache__/
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*.pyc
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*.pyo
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.env
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.venv/
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venv/
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*.egg-info/
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dist/
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build/
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*.egg
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response_*.wav
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*.log
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.git/
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.gitignore
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.dockerignore
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Dockerfile
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requirements-cpu.txt
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Dockerfile
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Dockerfile
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ARG BASE_IMAGE=nvidia/cuda:12.2.2-runtime-ubuntu22.04
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FROM python:3.11-slim
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FROM ${BASE_IMAGE}
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# Set environment variables
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ENV PYTHONUNBUFFERED=1 \
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ENV PYTHONUNBUFFERED=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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PYTHONDONTWRITEBYTECODE=1 \
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DEBIAN_FRONTEND=noninteractive \
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DEBIAN_FRONTEND=noninteractive \
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MODEL_CACHE_DIR=/models
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MODEL_CACHE_DIR=/models \
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TORCH_CUDA_ARCH_LIST=""
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# Install Python and system dependencies
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# Install system dependencies
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RUN apt-get update && apt-get install -y --no-install-recommends \
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3.10 \
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python3.10-venv \
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python3-pip \
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build-essential \
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git \
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git \
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curl \
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curl \
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wget \
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wget \
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@@ -22,11 +16,15 @@ RUN apt-get update && apt-get install -y --no-install-recommends \
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ffmpeg \
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ffmpeg \
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&& rm -rf /var/lib/apt/lists/*
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&& rm -rf /var/lib/apt/lists/*
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# Install torch CPU-only first (before other deps that might pull CUDA)
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RUN pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cpu
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# Set up Python virtual environment
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# Set up Python virtual environment
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WORKDIR /app
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WORKDIR /app
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# Copy requirements and install Python deps
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COPY requirements.txt .
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COPY requirements.txt .
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RUN pip install --no-cache-dir --upgrade pip && \
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RUN pip install --no-cache-dir -r requirements.txt
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pip install --no-cache-dir -r requirements.txt
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# Copy application code
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# Copy application code
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COPY . .
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COPY . .
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@@ -36,11 +34,14 @@ RUN mkdir -p ${MODEL_CACHE_DIR}
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# Set environment variables for models
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# Set environment variables for models
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ENV TRANSFORMERS_CACHE=${MODEL_CACHE_DIR} \
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ENV TRANSFORMERS_CACHE=${MODEL_CACHE_DIR} \
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HF_HOME=${MODEL_CACHE_DIR} \
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HF_HOME=${MODEL_CACHE_DIR}
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PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True
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# Expose WebSocket port
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# Expose WebSocket port
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EXPOSE 8000
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EXPOSE 8000
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \
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CMD curl -f http://localhost:8000/docs || exit 1
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# Run the application
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# Run the application
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
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26
requirements-cpu.txt
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26
requirements-cpu.txt
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@@ -0,0 +1,26 @@
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# WebSocket server
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fastapi
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uvicorn[standard]
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websockets
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webrtcvad
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# Speech-to-Text
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faster-whisper
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soundfile
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# LLM (CPU-only)
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transformers
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accelerate
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bitsandbytes
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# TTS
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torchaudio
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# Audio processing
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numpy
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scipy
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# Utilities
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python-dotenv
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pydantic
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pydantic-settings
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