310491a557
- Updated python_coding_microproject.csv to use NVIDIA NIM Kimi K2 - Updated kali_linux_shell_simulator.csv to use NVIDIA NIM Kimi K2 - Made more general-purpose (flexible targets, expanded tools) - Added nemotron-mini-agent.csv for fast local inference via Ollama - Added nemotron-agent.csv for advanced reasoning via Ollama - Added wiki page: Projects for NVIDIA NIMs and Nemotron
73 lines
2.0 KiB
Docker
73 lines
2.0 KiB
Docker
FROM python:3.11-slim
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# Install system dependencies for DGX Grace ARM with optimized libraries
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# Note: Removed libatlas-base-dev as it's not available in Debian Trixie ARM64
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RUN apt-get update && apt-get install -y \
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gcc \
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g++ \
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curl \
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libblas-dev \
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liblapack-dev \
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libopenblas-dev \
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gfortran \
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pkg-config \
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build-essential \
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cmake \
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&& rm -rf /var/lib/apt/lists/*
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# Install PyTorch CPU-only for ARM with optimized BLAS
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RUN pip install --no-cache-dir torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
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# Install optimized dependencies for DGX Grace ARM64
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RUN pip install --no-cache-dir \
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transformers>=4.36.0 \
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sentence-transformers \
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fastapi \
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uvicorn \
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numpy \
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accelerate \
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onnxruntime \
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optimum[onnxruntime] \
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psutil
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# Set comprehensive DGX Grace ARM64 environment variables for maximum performance
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ENV OMP_NUM_THREADS=20
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ENV MKL_NUM_THREADS=20
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ENV BLIS_NUM_THREADS=20
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ENV OPENBLAS_NUM_THREADS=20
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ENV VECLIB_MAXIMUM_THREADS=20
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ENV PYTORCH_NUM_THREADS=20
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ENV PYTORCH_ENABLE_MPS_FALLBACK=1
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ENV PYTORCH_MPS_HIGH_WATERMARK_RATIO=0.0
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ENV CUDA_VISIBLE_DEVICES=""
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ENV USE_ONNX_RUNTIME=true
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ENV MALLOC_ARENA_MAX=8
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# DGX Grace architecture optimizations
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ENV CFLAGS="-march=armv8.2-a+fp16+rcpc+dotprod -O3 -ffast-math"
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ENV CXXFLAGS="-march=armv8.2-a+fp16+rcpc+dotprod -O3 -ffast-math"
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# Memory optimization for 128GB system
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ENV PYTORCH_CUDA_ALLOC_CONF=max_split_size_mb:512
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ENV OMP_STACKSIZE=2M
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ENV KMP_STACKSIZE=2M
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# Platform identification
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ENV GT2_PLATFORM=dgx
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ENV GT2_ARCHITECTURE=grace-arm
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# Create app directory
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WORKDIR /app
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# Copy the custom OpenAI-compatible BGE-M3 server optimized for DGX
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COPY .deployment/docker/embedding_server_dgx.py /app/embedding_server.py
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# Expose port
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EXPOSE 8000
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# Health check with longer timeout for DGX startup
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HEALTHCHECK --interval=30s --timeout=60s --start-period=600s --retries=5 \
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CMD curl -f http://localhost:8000/health || exit 1
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# Run the embedding server
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CMD ["python", "embedding_server.py"] |