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43
ai_platform/modules/llm-inference/deploy/Dockerfile
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43
ai_platform/modules/llm-inference/deploy/Dockerfile
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# LLM Inference API Server
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# Multi-stage build for smaller final image
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FROM python:3.11.12-slim AS builder
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# Install uv
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COPY --from=ghcr.io/astral-sh/uv:0.10 /uv /usr/local/bin/uv
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WORKDIR /app
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# Copy project files
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COPY pyproject.toml uv.lock README.md ./
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COPY src/ ./src/
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# Install dependencies
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RUN uv sync --frozen --no-dev
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# Production image
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FROM python:3.11.12-slim
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WORKDIR /app
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# Copy virtual environment from builder
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COPY --from=builder /app/.venv /app/.venv
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# Copy source code
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COPY src/ ./src/
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# Set environment variables
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ENV PATH="/app/.venv/bin:$PATH"
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ENV PYTHONUNBUFFERED=1
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ENV LLM_HOST=0.0.0.0
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ENV LLM_PORT=14011
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# Health check (using Python since curl not available in slim image)
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HEALTHCHECK --interval=30s --timeout=10s --retries=3 \
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CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:14011/health')" || exit 1
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# Expose port
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EXPOSE 14011
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# Run the server
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CMD ["python", "-m", "llm_inference.cli"]
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154
ai_platform/modules/llm-inference/deploy/deploy.sh
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154
ai_platform/modules/llm-inference/deploy/deploy.sh
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#!/usr/bin/env bash
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#
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# Docker Compose Startup Script for LLM Inference
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#
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# Usage: ./deploy/docker-start.sh [OPTIONS]
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#
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# Options:
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# --profile <api|vllm|llamacpp|full> Docker compose profile
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# --detach Run in detached mode
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# --down Stop and remove containers
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# --logs Show logs
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# --help Show this help message
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#
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# Required: Set environment variables in .env file or export them before running.
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# See .env.example for the full list of required variables.
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set -euo pipefail
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# Script directory
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SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
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# Load .env file if it exists (check deploy/ first, then module root)
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ENV_FILE=""
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if [[ -f "$SCRIPT_DIR/.env" ]]; then
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ENV_FILE="$SCRIPT_DIR/.env"
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elif [[ -f "$SCRIPT_DIR/../.env" ]]; then
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ENV_FILE="$SCRIPT_DIR/../.env"
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fi
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if [[ -n "$ENV_FILE" ]]; then
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echo "Loading environment from: $ENV_FILE"
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set -a
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source "$ENV_FILE"
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set +a
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fi
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PROFILE=""
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DETACH=""
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ACTION="up"
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show_help() {
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sed -n '2,16p' "$0" | sed 's/^# //' | sed 's/^#//'
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exit 0
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}
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check_required_var() {
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local var_name="$1"
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if [[ -z "${!var_name:-}" ]]; then
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echo "ERROR: Required environment variable $var_name is not set"
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echo "Set it in .env file or export it before running this script"
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exit 1
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fi
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}
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# Parse arguments
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while [[ $# -gt 0 ]]; do
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case $1 in
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--profile)
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PROFILE="$2"
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shift 2
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;;
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--detach|-d)
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DETACH="-d"
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shift
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;;
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--down)
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ACTION="down"
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shift
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;;
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--logs)
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ACTION="logs"
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shift
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;;
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--help|-h)
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show_help
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;;
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*)
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echo "Unknown option: $1"
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echo "Use --help for usage information"
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exit 1
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;;
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esac
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done
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# Require profile to be specified
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if [[ -z "$PROFILE" && "$ACTION" == "up" ]]; then
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echo "ERROR: --profile is required"
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echo "Options: api, vllm, llamacpp, full"
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exit 1
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fi
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# Check required variables based on profile
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check_required_var "LLM_DEFAULT_BACKEND"
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check_required_var "LLM_ENABLE_VLLM"
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check_required_var "LLM_ENABLE_LLAMACPP"
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check_required_var "LLM_EXTERNAL_URL"
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# API keys - at least one MUST be set for litellm backend (fail-fast)
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if [[ "$LLM_DEFAULT_BACKEND" == "litellm" ]]; then
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if [[ -z "${OPENROUTER_API_KEY:-}" && -z "${OPENAI_API_KEY:-}" && -z "${ANTHROPIC_API_KEY:-}" ]]; then
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echo "ERROR: litellm backend requires at least one API key"
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echo "Set one of: OPENROUTER_API_KEY, OPENAI_API_KEY, or ANTHROPIC_API_KEY"
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exit 1
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fi
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fi
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# Check model-specific variables for vllm/llamacpp profiles
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case $PROFILE in
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vllm|full)
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check_required_var "MODELS_DIR"
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check_required_var "VLLM_MODEL"
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;;
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llamacpp|full)
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check_required_var "MODELS_DIR"
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check_required_var "LLAMACPP_MODEL"
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check_required_var "LLAMACPP_THREADS"
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check_required_var "LLAMACPP_PARALLEL"
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;;
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esac
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cd "$SCRIPT_DIR"
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case $ACTION in
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up)
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echo "Starting LLM Inference with profile: $PROFILE"
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echo " Default backend: $LLM_DEFAULT_BACKEND"
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echo " vLLM enabled: $LLM_ENABLE_VLLM"
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echo " llama.cpp enabled: $LLM_ENABLE_LLAMACPP"
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if [[ "$PROFILE" == "vllm" || "$PROFILE" == "full" ]]; then
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echo " vLLM model: $VLLM_MODEL"
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fi
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if [[ "$PROFILE" == "llamacpp" || "$PROFILE" == "full" ]]; then
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echo " llama.cpp model: $LLAMACPP_MODEL"
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fi
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echo ""
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# shellcheck disable=SC2086
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exec docker compose --profile "$PROFILE" up $DETACH
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;;
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down)
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if [[ -z "$PROFILE" ]]; then
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echo "ERROR: --profile is required with --down"
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exit 1
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fi
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echo "Stopping LLM Inference containers..."
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exec docker compose --profile "$PROFILE" down
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;;
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logs)
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if [[ -z "$PROFILE" ]]; then
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echo "ERROR: --profile is required with --logs"
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exit 1
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fi
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exec docker compose --profile "$PROFILE" logs -f
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;;
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esac
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142
ai_platform/modules/llm-inference/deploy/docker-compose.yml
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ai_platform/modules/llm-inference/deploy/docker-compose.yml
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# LLM Inference Module - Docker Compose Configuration
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#
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# Port Allocation:
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# 14001 - vLLM Qwen3.5-35B-A3B (text + vision MoE, native multimodal)
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# 14011 - LLM Inference API (unified router)
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#
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# Profiles:
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# api - API server only (uses external LLM services)
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# vllm - API + vLLM servers (GPU required)
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#
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# Model:
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# - Qwen3.5-35B-A3B (~57GB weights BF16, gpu-util 0.45)
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# - Native vision support (replaces separate Qwen3-VL)
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#
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# Hardware: 1x NVIDIA H200 NVL (~143GB VRAM)
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# - GPU 0: Qwen3.5-35B-A3B + Whisper (~2GB)
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#
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# Naming Convention: didiAI-{module}-{service}
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#
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# Network:
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# Uses deploy_default network (shared with other modules)
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networks:
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deploy_default:
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external: true
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services:
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# ==========================================================================
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# LLM Inference API Server
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# ==========================================================================
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llm-api:
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container_name: didiAI-llm-api
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image: didiai-llm-api
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build:
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context: ..
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dockerfile: deploy/Dockerfile
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ports:
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- "14011:14011"
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networks:
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- deploy_default
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environment:
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- LLM_PORT=14011
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- LLM_EXTERNAL_URL=${LLM_EXTERNAL_URL}
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- LLM_DEFAULT_BACKEND=${LLM_DEFAULT_BACKEND}
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- LLM_ENABLE_VLLM=${LLM_ENABLE_VLLM}
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- LLM_ENABLE_LLAMACPP=${LLM_ENABLE_LLAMACPP}
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- LLM_VLLM_BASE_URL=http://didiAI-vllm-qwen3.5:14001
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- LLM_LLAMACPP_BASE_URLS=${LLM_LLAMACPP_BASE_URLS:-}
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- LLM_API_TOKENS=${LLM_API_TOKENS:-}
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- LLM_DASHBOARD_URL=${LLM_DASHBOARD_URL:-http://didiAI-dashboard:51300}
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- OPENROUTER_API_KEY=${OPENROUTER_API_KEY:-}
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- OPENAI_API_KEY=${OPENAI_API_KEY:-}
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- ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY:-}
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healthcheck:
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test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:14011/health')"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 30s
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restart: unless-stopped
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profiles:
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- api
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- vllm
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# ==========================================================================
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# vLLM Server - Qwen3.5-35B-A3B (Unified Text + Vision MoE Model)
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# ==========================================================================
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# Native multimodal - replaces separate Qwen3 text + Qwen3-VL vision
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vllm-qwen3.5:
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container_name: didiAI-vllm-qwen3.5
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image: vllm/vllm-openai:qwen3_5
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ports:
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- "14001:14001"
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networks:
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- deploy_default
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volumes:
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- ${HF_CACHE_DIR}:/root/.cache/huggingface
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environment:
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- HF_HOME=/root/.cache/huggingface
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- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN:-}
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- CUDA_VISIBLE_DEVICES=0
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- VLLM_ALLOW_LONG_MAX_MODEL_LEN=1
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command: >
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--model Qwen/Qwen3.5-35B-A3B
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--host 0.0.0.0
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--port 14001
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--served-model-name qwen3.5
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--tensor-parallel-size 1
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--max-model-len 32000
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--gpu-memory-utilization 0.65
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--trust-remote-code
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--enable-prefix-caching
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--disable-log-requests
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--enable-auto-tool-choice
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--tool-call-parser hermes
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deploy:
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resources:
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reservations:
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devices:
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- driver: nvidia
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device_ids: ['0']
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capabilities: [gpu]
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:14001/health"]
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interval: 30s
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timeout: 10s
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retries: 10
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start_period: 600s
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restart: unless-stopped
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profiles:
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- vllm
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# ==========================================================================
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# llama.cpp Server (CPU/Metal Inference) - Optional
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# ==========================================================================
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llamacpp:
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image: ghcr.io/ggml-org/llama.cpp:server-b4769
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expose:
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- "14011"
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volumes:
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- ${MODELS_DIR:-/cai2_ds_storage/models}:/models:ro
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command: >
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--model /models/${LLAMACPP_MODEL:-model.gguf}
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--host 0.0.0.0
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--port 14011
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--ctx-size 4096
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--threads ${LLAMACPP_THREADS:-4}
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--parallel ${LLAMACPP_PARALLEL:-1}
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healthcheck:
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test: ["CMD", "curl", "-f", "http://localhost:14011/health"]
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interval: 30s
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timeout: 10s
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retries: 3
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start_period: 60s
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restart: unless-stopped
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profiles:
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- llamacpp
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- full
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volumes:
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vllm-cache:
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name: llm-inference-vllm-cache
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49
ai_platform/modules/llm-inference/deploy/nginx.conf
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49
ai_platform/modules/llm-inference/deploy/nginx.conf
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upstream llm_api {
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server llm-api:8100;
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keepalive 32;
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}
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server {
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listen 80;
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server_name _;
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# Timeouts for slow LLM responses
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proxy_connect_timeout 60s;
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proxy_send_timeout 120s;
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proxy_read_timeout 300s;
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# Health checks (no logging)
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location /health {
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access_log off;
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proxy_pass http://llm_api/health;
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proxy_http_version 1.1;
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proxy_set_header Host $host;
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}
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location /ready {
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access_log off;
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proxy_pass http://llm_api/ready;
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proxy_http_version 1.1;
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proxy_set_header Host $host;
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}
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# API endpoints
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location / {
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proxy_pass http://llm_api;
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proxy_http_version 1.1;
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# Forward client info
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proxy_set_header Host $host;
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proxy_set_header X-Real-IP $remote_addr;
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proxy_set_header X-Forwarded-For $proxy_add_x_forwarded_for;
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# SSE streaming support
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proxy_set_header Connection '';
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proxy_buffering off;
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proxy_cache off;
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chunked_transfer_encoding off;
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# Large request bodies for long conversations
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client_max_body_size 10M;
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}
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}
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