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Dockerfile

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+FROM ghcr.io/astral-sh/uv:latest AS uv_bin
+FROM nvidia/cuda:12.4.1-runtime-ubuntu22.04
+
+ENV UV_LINK_MODE=copy \
+    UV_COMPILE_BYTECODE=1 \
+    PYTHONUNBUFFERED=1 \
+    UV_PROJECT_ENVIRONMENT=/app/.venv \
+    TORCH_CUDA_ARCH_LIST="8.6" \
+    CUDA_VISIBLE_DEVICES=0 \
+    UV_TORCH_BACKEND=cu128
+
+COPY --from=uv_bin /uv /uvx /bin/
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    ffmpeg libsm6 libxext6 \
+    build-essential g++ \
+    git \
+    && apt-get clean && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+# 3. 安装依赖 (不加 --system，让 uv 创建受管的虚拟环境)
+# 这里会根据 pyproject.toml 自动下载并安装 Python 3.11
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-dev --no-install-project --python 3.9
+# 4. 拷贝项目文件并安装项目本身
+COPY . .
+RUN uv sync --frozen --no-dev --python 3.9
+
+# 5. 【最关键】将虚拟环境的 bin 目录提到最前面
+# 注意：uv sync 创建的 python 就在这个目录下
+ENV PATH="/app/.venv/bin:$PATH"
+
+EXPOSE 8000
+
+# 验证路径并运行
+# 此时运行 python 实际上是运行 /app/.venv/bin/python
+CMD ["sleep", "infinity"]

docker-compose.yaml

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+services:
+  AiDA_design2garment:
+    container_name: AiDA_design2garment
+    build:
+      context: .
+      dockerfile: Dockerfile
+    working_dir: /app
+    volumes:
+      - .:/app
+    ports:
+      - "10071:8000"
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              device_ids: [ '1' ]
+              capabilities:
+                - gpu
+    networks:
+      - lc_app_net
+networks:
+  aida_app_net:
+    external: true
+    name: aida_app_net