Docker部署nnunetv2的实现步骤

# 使用适当的基础镜像（如 pytorch 官方镜像）
FROM pytorch/pytorch:latest

# 更新 apt-get 并安装必要的工具和库
RUN apt-get update && apt-get install -y vim \
    && apt-get install -y --no-install-recommends \
    python3-pip \
    python3-setuptools \
    build-essential \
    && apt-get clean \
    && python -m pip install --upgrade pip

# 设置工作目录
WORKDIR /workspace

# 将项目代码复制到容器中
COPY ./nnunetv2 /workspace
COPY requirements.txt /workspace

# 安装项目依赖
RUN pip install --no-cache-dir -r requirements.txt
RUN pip install nnunetv2

# 配置 nnUNet 的环境变量
ENV nnUNet_raw="/workspace/nnUNet_raw"
ENV nnUNet_preprocessed="/workspace/nnUNet_preprocessed"
ENV nnUNet_results="/workspace/nnUNet_trained_models"

# 默认启动命令
CMD ["/bin/bash"]

docker build -t nnunet_docker:0.0.1 .

# 旧版配置
ENV nnUNet_raw_data_base="/workspace/DATASET/nnUNet_raw"
ENV nnUNet_preprocessed="/workspace/DATASET/nnUNet_preprocessed"
ENV RESULTS_FOLDER="/workspace/DATASET/nnUNet_trained_models"

torch>=2.1.2
acvl-utils<0.3,>=0.2
dynamic-network-architectures<0.4,>=0.3.1
tqdm
dicom2nifti
scipy
batchgenerators>=0.25
numpy
scikit-learn
scikit-image>=0.19.3
SimpleITK>=2.2.1
pandas
graphviz
tifffile
requests
nibabel
matplotlib
seaborn
imagecodecs
yacs
batchgeneratorsv2>=0.2
einops
flask
black
ruff
pre-commit

docker run --rm -it --gpus all \
    -v /data/xtx/nnUNet/nnUNetFrame/DATASET/nnUNet_raw:/workspace/nnUNet_raw \
    -v /data/xtx/nnUNet/nnUNetFrame/DATASET/nnUNet_preprocessed:/workspace/nnUNet_preprocessed \
    -v /data/xtx/nnUNet/nnUNetFrame/DATASET/nnUNet_trained_models:/workspace/nnUNet_trained_models \
    -p 5000:5000 \
    --ipc=host nnunet_docker:0.0.1