为了避免遗忘，整理一下开发使用Docker配置PyTorch/TensorFlow环境的方法
DockerFile
# For more information, please refer to https://aka.ms/vscode-docker-python
FROM cddlyf/pytorch1.3.0-tensorflow-py36-cuda10.1:latest
# Keeps Python from generating .pyc files in the container
ENV PYTHONDONTWRITEBYTECODE=1
# Turns off buffering for easier container logging
ENV PYTHONUNBUFFERED=1
# Copy pip requirements
COPY requirements.txt .
# Copy pre-downloaded wheels
COPY ./wheels ./wheels
# switch to ustc pip source
# RUN pip install --default-timeout=10 -U pip
# RUN pip config set global.index-url http://pypi.mirrors.ustc.edu.cn/simple
# RUN pip config set install.trusted-host pypi.mirrors.ustc.edu.cn
# RUN pip install -r requirements.txt
# Run pip install from file
RUN pip install --no-index --find-links=./wheels -r requirements.txt
RUN pip install torch-cluster==1.4.5
RUN pip install torch-geometric==1.3.2
RUN pip install torch-scatter==1.3.2
RUN pip install torch-sparse==0.4.3
# Install libraries
COPY ./mesh /mesh
# RUN apt-get install libboost-dev
WORKDIR /mesh
RUN make all
# Copy code from . to $CONTAINER/app
WORKDIR /app
COPY . /app
# Remove wheels
RUN rm -rf /app/wheels
# Creates a non-root user with an explicit UID and adds permission to access the /app folder
# For more info, please refer to https://aka.ms/vscode-docker-python-configure-containers
# RUN adduser -u 5678 --disabled-password --gecos "" appuser && chown -R appuser /app
# USER appuser
# During debugging, this entry point will be overridden. For more information, please refer to https://aka.ms/vscode-docker-python-debug
# CMD ["python", "main.py"]
一些记录：
- 由于pip安装时间长，经常崩溃，可以先使用
pip download -d wheels -r requirements.txt将依赖wheels下载下来，再进行本地安装 - 配置错误产生中间文件太多，可以用
docker image prune,docker container prune清理
构建与运行
docker build --pull -f "/your/path/to/Dockerfile" -t "img_name:tag" "/your/path/to/workdir"
# Replace 6667 with your port
docker run -p 6667:22 -it -d --name="container_name" img_name:tag
# Test docker ssh
docker ps
ssh -p 6667 root@0.0.0.0