使用docker来配置pycharm开发和训练环境

本文主要介绍使用docker＋pycharm方式来搭建pytoch训练平台

操作流程如下：

一、宿主机docker环境安装

　　可参考该链接：NVidia-Docker2安装与常用命令

二、新建Docker镜像

有两种方式：

１、直接新建Ｄockerfile的方式生成

     1)新建一个Dockerfile文件

ARG CUDA="11.0"
ARG CUDNN="8"

FROM nvidia/cuda:${CUDA}-cudnn${CUDNN}-devel-ubuntu16.04

RUN echo 'debconf debconf/frontend select Noninteractive' | debconf-set-selections

# install basics
RUN apt-get update -y \
 && apt-get install -y apt-utils git curl ca-certificates bzip2 cmake tree htop bmon iotop g++ \
 && apt-get install -y libglib2.0-0 libsm6 libxext6 libxrender-dev

# Install Miniconda
RUN curl -so /miniconda.sh https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh \
 && chmod +x /miniconda.sh \
 && /miniconda.sh -b -p /miniconda \
 && rm /miniconda.sh

ENV PATH=/miniconda/bin:$PATH

# Create a Python 3.6 environment
RUN /miniconda/bin/conda install -y conda-build \
 && /miniconda/bin/conda create -y --name py36 python=3.6.7 \
 && /miniconda/bin/conda clean -ya

ENV CONDA_DEFAULT_ENV=py36
ENV CONDA_PREFIX=/miniconda/envs/$CONDA_DEFAULT_ENV
ENV PATH=$CONDA_PREFIX/bin:$PATH
ENV CONDA_AUTO_UPDATE_CONDA=false

RUN conda install -y ipython
RUN pip install requests ninja yacs cython matplotlib opencv-python tqdm

# Install PyTorch 1.0 Nightly
ARG CUDA
RUN conda install pytorch-nightly cudatoolkit=${CUDA} -c pytorch \
 && conda clean -ya

# Install TorchVision master
RUN git clone https://github.com/pytorch/vision.git \
 && cd vision \
 && python setup.py install

# install apex
RUN git clone https://github.com/NVIDIA/apex.git \
 && cd apex \
 && python setup.py install --cuda_ext --cpp_ext

# install PyTorch Detection
ARG FORCE_CUDA="1"
ENV FORCE_CUDA=${FORCE_CUDA}

RUN mkdir workspace

WORKDIR /workspace

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