【Docker】一些可以直接用的Docker环境

这里罗列一些打包的镜像，方便直接使用。

cu11.6ubuntu18.04

docker push kevinchina/deeplearning:cu11.6ubuntu18.04

FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu18.04
RUN apt-get update && apt-get install -y wget git vim curl
RUN wget http://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/conda.sh && \bash /tmp/conda.sh -b && rm /tmp/conda.sh
ENV DEBIAN_FRONTEND=noninteractive
# 安装 tzdata 包并设置时区为上海（无交互）
RUN apt-get update && \apt-get install -y tzdata && \ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && \echo "Asia/Shanghai" > /etc/timezone
RUN ~/miniconda3/bin/conda init bash && . ~/.bashrc
RUN . ~/.bashrc && curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh |  bash && \apt-get install -y git-lfs && git-lfs install
RUN apt-get install -y python3-pip
# 打印~/.bashrc
RUN . ~/.bashrc && pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
RUN . ~/.bashrc && conda create -n py3_10_c2 python=3.10 -y
# RUN . ~/.bashrc && conda activate py3_10_c2 && conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia -y

cu11.8ubuntu22.04

docker push kevinchina/deeplearning:cu11.8ubuntu22.04

FROM nvidia/cuda:11.8.0-devel-ubuntu22.04
RUN apt-get update && apt-get install -y wget git vim curl
RUN wget http://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /tmp/conda.sh && \bash /tmp/conda.sh -b && rm /tmp/conda.sh
ENV DEBIAN_FRONTEND=noninteractive
# 安装 tzdata 包并设置时区为上海（无交互）
RUN apt-get update && \apt-get install -y tzdata && \ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime && \echo "Asia/Shanghai" > /etc/timezone
RUN ~/miniconda3/bin/conda init bash && . ~/.bashrc
RUN . ~/.bashrc && curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh |  bash && \apt-get install -y git-lfs && git-lfs install
RUN apt-get install -y python3-pip
# 打印~/.bashrc
RUN . ~/.bashrc && pip config set global.index-url https://pypi.tuna.tsinghua.edu.cn/simple
RUN . ~/.bashrc && conda create -n py3_10_c2 python=3.10 -y
#RUN . ~/.bashrc && conda activate py3_10_c2 && conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.6 -c pytorch -c nvidia -y