docker启动项目环境搭建
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yanyuxiyangzk@126.com
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2
app.py
2
app.py
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@ -95,7 +95,7 @@ def txt_to_audio(text_):
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text_,
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gspeaker,
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"zh-cn", #en args.language,
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"http://localhost:9000", #args.server_url,
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"http://192.168.1.3:9000", #args.server_url,
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"20" #args.stream_chunk_size
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),
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nerfreal
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@ -0,0 +1,56 @@
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# 使用NVIDIA的CUDA基础镜像
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#FROM nvidia/cuda:11.3.0-cudnn8-runtime-ubuntu18.04
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#FROM m11007322/cuda11.3.0-cudnn8-devel-ubuntu20.04-jupyterlab
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FROM nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04
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# 安装Python和pip
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RUN apt-get update && apt-get install -y --no-install-recommends \
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python3 \
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python3-pip \
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&& rm -rf /var/lib/apt/lists/*
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# 安装Jupyter
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RUN pip3 install --no-cache-dir jupyter
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# 安装基础工具
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RUN apt-get update -yq --fix-missing \
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&& DEBIAN_FRONTEND=noninteractive apt-get install -yq --no-install-recommends \
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pkg-config \
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wget \
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cmake \
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curl \
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git \
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vim
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# 创建一个新的Conda环境
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RUN apt-get update && apt-get install -y wget \
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&& wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh \
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&& /bin/bash Miniconda3-latest-Linux-x86_64.sh -b -p /opt/conda \
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&& rm Miniconda3-latest-Linux-x86_64.sh \
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&& apt-get remove --purge --auto-remove -y wget \
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&& apt-get clean \
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&& ln -s /opt/conda/bin/conda /usr/bin/conda \
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&& conda update -n base -c defaults conda
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SHELL ["/bin/bash","-ic"]
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# 增加cuda全局变量
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RUN echo "export CUDA_HOME=/usr/local/cuda" >> ~/.bashrc \
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&& echo "export PATH=${CUDA_HOME}/bin:$PATH" >> ~/.bashrc \
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&& echo "export LD_LIBRARY_PATH=${CUDA_HOME}/lib64:$LD_LIBRARY_PATH" >> ~/.bashrc \
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&& source ~/.bashrc
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#&& echo "nameserver 8.8.8.8" >> /etc/resolv.conf
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# 安装cv2依赖，修复libGL.so.1错误
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RUN apt-get update
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RUN apt-get install ffmpeg libsm6 libxext6 -y
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# 配置Jupyter
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ENV JUPYTER_ENABLE_LAB=yes
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ENV USER=root
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ENV HOME=/home/$USER
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# 设置工作目录
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WORKDIR /root
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# 设置启动命令
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CMD ["jupyter", "lab", "--ip='*'", "--port=8888", "--no-browser", "--allow-root"]
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@ -0,0 +1,74 @@
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一、cuda11.3容器启动过程
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1、拷贝Dockerfile文件到任意磁盘目录，然后执行下面的命令
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docker build -t nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda .
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docker images
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2、启动容器
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打开镜像（常规模式--支持使用GPU）
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docker run -i -t --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda /bin/bash
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打开镜像（增强模式--支持使用GPU、映射目录、设置内存）
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docker run -i -t -v /home/liguopu/:/guopu:rw --gpus all --shm-size 16G nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04 /bin/bash
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测试环境（使用端口映射，把服务映射出去）
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docker run -i -td --name metehuman --gpus -p 8000:8000 all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda /bin/bash
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正式使用（8000端口为业务对外的服务端口，根据情况可以自行增加）
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docker run -it --rm -p 8886:8888 -p 8000:8000 --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
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docker run -itd -p 8886:8888 -p 8000:8000 --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
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docker run -itd --name metehuman -p 8886:8888 -p 8000:8000 --gpus all nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
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docker run --gpus '"device=vgpu,id=0"' -it --rm nvidia/cuda:11.0-base nvidia-smi
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docker run -itd --name metehuman \
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-p 8885:8888 -p 8001:8000 \
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-e GRANT_SUDO=yes \
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-e JUPYTER_ENABLE_LAB=yes \
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--user root \
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--gpus all \
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nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
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3、查看token
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token=$(docker exec -it metehuman jupyter server list | grep -oP '(?<=token=)[a-zA-Z0-9]+')
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echo $token
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二、启动默认测试镜像
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docker pull m11007322/cuda11.3.0-cudnn8-devel-ubuntu20.04-jupyterlab
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docker run -it \
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-d \
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--gpus all \
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-p 8887:8888 \
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-p 8001:8000 \
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--name metehuman2 \
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--user root \
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-e NB_USER="ubuntu" \
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-e CHOWN_HOME=yes \
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-e GRANT_SUDO=yes \
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-w "/home/${NB_USER}" \
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-v "$PWD":"/home/$USER/work" \
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m11007322/cuda11.3.0-cudnn8-devel-ubuntu20.04-jupyterlab
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三、启动jupter镜像测试
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docker run -itd --name test \
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-p 8886:8888 -p 8000:8000 \
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-e GRANT_SUDO=yes \
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-e JUPYTER_ENABLE_LAB=yes \
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--user root \
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--gpus '"device=vgpu,id=0"' \
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nvidia/cuda:11.3.1-cudnn8-devel-ubuntu20.04-jupyter-conda
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docker run -it --name test --network=host --dns 8.8.8.8 --dns 8.8.4.4 --rm ubuntu
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docker run -it --gpus all --network=host --rm registry.cn-hangzhou.aliyuncs.com/lipku/nerfstream:v1.3
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四、查看容器IP
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docker inspect bceda087524e | grep IPAddress
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curl https://openai.api2d.net/v1/chat/completions \
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-H 'Content-Type: application/json' \
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-H 'Authorization: Bearer fk193752-RlcPi2mBQqPOU5u1F8SFkG2z0gtxD0HS' \
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-d '{
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"model": "gpt-3.5-turbo",
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"messages": [{"role": "user", "content": "你好！给我讲个笑话。"}]
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}'
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