74 lines
2.7 KiB
Plaintext
74 lines
2.7 KiB
Plaintext
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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}' |