构建py3.6+tensorflow1.15镜像

通过容器构建

#创建python3.6  
conda install -y  python=3.6 
#创建tensorflow1.15
conda install -y  tensorflow-gpu=1.15

python -m pip install -i https://pypi.douban.com/simple/ jupyter 

python -m pip install -i https://pypi.douban.com/simple/ ipywidgets 

jupyter nbextension enable --py widgetsnbextension  

python -m pip install -i https://pypi.douban.com/simple/ jupyterlab 

jupyter serverextension enable --py jupyterlab

构建

#获取容器id
sudo docker ps -a |grep "cuda"
#提交镜像到本地镜像
sudo docker commit 4cd66f64b697 xxx/public_image/cuda_miniconda_py_tensorflow/cuda_10.0_miniconda_py3.6_tensorflow1.15:v1
#镜像打个标签
sudo docker tag 1877820c0e31 xxx/public_image/cuda_miniconda/cuda_10.0_miniconda_py3.6_tensorflow1.15:v1
#上传到harbor
sudo docker push xxx/public_image/cuda_miniconda/cuda_10.0_miniconda_py3.6_tensorflow1.15:v1

通过dockerfile方式

FROM xxx/public_image/cuda_miniconda/cuda_10.0_miniconda_latest:v1

MAINTAINER [email protected]

RUN  conda install -y  python=3.6 \

&& conda install -y  tensorflow-gpu=1.15 \

&& python -m pip install -i https://pypi.douban.com/simple/ jupyter \

&& python -m pip install -i https://pypi.douban.com/simple/ ipywidgets \

&& jupyter nbextension enable --py widgetsnbextension  \

&& python -m pip install -i https://pypi.douban.com/simple/ jupyterlab \

&& jupyter serverextension enable --py jupyterlab

构建

#构建镜像
sudo docker build  -t xxx/public_image/cuda_miniconda_py_tensorflow/cuda_10.0_miniconda_py3.6_tensorflow1.15:v1 .
#登录到harbor
sudo docker login --username=admin xxx --password=xxxxxx 
#推送镜像到harbor
sudo docker push xxx/public_image/cuda_miniconda/cuda_10.0_miniconda_py3.6_tensorflow1.15:v1