The blog is about step by step procedure to install docker in Ubuntu 16.04 and use the docker by running Jupyter notebook with deep learning libraries.
Step 1: Update the system and install the docker dependencies.
sudo apt-get update
sudo apt-get install \
Step 2 Add docker official key
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add –
Step 3 Verify the docker key
sudo apt-key fingerprint 0EBFCD88
Step 4 Add the stable docker repository
sudo add-apt-repository \
“deb [arch=amd64] https://download.docker.com/linux/ubuntu \
$(lsb_release -cs) \
Step 5 Update the package index
sudo apt-get update
Step6 Install the latest version of Docker CE and container
sudo apt-get install docker-ce docker-ce-cli containerd.io
Step 7 Verify that docker is installed by running docker hello world container
sudo docker run hello-world
If you get the hello-world Docker installation is completed.
Lets use the docker by pulling the Docker image. I am using all in one deep learning docker image of docker hub to show how to use docker. Image have tensorflow , keras , caffe , opencv and others deep leaning library. Below is the link of docker hub for all in one deep learning docker image you can check the details of image.
Step8 Pull the docker image
sudo docker pull floydhub/dl-docker:cpu
Step 9 Run the docker image
docker run -it -p 8888:8888 -p 6006:6006 -v /sharedfolder:/root/sharedfolder floydhub/dl-docker:cpu bash
Above command – p is for port forwarding from docker to system and – v is share a folder from system to docker you need to edit path . I am sharing cv_basics folder to docker with name my_folder. You can give any name while sharing.
In my case command is
docker run -it -p 8888:8888 -p 6006:6006 -v /home/lord/cv_basics/:/root/my_folder floydhub/dl-docker:cpu bash
Now we are inside the docker container. Give execute permission and run the jupyter notebook.
sudo chmod +x run_jupyter.sh
open browser in system and type http://localhost:8888
Check the docker pulled images and docker containers in system.
sudo docker images
sudo docker ps -a
We can see I have two docker images and two excited container in my system.