mpkuse/kusevisionkit repository overview

#Drop into bash shell of the tensorflow image *
$ docker run --runtime=nvidia -it \
        tensorflow/tensorflow:latest-gpu bash 

# Share a host folder with the docker container. This is the 
# recommended way for developers. You edit your files on the 
# host machine and execute on the docker. This will mount the 
# hosts's $HOME/docker_ws on /app in the docker virtual machine.
$ docker run --runtime=nvidia -it -v $HOME/docker_ws:/app \
               tensorflow/tensorflow:latest-gpu bash 

# Open additional shell in existing container
$ docker container ls 
CONTAINER ID        IMAGE                       COMMAND             CREATED             STATUS              PORTS                NAMES
e66e902667cc        mpkuse/kusevisionkit:v0.4   "bash"              2 hours ago         Up 2 hours          6006/tcp, 8888/tcp   tender_raman
$ docker exec -it e66e902 bash 

# Container's port can be made available on the host machine. 
# This is for enabling communication between applications running
# on different containers. Following will make 8080 port of the
# container available as port 8080 on the host machine. 
docker run -it -p 8080:8080 mpkuse/hello:v0.1 bash

# After you have a base image like above, go ahead and customize 
# it with additional libraries that are need to run your code etc. 
# COmmit and push to your personal hub.docker. You will need 
# a hub.docker account. Go ahead and create it. 
$(container) apt-get install python-dev
$(host) docker commit <container id> mpkuse/customimage:v0.1
$(host) docker login 
$(host) docker push mpkuse/customimage:v0.1