Hands-On Deep Learning for Images with TensorFlow

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Sharing data
In this section, we will take a look at sharing data between your Docker container and your
desktop. We're going to cover some necessary security settings to allow access. We will
then run the self test to make sure that we've got those security settings correct, and finally,
we're going to run our actual Docker file.
Now, assuming you have Docker installed and running, you need to get into the Docker
settings from the cute little whale in the Settings... menu. So, go to the lower right on your
taskbar, right-click the whale, and select Settings...:
Docker Settings

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Machine Learning Toolkit
Chapter 1
[ 14 ]
There are a few security settings we need to get right in order for our 
VOLUME
to work so
that our Docker container can look at our local hard drive. I've popped this setting up from
the whale, and we're going to select and copy the test command we'll be using later, and
click on Apply:
Docker Settings window
Now, this is going to pop up with a new window asking for a password so that we are
allowing Docker to map a shared drive back to our PC so that our PC's hard drive is visible
from within the container. This share location is where we're going to be working and
editing files so that we can save our work.
Now that we have the command that we copied from the dialog, we're going to go ahead
and paste it into the Command Prompt, or you can just type it in where we're going to run
a test container, just to make sure that our Docker installation can actually see local hard
drives:
C:\11519>docker run --rm -v c:/Users:/data alpine ls /data

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Machine Learning Toolkit
Chapter 1
[ 15 ]
So, you can see that with the 
-v
switch, we're saying see 
c:/Users:
, which is actually on
our local PC, and then 
/data
, which is actually on the container, which is the volume and
the 
alpine
test machine. What you can see is that it's downloading the 
alpine
test
container, and then running the 
ls
command, and that we have access:
Output— ls command
Note that if you are running on Linux, you won't have to do any of these steps; you just
have to run your Docker command with 
sudo
, depending upon which filesystem you're
actually sharing. Here, we're running both 
docker
and 
nvidia-docker
to make sure that
we have access to our home directories:
Running docker and nvidia-docker
Remember, 
nvidia-docker
is a specialized version of Docker with
plugins with a nice convenient wrapper that allows local GPU devices on
your Linux installation to be visible from Docker containers. You need to
remember to run it with 
nvidia-docker
if you intend on using GPU
support.

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Machine Learning Toolkit
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Now, we're actually going to build our container with the 
docker build
command. We're
going to use 
-t
in order to give it a name called 
keras
, and then go ahead and run the
following command: