Public | Automated Build

Last pushed: 2 days ago
Short Description
Image with all prerequisites needed to run the projects of the Udacity Self-Driving Car Nanodegree
Full Description


udacity-carnd

<img src="images/screenshot.png" width="600"/>

Creates a Docker image with all the prerequisites needed to run the projects of the Udacity Self-Driving
Car Nanodegree
.

This image is based on the dev-machine image, and extends it by
adding Anaconda3, OpenCV, TensorFlow, Keras, uWebSockets, cppad, ipopt, etc as instructed in the nanodegree.
I have used it to successfully complete
all projects of the first and second terms.

This image can be launched in CLI mode, in UI mode via a TurboVNC server that you can connect to, or as a
jupyter server. Or in a variety of other situations.

The most useful way to launch it, is probably as a jupyter server. The image has a useful script baked in,
that you can run as:

$ docker run -it -v $PWD:/src -p 8888:8888 yrahal/udacity-carnd bash /bin/run_jupyter.sh

You can then open localhost:8888 in your browser and execute your notebooks, assuming your notebooks
can be found under the current directory hierarchy.

You can also refer to the instructions from the
dev-machine repository for extra information on
usage. The notable differences is that yrahal/dev-machine must be replaced by yrahal/udacity-carnd
and that the default user in yrahal/udacity-carnd is... kitt.

You might also find this
Medium post
and this YouTube video useful.

Files

  • run.sh: Script provided for convenience to run the image with some useful mappings:
    • Runs the image with a TurboVNC server and maps the container's 5901 port to the same one on
      the host. It also maps the 8888 jupyter and 4567 simulator ports to that of the host.
    • Maps the current directory on the host to /src on the container (which is the default working
      directory).
    • Maps the Docker volume kitt-home to the kitt home directory on the container. This volume
      exists on the host and is created on the first run. This is useful to persist the preferences
      between sessions, but is not required.
  • run_jupyter.sh: Another script provided for convenience, which mimics the previous one except:
    • Runs a jupyter server by default. So no TurboVNC server is launched. Therefore, only the
      8888 port is mapped.
  • build.sh: Script to build the image from the Dockerfile.
  • Dockerfile: File used to build the image. This image is hosted on Docker Hub as
    yrahal/udacity-carnd.
Docker Pull Command
Owner
yrahal
Source Repository