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Configure and Manage Your Environment with Docker

Per the Docker docs:

Docker containers wrap a piece of software in a complete filesystem that contains everything needed to run: code, runtime, system tools, system libraries – anything that can be installed on a server. This guarantees that the software will always run the same, regardless of its environment.

Overview

Using Docker to run your code consists of the following:

  1. Install Docker on your computer or a remote system
  2. Pull the precompiled Docker image from Docker Hub
  3. Each time you wish to work, run the image as a new container

The tricky part to working with Docker will be accessing your project code while working with a Python process (via Jupyter, Python, or IPython) running in a container.

GPU support

GPU Support is available for AWS using nvidia-docker. Start here.

Install Docker On Your Computer

Instructions for installation vary by operating system and version.

OS Installation
Instruction		 Docker System Shell Access Jupyter at
Linux Here Docker for Linux bash localhost:8888
MacOS
>= 10.10.3 (Yosemite)		 Here Docker for Mac bash localhost:8888
MacOS
>= 10.8 (Mountain Lion)		 Here Docker Toolbox for Max Docker Quickstart Terminal #DOCKERIP:8888
Windows
10 Pro, Enterprise, or Education		 Here Docker for Windows Windows PowerShell localhost:8888
Windows
7, 8, 8.1, or 10 Home		 Here Docker Toolbox for Windows Docker Quickstart Terminal #DOCKERIP:8888

Pull the Precompiled Docker Image from Docker Hub

A precompiled image with all dependencies required for the first term is available on Docker Hub.

Once you have docker working, pull the image using the following command:

docker pull udacity/carnd-term1-starter-kit

Run The Image as a New Container

In your shell, navigate to the directory of a project, e.g.

$ cd ~/src/CarND-LaneLines-P1

From within this directory, you are going to run a Jupyter server. In order to do this you must attach to the correct port and share a local volume.

The easiest way to share a local volume is via the pwd command, a shell command that prints the working directory. This command will be used differently based on your shell.

If you're using Windows PowerShell:

docker run -it --rm --entrypoint "/run.sh" -p 8888:8888 -v ${pwd}:/src udacity/carnd-term1-starter-kit

If you're using bash or Docker Quickstart Terminal:

docker run -it --rm --entrypoint "/run.sh" -p 8888:8888 -v `pwd`:/src udacity/carnd-term1-starter-kit

Let's break this down.

docker run is the command a startup and run a Docker container.

-it forces the container to run in the foreground (interactive mode) and provides an I/O to the container.

--rm removes the container once it stops running. It prevents the buildup of stale containers once you stop them from running.

--entrypoint "/run.sh" tells the container to run run.sh when it opens. In our case, this file activates the conda environment and opens a jupyter notebook in the background. This is also included in the dockerfile, but sometimes does not appropriately run without this flag.

-p 8888:8888 maps port 8888 on our local machine to port 8888 in the Docker container, this allows us to access port 8888 in the container by visiting localhost:8888.

-v ${pwd}:/src mounts the pwd (present working directory) to the /src directory in the container. Basically, this lets us access files from our local machine on the docker container.

udacity/carnd-term1-starter-kit is the name of the container to run.

To learn more about Docker visit the docs.

For Windows

Use the ip address of the container to connect to Jupyter notebook, this can be found by opening another docker terminal and running this command: docker-machine ip default. Jupyter server can then be accessed by going to this page from your browser: http://[ip-of-container]:8888.