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dt-exercises

This is the dt-exercises repo. It contains a suite of exercises that are designed to be used for learning.

Getting Started

Start by installing the requirements:

$ pip install -r requirements.txt

How to use this Repository

Enter into an exercise folder. From there you can do the following:

  • Look at the description of the exercise.
  • dts exercises notebooks will spin up the jupyter server to follow the notebook tutorials (if any) that are related to the exercise.
  • dts exercises build will build the code (if any) in the exercise.
  • dts exercises test --sim will run the code using the Duckietown simulator.
  • dts exercises test --duckiebot_name ![ROBOT_NAME] (--local) will run the exercise on real Duckiebot hardware. If the --local option is specified then the agent will run on your local machine but communicate over the network with your Duckiebot.

After running dts exercises test you can point your browser to http://localhost:8087 which will give you a desktop environment that you can use to view the status of your agent. For example if it is a ROS agent, you can use rqt_image_view or rviz to look at debugging outputs.

Typically there will be code for you to edit. Once you are happy with your code, in many cases you can evaluate it locally or submit it to a challenges for official evaluation against our challenge infrastructure. These are done with:

  • dts challenges evaluate for local evaluation and
  • dts challenges submit for official submission.

You can also run your submission on your Duckiebot with:

  • dts duckiebot submit

The Anatomy of an Exercise

TODOS

  • One recurrent issue is the framerate. The number of renders was decreased to improve speed, but having more than 15 fps would be great.
  • The notebook container has not been updated since last year, everything should be moved to python3 once ente is merge into daffy
  • At some point we might want to move away from docker-compose and makefile. Everything will be migrated to python and dts mooc ...
  • The workflow to make a submission should be made as easy as possible, i.e. we should not have to edit the dockerfile. It might be possible to use parameters.env
  • Some of the knobs for the simulator are either not implemented, not working properly or not tested. We should have every param that we need working in the param file
  • All the images that we need should be moved to the duckietown dockerhub and added to CI. They should be made as dt-projects
  • It might be a better experience to have noVNC and notebook/agent container in the same container. We should also make use of the dashboard for everything that we can
  • We should reuse as many docker layers as possible. base images have been changed recently and making sure we reuse the new base images when possible would save time and space.
  • Move as much stuff as we can into setup and out of root of the repo to make it easier to see what is actually useful to student.
  • Remove all the outputs from the containers (mostly simulator) such that the jupyter and novnc links stays visible at the bottom (alternatively, automatically open them in the browser)