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This is a simple, baseline system that does well on the question answering task "quiz bowl". This system generates the baseline score on our leaderboard and demonstrates the IO profile we expect of Docker container submission.

Reference System

We provide sample code which when combined with the provided docker container can answer Quiz Bowl questions. This should provide an example of how the codalab server interacts with the container as well as a simple yet surprisingly effective baseline. The simple system consists of a TF-IDF guesser and a threshold-based buzzer.

System Requirements

All systems will take as input a question (sequence of words), and output an answer guess (a Wikipedia entity) and a binary decision whether to buzz or not.

For example, this command queries a system running locally for what it thinks the best current answer is as well as whether it is deciding to buzz on the answer.

$ http POST text='Name the the inventor of general relativity and the photoelectric effect'
HTTP/1.0 200 OK
Content-Length: 41
Content-Type: application/json
Date: Wed, 10 Oct 2018 01:12:27 GMT
Server: Werkzeug/0.14.1 Python/3.7.0

    "buzz": false,
    "guess": "Albert_Einstein"

Input Format

In addition to the question_text field shown in the httpie sample request we provide a few additional fields.

  • question_idx: Question number in the current game.
  • char_idx: This corresponds on the server to full_question[0:char_idx] if full_question is the entire question.
  • sent_idx: The current sentence number.
  • text Question text up to char_idx


  "question_idx": 0,
  "char_idx": 112,
  "sent_idx": 0,
  "text": "At its premiere, the librettist of this opera portrayed a character who asks for a glass of wine with his dying wish"

Optional Wikipedia paragraphs

Models can optionally (see the status API below) request providing retrieved Wikipedia paragraphs for each sentence of the questions. In that case, one more field, wiki_paragraphs, is added to the input. wiki_paragraphs is a list of the top-5 retrieved sentences for each completed question sentence. Each sentence contains the page from which the sentence comes from, the paragraph and sentence index in that page, the sentence text, and correct answer spans (to help build RC systems). An IR system example actually using these 'evidence' files can be found here.

Output Format

The output answer to each question is also a json object of two fields

  • guess Guessed Wikipedia answer entity
  • buzz true/false whether to buzz given the seen question text so far or not


{"guess": "The_Marriage_of_Figaro", "buzz": true}

Code Requirements

The first requirement we enforce on all systems is that if the current working directory is the contents of src/, and if we run bash that it will start a web server satisfying the input/output formats outlined above.

The second requirement we enforce is that all systems should support a status API. When we startup your system we will query this API until it is running and returning a valid response. If it takes to long to detect then the evaluation script will return an error.

  • URL: /api/1.0/quizbowl/status
  • ready: return True if ready to accept requests
  • batch: True if model accepts batch API (see farther down), False otherwise
  • batch_size: If batch is true, an integer indicating max batch size
  • include_wiki_paragraphs: True to request providing retrieved Wikipedia paragraphs for each question sentence.


You will only need to have docker and docker-compose installed to run this reference system. You may optionally wish to install httpie to test the web api.


To run the reference system we have provided four easy commands to run. All of these commands use the utility docker-compose to do much of the heavy lifting behind the scenes. Importantly it handles port forwarding and volume mapping so that:

  • The directory src/ is synced to /src/ in the container
  • The directory data/ is synced to /src/data in the container
  • The web api is accessible at


These commands are structured via docker-compose CMD CONTAINER ARGS where CMD is a docker-compose command, CONTAINER is either qb or eval, and ARGS runs inside of the container.

  1. docker-compose run qb ./cli download: This will download the training data to data/. Add flag --retrieve-paragraphs to download retrieved Wikipedia paragraphs.
  2. docker-compose run qb ./cli train: This will train a model and place it in src/tfidf.pickle
  3. docker-compose up: This will start the web server in the foreground, -d for background, ctrl-c to stop
  4. docker-compose run eval: This will run the evaluation script

Another useful command is docker-compose down to shutdown zombied web servers and scripts. docker ps -a will also display all running and stopped containers. docker-compose logs will show all the container logs together.

Test Web API

After you have run (1) and (2), you can test everything works by running

docker-compose up

And then the httpie command from before:

$ http POST text='Name the the inventor of general relativity and the photoelectric effect'
HTTP/1.0 200 OK
Content-Length: 41
Content-Type: application/json
Date: Wed, 10 Oct 2018 01:12:27 GMT
Server: Werkzeug/0.14.1 Python/3.7.0

    "buzz": false,
    "guess": "Albert_Einstein"

Batch Web API

WIP: More updates coming soon

GET URL: /api/1.0/quizbowl/status


  "batch": true,
  "batch_size": 200,
  "ready": true

Managing your own special dependencies

If you have no special dependencies, you should not have to create your own docker container, the image we provide and the instructions above should suffice in most cases. In case you have an environment with some non-standard dependencies, you may want to create a docker container for the environment for replicability purposes. You will create your docker container the standard way; some resources that may help:,,

Another option that works particularly well if the dependency is light, is to insert "pip install gensim" to the top of the cli/ file.


These instructions show you how to setup and run a codalab compatible model locally, but to submit to codalab you should follow the instructions at

The diagram below shows how Docker, codalab, and the evaluation scripts are related.

codalab and docker evaluation

At a high level:

  1. You submit your code/model for evaluation by running either the docker-compose command or the codalab macro.
  2. Whichever way you submitted, the evaluation script assumes that running src/ will start your web server
  3. The evaluation script will run questions against the web API endpoint /api/1.0/quizbowl/act
  4. The evaluation script will output your scores to the standard out locally, or post to the leaderboard when run on codalab.


You may be interested in using a GPU in which case these instructions should help. We have prepared a GPU docker image, but there are several things which are different from the normal one. Here we assume you are on a machine with a GPU, and want to run docker locally.

  1. On your local machine you need to have CUDA libraries installed. Installation is specific to OS and not covered here.
  2. Install nvidia-docker which allows docker to use the host machine CUDA driver
  3. Instead of docker-compose you'll need to use docker-compose -f docker-compose-gpu.yml which tells docker to use entilzha/quizbowl-gpu instead of entilzha/quizbowl. This image has the appropriate CUDA libraries installed.
  4. To run you can use nvidia-docker, but this won't use the compose file we have provided. (Instructions untested here) you can use nvidia-docker-compose as a workaround instead as described here
  5. When submitting to codalab we recommend you use start with the following parameters after the cl macro command --request-gpus 1 --request-cpus 2 --request-memory 12g --request-docker-image entilzha/quizbowl-gpu
  6. Lastly, the image does not modify the base anaconda environment, but instead creates a new one named qb. For things to work you will need to run source activate qb at the top of and cli to switch to the new environment.

Maintainer Notes


The default docker-compose file references the published image for quizbowl at

To push new images requires the correct permissions and running the following commands in sequence:

docker-compose -f build
docker tag qanta-codalab_qb:latest entilzha/quizbowl
docker push entilzha/quizbowl

Codalab CLI

Install with anaconda python using

$ conda env create -f codalab_cl_env.yml
$ source activate codalab
$ source ~/anaconda3/etc/fish/conf.d/


  1. When training locally in docker, i got the training process killed. A: This happened in Mac users. You need to increase the memory in docker configuration. See the instructions from this website.

  2. When I tried to install Codalab on Mac OS, I got the following error:

codalabworker 0.2.41 has requirement six==1.11.0, but you'll have six 1.12.0 which is incompatible.
codalab 0.2.41 has requirement six==1.11.0, but you'll have six 1.12.0 which is incompatible.

A: This is because codalab is using a couple of out of date components, and Mac OS has a more recent version installed that you can't overwrite. To fix this, you can install the specific version that codalab needs:

sudo pip2 install six==1.11.0
  1. My pip is connected to Python3 and isn't letting me install packages for Codalab, which needs Python2. A: Install pip2:
sudo python2.7
  1. FOR MAC USERS: I keep getting messages indicating a container was "Killed". A: Follow these instructions to allow Docker to use more CPU/RAM.

  2. TIP: You might want to use Python 3.6 and above for your code.