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 http://0.0.0.0:4861/api/1.0/quizbowl/act 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

Example

{
  "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"
}

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

Example

{"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 run.sh 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

Installation

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.

IMPORTANT FOR MAC USERS: If you keep getting messages indicating a container was "Killed", follow these instructions to allow Docker to use more CPU/RAM.

Running

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 http://0.0.0.0:4861

Commands

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/
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 http://0.0.0.0:4861/api/1.0/quizbowl/act 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

Output:

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

Codalab

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 codalab.qanta.org.

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

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/run.sh 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.

Maintainer Notes

Dockerhub

The default docker-compose file references the published image for quizbowl at https://hub.docker.com/r/entilzha/quizbowl/

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

docker-compose -f docker-compose.dev.yml 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
$ #config.fish: source ~/anaconda3/etc/fish/conf.d/conda.fish

FAQ

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. https://lucianomolinari.com/2017/06/11/containers-being-killed-on-docker-for-mac/