paul-octopus-python

You can use this Python code to construct your solution to predict the results of the 2022 World Cup.

This is a Python webapp, and you will need to deploy it in a cloud provider. In this example we will use the Azure Container Instances to run the webapp and generate the predictions.

Requirements

• Docker
• Python

Google Artifact Registry

In this example we will use the Google Artifact Registry to push our Docker image containing the webapp.

To start using the GCP to push images from your local environment, you will first need to authenticate

gcloud auth login
gcloud config set project phoenix-cit
gcloud auth application-default login

Create an artifact repository for you

gcloud artifacts repositories create paul-2022-<login> --repository-format=docker \
--location=us-central1 --description="Paul 2022 - <Name>"

With docker running on your machine, configure docker to be able to connect to your artifact repository

gcloud auth configure-docker us-central1-docker.pkg.dev

Implementing your code to predict matches

Feel free to change or adapt anything in this solution.

If you just want to write a function to predict a match, based on the teams that are playing, you just need to create a new python file inside predictor folder, and extend the AbstractPredictor class.

One dummy predictor called OneZeroPredictor is included as an example.

Remember that you will need to use at lease one of the dataset files provided inside the Storage Account.

Building and pushing your docker image

When you have your predictor implemented, you can build the docker image using the provided Dockerfile.

Include your login in the image, to avoid conflicts with others that are sharing the same environment.

docker build -t us-central1-docker.pkg.dev/phoenix-cit/paul-2022-<login>/<login>-predictor .
docker push us-central1-docker.pkg.dev/phoenix-cit/paul-2022-<login>/<login>-predictor

This will push your predictor WebApp to Artifact Registry, and it will be available to deploy using Cloud Run.

Deploy in Cloud Run

Cloud Run is an easy way to deploy your WebApp in the GCP.

gcloud run deploy <login>-predictor --image=us-central1-docker.pkg.dev/phoenix-cit/paul-2022-<login>/<login>-predictor --region=us-central1

Accessing your App and generating the Predictions

After the deployment of your app, gcloud will output the service url that you can use to access your predictor.

You can now access the app from your browser.

Running a predictor class

The way that this solution is implemented, you can pass the name of your predictor in the URL like the following example:

https://eduardohf-predictor-wuydz5dt4a-uc.a.run.app/predict/OneZeroPredictor

In this example, it will instantiate the OneZeroPredictor class and call the method to make predictions. The browser will download the CSV file called predictions.csv.

This file that you just download contains the predictions from your algorith and should be ready to submit.