Use git to clone the repository:

git clone https://github.com/unica-isde/flask-classification-2021-projects-C

And install the requirements with

pip install -r requirements.txt

Additional requirements:

• Redis
• Docker

Configure the service by editing the file config.py.

It is recommended to pre-download images and models before running the server. This is to avoid unnecessary waits for users.

Run prepare_images.py and prepare_models.py. Models will be stored in your PyTorch cache directory, while the path for the image directory can be found in the config.py file.

To run the code without containers, it is sufficient to run separately the runserver.py script, and the worker.py script. The worker will process jobs stored in the queue. In order for the queue to work, you should have redis
installed and running (specify port in config.py).

To run the service with docker, we will use docker-compose:

docker-compose build
docker-compose up

You can run multiple workers with:

docker-compose up --scale worker=2