The medcatmlflow package is designed to measure metrics of and serve MedCAT models.

It uses mlflow for model tracking and serving.

It uses regression tooling within medcat to generate metrics.

Installing medcatmlflow

We've packaged the project into a docker container.

Running in development mode

docker-compose -f docker-compose-dev.yml up

Or with the -d option to run it in detached mode.

Running in production mode

In production, we want to use a specific pre-built image. That's why we use the docker-compose-prod.yml instead.

The steps are as follows:

1. Get the docker-compose-prod.yml

• Either by cloning git clone -b master --single-branch git@github.com:mart-r/medcatmlflow.git/
• Or by copying the contents of the file (i.e if github is not available)

2. [Optional] Setup configs

• [Optional] Change some of the environmental variables in docker-compose-prod.yml to suit your needs / environment
  • You can change where the models (MEDCATMLFLOW_MODEL_STORAGE_PATH) or the database (MEDCATMLFLOW_DB_URI) are saved
  • You can change the log path (MEDCATMLFLOW_LOGS_PATH) and level (MEDCATMLFLOW_LOGS_LEVEL)
  • You can change the MedCATtrainer URL (MCT_BASE_URL)
• [Optional] You can specify MedCATtrainer login details in .env

3. Run the container

• docker-compose -f docker-compose-prod.yml up -d

How to use medcatmlflow

When the service is running, you just need to go to http://localhost:8000/ (by default). You can then start uploading models and looking at the model hierarchies.