Python library for handling containerized PHT trains
Project description
PHT Station Backend
This project contains the implementation of the station API, workers for training models, as well as the configuration
for the station airflow instance and related workers. A FastAPI REST API als well as a command line tool for train and
station management can be found in the station
directory.
Setup development environment
Checkout the repository and navigate to project directory.
git clone https://github.com/PHT-Medic/station-backend.git
cd station-backend
Prerequisites
Make sure the following ports required for the services are open or change the port mappings in the docker-compose.yml
file.
- Postgres:
5432
- Redis:
6379
- Minio:
9000
&9001
(Console) - Airflow:
8080
- Blaze FHIR server:
9090
- API:
8000
Start services
Start the services for development using docker-compose.
docker compose up -d
Check the logs of the services to see if everything is running as expected.
docker compose logs -f
Configure station config for running the API in development mode
Copy the station_config.yml.tmpl
file in the root directory to station_config.yml
and adjust the values (especially
configuring addresses and credentials for the central api)
# Configure authentication for central services
central:
api_url: ""
# Robot credentials for accessing central services, these can be obtained in the central UI
robot_id: "central-robot-id"
robot_secret: "central-robot-secret"
private_key: "/path/to/private_key.pem"
# optional password for private key
private_key_passphrase: "admin"
######### some lines omitted #########
# Configures the address and credentials for the central container registry
registry:
address:
password:
user:
project:
Install python dependencies
Install dependencies using poetry. This will also create a virtual environment for the project.
poetry install --with dev
Run the station API
To run the station API with hot reloading, run the following command:
poetry run python station/app/run_station.py
Project details
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