Skip to content

Latest commit

 

History

History
153 lines (122 loc) · 8.54 KB

DEVELOPMENT.md

File metadata and controls

153 lines (122 loc) · 8.54 KB
Raw

Development

This document gives an overview of the code contained in this monorepo and the recommended development setup.

Develop with Docker (recommended quickstart)

This is the simplest configuration for developers to start with.

  1. Make a copy of template.env and call it .env.
  2. Set the environment variables in .env.
  3. Run docker compose up to start the Django development server and Celery worker, plus all backing services like PostGIS, Redis, RabbitMQ, etc.
  4. Run docker compose run --rm django poetry run django-admin migrate to apply database migrations.
  5. Run docker compose run --rm django poetry run django-admin loaddata lookups to initialize your database with required data.
  6. Create an account for the Django admin (http://localhost:8000/admin) by running docker compose run --rm django poetry --directory django run django-admin createsuperuser
  7. If running the docker compose by default a client development server should be started at http://localhost:8080/
  8. If doing local Client Development, start the client development server: 
    cd vue
npm install
npm run dev
    The server will be started at http://localhost:3000 as to not conflict with the docker compose development service When finished, use Ctrl+C

Develop Natively (advanced)

This configuration still uses Docker to run attached services in the background, but allows developers to run Python code on their native system.

Initial Setup

  1. Make a copy of template.env and call it .env.
  2. Set the environment variables in .env.
  3. Run docker compose -f ./docker-compose.yaml up -d
  4. Install Python 3.11
  5. Install psycopg2 build prerequisites
  6. Install Poetry
  7. Run poetry --directory django install
  8. Run the following command to configure your environment: source ./dev/export-env.sh dev/.env.docker-compose-native ./dev/export-env.sh .env
  9. Create an account for the Django admin (http://localhost:8000/admin) by running poetry --directory django run django-admin createsuperuser

Run Application

  1. Ensure docker compose -f ./docker-compose.yaml up -d is still active
  2. Run:
    1. source ./dev/export-env.sh dev/.env.docker-compose-native
    2. source ./dev/export-env.sh .env
    3. poetry run --directory django django/src/manage.py migrate
    4. poetry run --directory django django/src/manage.py loaddata lookups
    5. poetry run --directory django django/src/manage.py runserver
  3. Run in a separate terminal:
    1. source ./dev/export-env.sh
    2. poetry run --directory django celery --app rdwatch.celery worker --loglevel INFO --without-heartbeat
  4. Run in another separate terminal:
    1. source ./dev/export-env.sh
    2. poetry run --directory django celery --app rdwatch.celery beat --loglevel INFO
  5. When finished, run docker compose stop
  6. To destroy the stack and start fresh, run docker compose down
    1. Note: this command does not destroy docker volumes, such as those associated with the postgresql and minio services. To destroy those as well, run docker compose down -v.

A note on database migrations

Note that database migrations are not run automatically. Anytime a new migration is introduced, you must run the following command to apply it:

poetry --directory django run django-admin migrate

Type support for ".vue" imports in VS Code

Enable "takeover mode" for Volar.

  1. Disable built-in TypeScript extension:
    1. Open the Command Palette (⌘ ⇧ P or Ctrl Shift P) and run >Extensions: Show Built-in Extensions command
    2. Find "TypeScript and JavaScript Language Features", right click and select "Disable (Workspace)"
  2. Reload VS Code

Stack

The key software used to build the application.

Django

A single Django application (rdwatch) for the backend. Source code is in the "django" folder.

Vue

The Vue-based SPA frontend. Source code is in the "vue" folder.

Services

Services the application requires.

Ingesting Data

Loading Ground Truth Data

Within the ./scripts directory is a python script named loadGroundTruth.py. This file can be used in conjunction with the ground truth annotaitons located in the annotation Repo: Annotation Repo Running a command like python loadGroundTruth.py ~/AnnotationRepoLocation --skip_region will load all of the annotations for the ground truth while skipping the regions.

Loading Single Model Runs

Within the ./scripts directory is a python script named loadModelRuns.py. This can be used to load a folder filled with geojson data into the system by using a command like:

python loadModelRuns.py 'KR_0001' "./site_models/KR_R001_*.geojson" --title Test_Eval_12 --performer_shortcode 'KIT' --eval_num 12 --eval_run_num 0

Within this python file at the top is the rgd_endpoint variable which needs to be set to the server URL and port for where RGD is hosted. By default this assumes running locally with http://localhost:8000 Be sure that the system is up and running before running the commands. The above command will load the data in the site_models/KR_R001 files and give it the title 'Test_Eval_12'. The eval_num and eval_run_num aren't required unless the scoring database is going to be connected to the system. Within the script there is

Scoring

The Metrics and Test Framework can be used in addition with RGD to display scores from results. In development mode a scoring Database is automatically initialized at URI: postgresql+psycopg2://scoring:secretkey@localhost:5433/scoring

To score data:

  • Clone the Metrics and Test Framework repo.
  • In the Metrics and Test Framework repo:
    • Copy the alembic_example.ini to alembic.ini and set the sqlalchemy.url = postgresql+psycopg2://scoring:secretkey@localhost:5433/scoring
    • Run pip install -e . to install the metrics-and-test-framework package
    • Run alembic upgrade head to initialize the scoring database schema
    • Execute the scoring code from inside the metrics and test framework:
  python -m iarpa_smart_metrics.run_evaluation \
               --roi KR_R001 \
               --gt_dir ../annotations/site_models/ \
               --rm_path ../KR_R001/region_model.geojson \
               --sm_dir ../KR_R001/site_models/ \
               --output_dir ../KR_R001/output \
               --eval_num 12 \
               --eval_run_num 0 \
               --performer kit \
               --no-viz \
               --no-viz-detection-table \
               --no-viz-comparison-table \
               --no-viz-associate-metrics \
               --no-viz-activity-metrics \
               --sequestered_id KR_R001 \
               --db_conn_str postgresql+psycopg2://scoring:secretkey@localhost:5433/scoring
  • the rm_path and sm_dir shgould be your test annotaitons.
  • gt annotations can be retrieved from the Annotation Repo
  • be sure to set the val_num and eval_run_num and remember them when ingesting data into RGD. The region, eval_num, eval_run_num and performer are used to connect data loaded in RGD to the scoring data.