Docker-installation.md

Docker Compose is created with redis , worker , postgis database , api and frontend all in one making it easy for development . For production it is not recommended

[DEV] Installation With Docker

1. Clone Repo

   git clone https://github.com/hotosm/fAIr.git
   

2. Get Docker Compose Installed

   If docker is not installed , Install it from here

   docker compose version
   

3. Check your Graphics

   fAIr works best with graphics card. It is highly recommended to use graphics card . It might not work with CPU only (You can setup and test from bottom of this document). Nvidia Graphics cards are tested

   You need to make sure you can see your graphics card details and can be accessed through docker by installing necessary drivers

   By following command you can see your graphics and graphics driver details & nvidia container toolkit is installed More details here

   nvidia-smi
   

4. Clonse Base Model and Create RAMP_HOME

   • Create a new folder called RAMP , outside fAIr

     mkdir ramp
     

   • Download BaseModel Checkpoint from here OR You can use basemodel from Model Ramp Baseline

     pip install gdown
     gdown --fuzzy https://drive.google.com/file/d/1YQsY61S_rGfJ_f6kLQq4ouYE2l3iRe1k/view
     

   • Clone Ramp Code

     git clone https://github.com/kshitijrajsharma/ramp-code-fAIr.git ramp-code
     

   • Unzip downloaded basemodel and move inside ramp-code/ramp

     unzip checkpoint.tf.zip -d ramp-code/ramp  
     

   • Export Env variable for RAMP_HOME Grab the file path of folder we created earlier ramp and export it as env variable

     export RAMP_HOME=/home/YOUR_RAMP_LOCATION
     

     eg : export RAMP_HOME=/home/kshitij/ramp

   • Export TRAINING_WORKSPACE Env Training workspace is the folder where fAIr will store its training files for eg :

     export TRAINING_WORKSPACE=/home/kshitij/hotosm/fAIr/trainings
     

5. Register your Local setup to OSM

   • Go to OpenStreetMap , Login/Create Account
   • Click on your Profile and Hit My Settings
   • Navigate to Oauth2 Applications
   • Register new application
   • Check permissions for Read user preferences and Redirect URI to be http://127.0.0.1:3000/authenticate/ , Give it name as fAIr Dev Local
   • You will get OSM_CLIENT_ID , OSM_CLIENT_SECRET Copy them

6. Create Env variables

   • Create a file .env in backend with docker_sample_env content

     cd backend
     cp docker_sample_env .env
     

   • Fill out the details of OSM_CLIENT_ID &OSM_CLIENT_SECRET in .env file and generate a unique key & paste it to OSM_SECRET_KEY (It can be random for dev setup)

     Leave rest of the items as it is unless you know what you are doing

   • Create .env in /frontend

     cd frontend
     cp .env_sample .env
     

     You can leave it as it is for dev setup

7. Build & Run containers

   docker compose build
   

   docker compose up
   

8. Run Migrations

   Run directly bash script :

   bash run_migrations.sh
   

   OR

   Grab API container & Open Bash

    docker exec -it api bash
   

   Once Bash is promoted hit following commands

    python manage.py makemigrations
    python manage.py makemigrations login
    python manage.py makemigrations core
    python manage.py migrate
   

9. Play and Develop

   Restart containers

   docker compose restart
   

   Frontend will be available on 5000 port , Backend will be on 8000 , Flower will be on 5500

10. Want to run your local tiles ?

    You can use titler , gdals2tiles or nginx to run your own TMS server and add following to docker compose in order to access your localhost through docker containers . Add those to API and Worker . Make sure you update the .env variable accordingly

    network_mode: "host"
    

    Example docker compose :

    backend-api:
        build:
        context: ./backend
        dockerfile: Dockerfile_CPU
        container_name: api
        command: python manage.py runserver 0.0.0.0:8000
    
        ports:
        - 8000:8000
        volumes:
        - ./backend:/app
        - ${RAMP_HOME}:/RAMP_HOME
        - ${TRAINING_WORKSPACE}:/TRAINING_WORKSPACE
        depends_on:
        - redis
        - postgres
        network_mode: "host"
    
    backend-worker:
        build:
        context: ./backend
        dockerfile: Dockerfile_CPU
        container_name: worker
        command: celery -A aiproject worker --loglevel=INFO --concurrency=1
    
        volumes:
        - ./backend:/app
        - ${RAMP_HOME}:/RAMP_HOME
        - ${TRAINING_WORKSPACE}:/TRAINING_WORKSPACE
        depends_on:
        - backend-api
        - redis
        - postgres
        network_mode: "host"
    

    Example .env after host change :

    DATABASE_URL=postgis://postgres:admin@localhost:5434/ai
    CELERY_BROKER_URL="redis://localhost:6379/0"
    CELERY_RESULT_BACKEND="redis://localhost:6379/0"