Installation and Set Up
Building the models
First, you must install the various models:
# SET $HOME/.aws/credentials to proper key and secret.
# Install MAAS
git clone https://github.com/WorldModelers/ModelService.git
cd ModelService/REST-Server
conda create -n maas_env python=3.7 pip jupyter -y
source activate maas_env
# Install GDAL dependencies
sudo add-apt-repository ppa:ubuntugis/ppa && sudo apt-get update
sudo apt-get update
sudo apt-get install python3.6-dev
sudo apt-get install gdal-bin
sudo apt-get install libgdal-dev
sudo apt install python3-rtree
# GDAL bindings
export CPLUS_INCLUDE_PATH=/usr/include/gdal
export C_INCLUDE_PATH=/usr/include/gdal
pip install -r requirements.txt
# Install UAZ Concept Mapping Service
# This builds the Docker container and runs it and will take a while since
# it requires building Eidos
cd ../UAZ-Integration
./maas_install.sh
# Install FSC Model. You will get prompted for github credentials.
# This will take quite a bit of time.
cd ../FSC-Integration/
./maas_install.sh
# Install Kimetrica Model.
cd ../../Kimetrica-Integration/
# Please look at this file as you must set the CKAN creds manually right now...
./maas_install.sh
# Install DSSAT
cd ../DSSAT-Integration
./maas_install.sh
# Install Atlas
cd ../Atlas-Integration
./maas_install.sh
# Install Yield Anomalies Model
cd ../Yield-Anomalies-Integration
./maas_install.sh
# Install World Population Africa Model
cd ../World-Population-Integration
./maas_install.sh
# Install Flood Severity Index
cd ../Flood-Index-Integration
./maas_install.sh
# Install PIHM
cd ../PIHM-Integration
./maas_install.sh
# Install APSIM and G-Range
cd ../CSIRO-Integration
./maas_install.sh
Redis Set-up
In production, MaaS relies on an AWS hosted Redis instance. For development, you can download a sample Redis dump with:
wget https://world-modelers.s3.amazonaws.com/data/redis-11-11-2019.rdb]
mv redis-11-11-2019.rdb dump.rdb
Then you can turn on Redis using the docker-compose file in the base of this repository. You can do this with docker-compose up -d (assumes you have Docker installed and running). This will run Redis on port 6379.
RQ Worker
Models are executed asynchronously using RQ. This requires that an RQ worker is running. You can do this in a screen session from the REST-Server directory with:
screen -S RQ
This will generate a screen session named RQ. You should then run the worker with:
rq worker high default low -u redis://localhost:6379/0
This will set the worker to listen to the queues called high and low as well as the default queue using a Redis instance running at localhost. For production this should be replaced with the URL for the production Redis instance.
DB Setup
You should also ensure that the database is appropriately set up. To do that, you should execute the SQL scripts located in db/db-setup against the database.