README.md

Run locally

Create a virtual environment

virtualenv env

Start the frontend Add the following .env file in the frontend directory

BACKEND_HOST=backend-service
BACKEND_PORT=8000

Install dependencies

cd frontend
pip install -r requirements.txt

python3 main.py

Start the backend Add the following .env file in the backend directory

MODEL_PATH=Rumesh/mbart-si-simp
MAX_LENGTH=700
NUM_BEAMS=5
TASK=com-sim
ENABLE_METRICS=1
REDIS_HOST=redis-service
REDIS_PORT=6379

Install dependencies

cd backend
pip install -r requirements.txt -r requirements-local.txt 

python3 main.py

Run using Docker Compose

Install Docker: Refer - https://docs.docker.com/engine/install/ubuntu/
Add the .env files to the respective directories.
From the root of the repository,

docker-compose up --build

Build new Docker images

Login to docker hub

docker login

Build and push the docker images

cd backend 
docker build -t rumeshms16/mabrt-simp:1.0.0 .
docker push rumeshms16/mabrt-simp:1.0.0

cd frontend 
docker build -t rumeshms16/simp-frontend:1.0.0 .
docker push rumeshms16/simp-frontend:1.0.0

Deploy on K8s

See k8s-resources

Deploy on minikube

Install kubectl

curl -LO "https://dl.k8s.io/release/$(curl -L -s https://dl.k8s.io/release/stable.txt)/bin/linux/amd64/kubectl"
sudo install -o root -g root -m 0755 kubectl /usr/local/bin/kubectl

Install minikube

curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube_latest_amd64.deb
sudo dpkg -i minikube_latest_amd64.deb

Install Helm

curl -fsSL -o get_helm.sh https://raw.githubusercontent.com/helm/helm/main/scripts/get-helm-3
chmod 700 get_helm.sh
./get_helm.sh

Add the grafana chart to Helm

helm repo add grafana https://grafana.github.io/helm-charts
helm repo update

Start minikube

minikube start

Run the minikube dashboard (optional)

minikube dashboard &

Add monitoring tools: Grafana, Loki, Promtail

helm upgrade --install loki grafana/loki-stack  --set grafana.enabled=true,prometheus.enabled=true,prometheus.alertmanager.persistentVolume.enabled=false,prometheus.server.persistentVolume.enabled=false --set-file extraScrapeConfigs=k8s-resources/extraScrapeConfigs.yaml --set-file prometheus.extraScrapeConfigs=extraScrapeConfigs.yaml

The --set-file flag sets the prometheus config required for prometheus to scrape metrics from the backend.
Deploy the application on k8s

helm install mbart-deploy ./helm-chart

Access Grafana: get the admin password

kubectl get secret --namespace default loki-grafana -o jsonpath="{.data.admin-password}" | base64 --decode ; echo

Port forward to access services

kubectl port-forward svc/frontend-service 5000:5000 --address=0.0.0.0 &
kubectl port-forward svc/loki-grafana 3000:80 --address=0.0.0.0 &

Deploy on GKE

todo

Model monitoring

We use Prometheus to collect metrics, Loki to collect logs, and Grafana to visualize them. todo

tests

see test