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Simple ML project used for the Release Engineering for Machine Learning Applications MSc course taught at Delft University of Technology

Jupyter Notebook 84.98% Python 14.64% Shell 0.08% Dockerfile 0.30%

remla-project's Introduction

REMLA 2022 Group 8

This project was conducted as part of the course Release Engineering for Machine Learning Applications (REMLA) taught at the Delft University of Technology by Prof. Luís Cruz and Prof. Sebastian Proksch.

Installation

Run: pip install -e .[extra]

Linting

Find the commands per tool below:

mllint: mllint .
flake8: flake8 .
pylint: pylint src && pylint tests
mypy: mypy .

Experiment tracking with Weights and Biases

Loging to Weights and Biases using the CLI tool:

wandb login

Run the code and the results will be logged to wandb. Note that you need to be part of the remla-2022-group-8 entity to have access to already pushed artifacts. Contact one of the authors to be added to the organization.

Reproducibility pipeline

To add a new model to the DVC reproducibility pipeline, add a class that extends BaseModel in the models package.

Add the name of the module, the name of the class and a config name in the params.yaml file. Note: the config field is not used right now.

Grafana Dashboard

Build the service located in the root of the repository: docker-compose build

Run the service: docker-compose up

Kubernetes

Setup

Create the namespace where we will run the deployment

kubectl create ns remla

Install the nginx ingress constroller and set prometheus config

helm upgrade -i ingress-nginx ingress-nginx/ingress-nginx --namespace ingress-nginx --set controller.metrics.enabled=true --set controller.podAnnotations."prometheus\.io/scrape"=true --set controller.podAnnotations."prometheus\.io/port"=5000

Install flagger and prometheus

helm upgrade -i flagger flagger/flagger --namespace ingress-nginx --set prometheus.install=true --set meshProvider=nginx

Install the flagger load tester

helm upgrade -i flagger-loadtester flagger/loadtester --namespace=remla

Deploy

Create deployment out of the resources in k8s folder

kubectl apply -f .\k8s\

Change the image of the remla-deployment, which trigger the canary update. Currently the deployment runs the farsene/remla-v1 image. The following command updates to a new version.

kubectl set image deployment/remla-deployment remla=farsene/remla-v2 -n remla

Command to open the canary gate to let them fly

Enter the load tester and approve the canary rollout by opening the gate

kubectl -n remla exec -it flagger-loadtester-7c47f949d-j8wt7 sh
curl -d '{"name": "remla","namespace":"remla"}' http://localhost:8080/gate/open

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