MlOPS learning project

Collection of readmes regarding used tools.

Diagram

Mlflow tracking UI

Deploys MLFLOW tracking UI to AWS cloud. Dependencies:

• AWS App Runner to host tracking UI
• AWS RDS Postgre as mflow persistent layer
• AWS S3 as storage for storing runs artifacts

Deploy to AWS

1. Configure AWS credentails - https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-configure.html
2. Execute next commands:

> cd terraform/apps/mlflow
> terraform init
> terraform apply

This will create RDS instance, s3 bucket and App Runner serivce from Mlflow image with public endpoint (with basic-auth)

Scalr

Scalr is a remote operations backend for Terraform and OpenTofu (OTF). Scalr executes runs and stores state centrally allowing for easy collaboration across your organization.

Deployment of mlflow via Scalr

Optional

You can create billing alarm for AWS via next steps:

> cd terraform/apps/cloudwatch
> terraform init
> terraform apply

This will create billing alarm for 3.33$ usage per day (approx. 100$ per month)

Local setup

You can spin up local instance of mlflow via docker-compose. In order to do that execute next command from the root directory of the repo:

docker-compose --file local-dev/local-docker-compose.yaml  up

This method does not store any persistant data, so it is only applicable for local dev/test purposes.

Run training

To run training of the model:

1. Export neccesary envirement variables for the train.py:

export MLFLOW_TRACKING_URI="Change me"
export MLFLOW_EXPERIMENT_NAME="Change me"
export MLFLOW_TRACKING_USERNAME="Change me"
export MLFLOW_TRACKING_PASSWORD="Change me"

export AWS_ACCESS_KEY_ID="Change me"
export AWS_SECRET_ACCESS_KEY="Change me"

2. Run train.py:

python train.py

The experiment will be track by mlflow, the artifact will be upload to S3 bucket.

Deployment

Locally

1. Enter needed env variables for running model (via deployment/env file)
2. Go to deployment folder:

> cd deployment

3. Run docker build:

> docker build -f Dockerfile -t model .

3. Run docker container:

docker run -it --rm -p 5001:80 --env-file env  model

This will serve the model via FastAPI. To trigger predict endpoint go to localhost:5001/docs and run request to /predict endpoint via OpenAPI UI.