1. The training can be run using train.py file.
2. Inference script can be run using inference.py.
3. Inference Dockerfile is Dockerfile
4. BentoML is used for model serving with prometheus integrated (exposed at /metrics route)
5. Deployment manifests can be found in manifests folder
6. MLFlow is integrated for logging of models and metrics

1. Clone the repo

git clone https://github.com/sethusaim/mnist_mlops.git

2. Setup MLFlow server

Run this the seprate terminal

mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root s3://<your-bucket-name>/ --host 0.0.0.0 -p 8000

Export MLFLOW_TRACKING_URI

export MLFLOW_TRACKING_URI=http://localhost:8000/

3. To run the training script

python train.py

The training script generates bentoml dockerfile and builds and image

4. Run the inference docker image

docker run -d -p 3000:3000 mnist-mlops

On http://localhost:3000/ url, we will get swagger ui, with predict route and prometheus metrics route