https://github.com/nasdin/example-ml-cicd-fullstack

Example how to run ML with DevOps
https://github.com/nasdin/example-ml-cicd-fullstack

datascience devops ml mlflow python

Last synced: about 5 hours ago
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Example how to run ML with DevOps

• Host: GitHub
• URL: https://github.com/nasdin/example-ml-cicd-fullstack
• Owner: Nasdin
• Created: 2020-04-02T13:22:01.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-01-04T15:10:07.000Z (almost 2 years ago)
• Last Synced: 2023-03-08T04:09:50.521Z (over 1 year ago)
• Topics: datascience, devops, ml, mlflow, python
• Language: HTML
• Homepage:
• Size: 33.5 MB
• Stars: 0
• Watchers: 2
• Forks: 1
• Open Issues: 23
• Metadata Files:
  • Readme: README.md

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README

        
## ML Example App

### An Example of how to deploy ML model and scale with kubernetes

## Requirements: Docker & Kubernetes

Ensure that your kubectl is connected to a kubernetes cluster

This can be done by modifying your kubectl config

with a system path pointing to KUBECONFIG

### Private Docker Registry

You must have a docker registry for the docker images to be sent to.

indicate the host to your docker registry in the file `docker-registry-address`

## Local kubernetes setup

If you dont have a kubernetes cluster, try microk8s

And then install kubectl separately:

#### Configure your kubectl with

`cat microk8s.config >> ~/.kube/config`

Then add `export KUBECONFIG=~/.kube/config`

into your bashrc

#### Installing Docker Registry

On microk8s, enable docker registry with 

`microk8s.enable registry`

## Allowing docker to push to private docker registry

Add the address of the docker private registry into your insecure-registries

E.g http://localhost:32000

For Mac/Windows:

```open the settings, goto the daemon tab and then pop in your registry’s URL in the “Insecure registries”``` 

Restart docker

#### For Ubuntu:

 

vim `/etc/docker/daemon.json`

```

{

    "insecure-registries" : ["localhost:32000"]

}

```

Restart your docker service with systemctl restart docker

## Installation instructions

Just run the included install.sh file

`sh install.sh`

## Removal

Just run the included uninstall.sh file

`sh uninstall.sh`

# Features

1. Frontend --> UI built with ReactJS to request predictions

2. Backend --> Backend built with Python Flask for 

            

            sanitize inputs

            Track & record predictions

            REST API for model serving

            Requests Model Serving API

            REST API for past predictions

            

3. Mushroom Model --> ML Pipeline built with MLFlow

 

          Website for viewing model training / track models

          WorkFlow for Training and deploying model

          Model trained as a container

          Model deployed as an isolated container