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MLOps-Level-4 Infrastructure Code

With increasing demand of EdgeAI or ML in practices, this repo aims to bring high level of automation in MLOps Life cycle. This will help to set up Level-4 MLOps Infrastructure and solve lots of real world problems around.

It comes with best practices in Software Design and Architecture for ML like testing, CI/CD, Kubernetes, Github workflow and Terraform + AWS Services.

Read More about MLOps Maturity Level here

Tests

Description

Currently, it is serving the following objectives:

  1. Image Dataset: Create Dataset from the csv and store them in directories under dataset. Directory Structure will look like as shown below and each directory will contain ~1000 images
  2. Classification MLOps Pipeline: The pipeline should automatically retrain a new classification model as soon as there are significant changes in dataset. significant changes here is the changes in files of a label is more than 200.

Flow/Diagram

image

Show me Working Images !

  1. pipeline/ec2/*.jpg: Replicating complete Infrastructure in AWS EC2 using docker-compose.

    • Images show ec2-setup, security-group,setting up code and complete config view
    • Lastly, test.py to get the prediction.
    • Check the images here.

  2. pipeline/webserver/serverless/*.jpg: to showcase the deployment of Inference Server in Serverless computing

    • Images show API Gateway, security-group, setting up ECR and using Lambda to server container
    • It shows complete AWS config view.
    • Lastly, test.py to get the prediction
    • Check the images here.

  3. Kubernetes (in Progress*): pipeline/webserver/manifests/: to showcase the deployment of Inference Server in Kubernetes Cluster

    • Images show Service and Deployment Stack, Config and Env Map, and port-forward to 9696
    • It shows Kubernetes.
    • Lastly, test.py to get the prediction
    • Check the images here.

Improvements

Minor Changes (in current release)

  • CI/CD Infrastructure
  • Github Job Flags
  • pre-commit hooks
  • tox
  • pylint
  • Integration with AWS Cloud
  • AWS Serverless: API Gateway + Lambda
  • AWS EC2: Docker-compose
  • Github Issues Templates
  • Kubernetes* (in progress ...)
  • isort failing
  • Optimise req.txt
  • multiple python version in tox
  • Add TerraForm as base IaaC
  • make a Video

Upcoming Major Changes (in next release)

  • Multi Input Data sources via Kafka, MQTT and AWS Kinesis.
  • Data Validation
  • Model Validation
  • Deployment Validation
  • Async Task Handling in Redis
  • Robust CI/CD/CTraining/CDeployment Pipeline
  • Model Serving in different hardware: raspberry pi, Android, javascript, onnx, tflite
  • Auto Documentations and hosted at *.github.io
  • Add More Status batch here like code-coverage, py version, maintainability,code-style, deploy, etc

Getting Started

Dependencies

  • Docker version 20.10.17, build 100c701
  • docker-compose version 1.29.2, build 5becea4c
  • Distributor ID: Ubuntu ,Description: Ubuntu 22.04.1 LTS ,Release: 22.04 , Codename: jammy

Installing

pipeline/: all the docker services is inside this folder

pipeline/data: represent volumes used between containers

  • Run the latest version of the code with Docker and Docker Compose:

    docker-compose up -d

  • Folder Structures

.
├── data # volumes that is shared among all services
│   ├── dataset # dataset for initial base model
│   ├── mlflow # mlruns and .db file, mlflow tracking uri
│   ├── monitored_dataset # watching this repo to track any changes in dataset
│   ├── prefect # for prefect flow uri
│   ├── saved-model
│   └── state # to manage state of docker-services
├── docker-compose.yml
├── mlflow/ # folder to build mlflow-server images
├── prefect/ # folder to build prefect-server images
├── README.md
├── test.py # a python script to trigger webserver and get output
├── watcher/ # folder to build watcher images
└── webserver/ # folder to build prediction server images

84 directories, 168 files

Executing program

By default, the stack exposes the following ports:

<Ports>: <Service>
5000: MLFlow Webserver
4200: Prefect Web Server
9696: Prediction Server

Authors

Version History

  • 0.0.2 : Automation in Coding Style (Currently)
  • 0.0.1 : Initial Release

License

This project is licensed under the Saurav Solanki License - see the LICENSE.md file for details

Acknowledgments