Data

Tools

Indexes

Applications

Experiments

Awesome

An open API service indexing awesome lists of open source software.

https://github.com/noahgift/python-mlops-cookbook

This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.
https://github.com/noahgift/python-mlops-cookbook

cookbook mlops

Last synced: over 1 year ago
JSON representation

This is an example of a Containerized Flask Application that can deploy to many target environments including: AWS, GCP and Azure.

Awesome Lists containing this project

README 

          
[![Python application test with Github Actions](https://github.com/noahgift/Python-MLOps-Cookbook/actions/workflows/pythonapp.yml/badge.svg)](https://github.com/noahgift/Python-MLOps-Cookbook/actions/workflows/pythonapp.yml)

[![Python application test with AWS Code Builde](https://codebuild.us-east-1.amazonaws.com/badges?uuid=eyJlbmNyeXB0ZWREYXRhIjoiSjN4ZXRiNllSOVlYWmZqQ2tyS05OOC8wUElNZ05uUFZkVDdKSHVrNzU2L2FzajJXUmlKUngxUkFvOGFTaStMNW9rOUJSS0VTWFRadVpHdWgyN1BLMjA4PSIsIml2UGFyYW1ldGVyU3BlYyI6IjB0anA4VWxkNFBvejJIcE0iLCJtYXRlcmlhbFNldFNlcmlhbCI6MX0%3D&branch=main)



## 🎓 Pragmatic AI Labs | Join 1M+ ML Engineers



### 🔥 Hot Course Offers:

* 🤖 [Master GenAI Engineering](https://ds500.paiml.com/learn/course/0bbb5/) - Build Production AI Systems

* 🦀 [Learn Professional Rust](https://ds500.paiml.com/learn/course/g6u1k/) - Industry-Grade Development

* 📊 [AWS AI & Analytics](https://ds500.paiml.com/learn/course/31si1/) - Scale Your ML in Cloud

* ⚡ [Production GenAI on AWS](https://ds500.paiml.com/learn/course/ehks1/) - Deploy at Enterprise Scale

* 🛠️ [Rust DevOps Mastery](https://ds500.paiml.com/learn/course/ex8eu/) - Automate Everything



### 🚀 Level Up Your Career:

* 💼 [Production ML Program](https://paiml.com) - Complete MLOps & Cloud Mastery

* 🎯 [Start Learning Now](https://ds500.paiml.com) - Fast-Track Your ML Career

* 🏢 Trusted by Fortune 500 Teams



Learn end-to-end ML engineering from industry veterans at [PAIML.COM](https://paiml.com)



# Python MLOps Cookbook 

This is an example of a Containerized Flask Application that can be the core ingredient in many "recipes", i.e. deploy targets..



![mlops-color](https://user-images.githubusercontent.com/58792/121539559-c6787e80-c9d3-11eb-9f48-5d25924fad25.png)

* [Read Practical MLOps Online](https://learning.oreilly.com/library/view/practical-mlops/9781098103002/)

* [Purchase Practical MLOps](https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017)



## Github Container Registery

Feel free to test my ML project:  `docker pull ghcr.io/noahgift/python-mlops-cookbook:latest`



## Assets in repo



* `Makefile`:  [View Makefile](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/Makefile)

* `requirements.txt`:  [View requirements.txt](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/requirements.txt)

* `cli.py`: [View cli.py](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/cli.py)

* `utilscli.py`: [View utilscli.py](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/utilscli.py)

* `app.py`:  [View app.py](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/app.py)

* `mlib.py`:  [View mlib.py](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/mlib.py)Model Handling Library

* `htwtmlb.csv`: [View CSV](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/htwtmlb.csv) Useful for input scaling

* `model.joblib`: [View model.joblib](https://github.com/noahgift/Python-MLOps-Cookbook/raw/781053e4d45ebeeb64ecdf2dc1b896b338530aab/model.joblib)

* `Dockerfile`:  [View Dockerfile](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/Dockerfile)

*  `Baseball_Predictions_Export_Model.ipynb`:  [Baseball_Predictions_Export_Model.ipynb](https://colab.research.google.com/drive/1mTTXGWp6jERDIBDLIQzBDvvwRciuEGcj?usp=sharing)



![Course2-Duke-Flask-Containerized](https://user-images.githubusercontent.com/58792/110816231-289cd880-8259-11eb-8ab7-45c4ef5190ad.png)



### CLI Tools



There are two CLI tools.  First, the main `cli.py` is the endpoint that serves out predictions.

To predict the height of an MLB player you use the following: ` ./cli.py --weight 180`



![predict-height-weight](https://user-images.githubusercontent.com/58792/110970118-6c5e1380-8327-11eb-95b2-aeba679c0270.png)



The second cli tool is `utilscli.py', and this performs model retraining, and could serve as the entry point to do more things.

For example, this version doesn't change the default `model_name`, but you could add that as an option by forking this repo.



`./utilscli.py retrain --tsize 0.4`



Here is an example retraining the model.

![model-retraining](https://user-images.githubusercontent.com/58792/110986838-31b2a600-833c-11eb-977f-13143d4471c7.png)



Additionally you can query the API via the CLI, allowing you to change both the host and the value passed into the API.

This is accomplished through the requests library.



`./utilscli.py predict --weight 400`



![predict-cli](https://user-images.githubusercontent.com/58792/111043970-b6bcbe80-8413-11eb-9298-5d091e5db0dd.png)



### Flask Microservice



The Flask ML Microservice can be run many ways.



#### Containerized Flask Microservice Locally



You can run the Flask Microservice as follows with the commmand: `python app.py`.



```

(.venv) ec2-user:~/environment/Python-MLOps-Cookbook (main) $ python app.py 

 * Serving Flask app "app" (lazy loading)

 * Environment: production

   WARNING: This is a development server. Do not use it in a production deployment.

   Use a production WSGI server instead.

 * Debug mode: on

INFO:werkzeug: * Running on http://127.0.0.1:8080/ (Press CTRL+C to quit)

INFO:werkzeug: * Restarting with stat

WARNING:werkzeug: * Debugger is active!

INFO:werkzeug: * Debugger PIN: 251-481-511

```



To serve a prediction against the application, run the `predict.sh`.



```

(.venv) ec2-user:~/environment/Python-MLOps-Cookbook (main) $ ./predict.sh                             

Port: 8080

{

  "prediction": {

    "height_human_readable": "6 foot, 2 inches", 

    "height_inches": 73.61

  }

}

```



#### Containerized Flask Microservice



Here is an example of how to build the container and run it locally, this is the contents of [predict.sh](https://github.com/noahgift/Python-MLOps-Cookbook/blob/main/predict.sh)



```

#!/usr/bin/env bash



# Build image

#change tag for new container registery, gcr.io/bob

docker build --tag=noahgift/mlops-cookbook . 



# List docker images

docker image ls



# Run flask app

docker run -p 127.0.0.1:8080:8080 noahgift/mlops-cookbook

```



#### Automatically Build Container via Github Actions and Push to Github Container Registery



To setup the container build process do the following.  This is also covered by Alfredo Deza in Practical MLOps book in greater detail.



```

  build-container:

    runs-on: ubuntu-latest

    steps:

    - uses: actions/checkout@v2

    - name: Loging to Github registry

      uses: docker/login-action@v1

      with:

        registry: ghcr.io

        username: ${{ github.repository_owner }}

        password: ${{ secrets.BUILDCONTAINERS }}

    - name: build flask app

      uses: docker/build-push-action@v2

      with:

        context: ./

        #tags: alfredodeza/flask-roberta:latest

        tags: ghcr.io/noahgift/python-mlops-cookbook:latest

        push: true 

    

```



![container-registry](https://user-images.githubusercontent.com/58792/111001486-d8ee0800-8351-11eb-984a-967558023cc8.png)



#### Automatically Build Container via Github Actions and Push to Dockerhub Container Registery



## Build Targets



With the project using DevOps/MLOps best practices including linting, testing, and deployment, this project can be the base to deploy to many deployment targets.



[In progress....]



### Other Tools and Frameworks



[In progress....]



#### FastAPI



* [fastapi](https://fastapi.tiangolo.com)



### AWS



#### Elastic Beanstalk



#### AWS Lambda Recipes



Install [SAM as documented here](https://docs.aws.amazon.com/serverless-application-model/latest/developerguide/serverless-getting-started-hello-world.html), AWS Cloud9 has it installed already.



You can [find the recipes here](https://github.com/noahgift/Python-MLOps-Cookbook/tree/main/recipes/aws-lambda-sam)



##### AWS Lambda-SAM Local



![sam-directory-layout](https://user-images.githubusercontent.com/58792/111075610-32277a00-84bf-11eb-9f61-41adc9f97a21.png)



##### AWS Lambda-SAM Containerized Deploy



Follow recipe in recipe section.



![sam-guided-deploy](https://user-images.githubusercontent.com/58792/111085621-7d0cb600-84ee-11eb-8405-e94ceb5d737e.png)



When deployed an easy way to verify image is via Console.



![invoke-lambda-console](https://user-images.githubusercontent.com/58792/111085839-92361480-84ef-11eb-8cdb-092dbd94e2d1.png)



A great way to test the API Endpoint is with the Cloud9 Environment:



![invoke-api-gateway](https://user-images.githubusercontent.com/58792/111086910-2b1b5e80-84f5-11eb-901e-807032b7427a.png)



Another way is the the tool "Postman":



![post-man](https://user-images.githubusercontent.com/58792/111086967-746bae00-84f5-11eb-9ca1-6315d3cfa5ed.png)



#### AWS App Runner



Watch a YouTube Walkthrough on AWS App Runner for this repo here:  https://www.youtube.com/watch?v=zzNnxDTWtXA



![mlops](https://user-images.githubusercontent.com/58792/119266194-d3196c00-bbb7-11eb-929e-77718411ffd5.jpg)



#### AWS Co-Pilot



Following setup here and then deploy project using cli

https://docs.aws.amazon.com/AmazonECS/latest/developerguide/getting-started-aws-copilot-cli.html



### GCP



#### Cloudrun (CaaS:  Container as a Service)



It is trivial (if you select project):



`gcloud config set project `



A.  Get GCP Account

B.  Checkout project

C.  `cloud run deploy` inside of project

D.  Verify it works by using `./utilscli.py`



![gcp-cloud-run](https://user-images.githubusercontent.com/58792/138346831-6d203164-e224-429f-8fb3-0d7314954697.png)



#### App Engine



#### GKE (Kubernetes)



### Azure App Services



## Production Patterns



[In progress....]



* Cached model (deploy)

* Load-testing



## DataScience Workflow



![mlb-ht-wt](https://user-images.githubusercontent.com/58792/110829008-a7980e00-8265-11eb-883d-4a87fe6f0a84.png)



This repository is focused on MLOps.  To see more about Data Storytelling, you can go to this [Github repo on Data Story Telling](https://github.com/noahgift/data-story-telling)



#### Next Steps:  Take Coursera MLOps Course



![cloud-specialization](https://user-images.githubusercontent.com/58792/121041040-650ca180-c780-11eb-956e-8d1ecb134641.png)



* [Take the Specialization](https://www.coursera.org/learn/cloud-computing-foundations-duke?specialization=building-cloud-computing-solutions-at-scale)

* [Cloud Computing Foundations](https://www.coursera.org/learn/cloud-computing-foundations-duke?specialization=building-cloud-computing-solutions-at-scale)

* [Cloud Virtualization, Containers and APIs](https://www.coursera.org/learn/cloud-virtualization-containers-api-duke?specialization=building-cloud-computing-solutions-at-scale)

* [Cloud Data Engineering](https://www.coursera.org/learn/cloud-data-engineering-duke?specialization=building-cloud-computing-solutions-at-scale)

* [Cloud Machine Learning Engineering and MLOps](https://www.coursera.org/learn/cloud-machine-learning-engineering-mlops-duke?specialization=building-cloud-computing-solutions-at-scale)