https://github.com/froukje/mnist-deployment

Deploy App for MNIST Classification using TorchServe and Flask
https://github.com/froukje/mnist-deployment

deep-learning docker docker-compose flask pytorch torchserve

Last synced: 4 months ago
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Deploy App for MNIST Classification using TorchServe and Flask

• Host: GitHub
• URL: https://github.com/froukje/mnist-deployment
• Owner: froukje
• Created: 2022-07-16T17:05:09.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2022-07-16T17:25:00.000Z (almost 4 years ago)
• Last Synced: 2025-10-09T20:08:55.528Z (9 months ago)
• Topics: deep-learning, docker, docker-compose, flask, pytorch, torchserve
• Language: Python
• Homepage:
• Size: 12.7 KB
• Stars: 3
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# MNIST Web app

This repository contains a simple web app for making predictions using the MNIST dataset. The purpose is to understand torchserve and how to combine it with a webservice. The model for making the predictions has been downloaded from https://github.com/pytorch/serve/tree/master/examples/image_classifier/mnist/mnist_cnn.pt

## Torchserve only

In the repo https://github.com/pytorch/serve/tree/master/examples/image_classifier/mnist/ an example how to serve this model using a customized handler based on the ImageClassifier. I used the BaseHandler to get familiar with it. The handler script is called mnist_handler_base.py and I used this post to write it: https://towardsdatascience.com/deploy-models-and-create-custom-handlers-in-torchserve-fc2d048fbe91. The base handler script can be found here: https://github.com/pytorch/serve/blob/master/ts/torch_handler/base_handler.py

Create the ```.mar``` file, which contains all information to deploy the model (in order to execute this some dependencies need to be installed)

 ```torch-model-archiver --model-name mnist --version 2.0 --model-file mnist.py --serialized-file mnist_cnn.pt --handler mnist_handler_base.py --force```

Move the created file into the ```model-store``` folder

 ```mv mnist.mar model-store/```

Use docker to serve the model

```

docker run --rm -it -p 8080:8080 -p 8081:8081 --name mar -v $(pwd)/model-store:/home/model-server/model-store -v $(pwd):/home/model-server/examples pytorch/torchserve:latest torchserve --start --model-store model-store --models mnist=mnist.mar

```

Check which models are registered:

```curl http://127.0.0.1:8081/models/```

 output

```

{

  "models": [

    {

      "modelName": "mnist",

      "modelUrl": "mnist.mar"

    }

  ]

}

```

Make predictions: 

```curl http://127.0.0.1:8080/predictions/mnist -T 3.png```, output  ```3```

## Web-App

now we want to combine the above with a simple web app to make the predictions from an uploaded image:

* in folder ```deployment``` is a Dockerfile from ```pytorch/torchserve:latest```, which only copies the ```.mar``` file to the ```model-store``` folder. nd starts torchserve (Note: change latest to other version)

    * build the image: ```build -t torchserve-mar:v1```

* In subfolder ```app```

    * create file ```app.py``` and subfolders ```templates``` and ```static```

    * ```templates``` contains html content of app

    * ```static``` is for saveig the uploaded images

    * ```app.py``` is script that creates the app to make predictions

    * in folder ```app``` create Dockerfile to run the app

    * build the image ```docker-build -t app:v1```

* in folder ```deployment``` use ```docker-compose.yaml``` to run both services

* start services with ```docker-compose up```