ML for classification of mango varieties

Developing and productization of a Machine Learning model for Classification of Mango Varieties.
Gamification with client app that challenges the user to compete against the IA.

Dataset description

https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification

This dataset contains images of eight varieties of Pakistani mangoes. Automated classification and grading of harvested mangoes will facilitate farmers in delivering high-quality mangoes on time for export, and a high accuracy may be achieved using Convolutional Neural Network.

Technologies

• Python
• Tensorflow / Tensorflow Lite
• Keras
• Models: Xception, EfficientNetB2
• Numpy, Pandas, MatplotLib
• Flask and FastAPI
• Streamlit
• Docker / docker-compose
• Kubernetes / Kind
• AWS EKS
• Locust
• Pytest

demo.mp4

Development Plan:

• Setup environment
• Download Dataset
• Development -> notebook.ipynb, train.py, convert_model.py
  • Visualize images
  • Prepare dataset: train-val-test split
  • Create model Xception (imagenet coefs)
    • Add checkpoints
    • Select learning rate
    • Add inner layers
    • Add dropout regularization
    • Perform data augmentation
    • Train with larget files (299x299)
    • Test the model with test set
    • Train efficient-net-b2 for comparison
  • Final model trainning script using the full_train and test datasets
  • Model conversion to tf_lite
• Productization -> ./production/*
  • Create tf-serving image
  • Create gateway (FastAPI) image
  • Create tests for testing the images (with docker-compose)
  • Create Kubernetes yaml files (kind and EKS)
    • gateway service and deployment
    • tf-serving service and deployment
  • Create Streamlit app
  • Performance test with Locust
• Documentation

Todo:

• Documentation (code)
• Development:
  • Add L1 and L2 regularization in inner layers
  • Test with 80-10-10 folds.
• Production:
  • Automatically get the names of the input/output from the default signature
  • Find solution to use grpc with asyc/await (FastAPI)
    • https://docs.python.org/3/library/asyncio-future.html
    • https://github.com/tensorflow/serving/blob/master/tensorflow_serving/example/mnist_client.py
  • Add simple makefile

Bugs:

• Check why pytest can non import modules in unit tests of gateway. Meantime, use python unit_tests.py

Setup

Follow the instructions in SETUP.md

Clone the repo

Open a shell (e.g. Powershell) and execute:
git clone https://github.com/MarcosMJD/ml-mango-classification.git

Download the dataset

Go to https://www.kaggle.com/datasets/saurabhshahane/mango-varieties-classification
Create a Kaggle account and click Download button.
The zip file actually contains two datasets, each one in a subdirectories. We only need the Classification_dataset.
So unzip the folder Classification_dataset into the data folder of the repository. Like this:
/data/Classificacion_dataset/Anwar Ratool
/data/Classificacion_dataset/Chanusa (Black)
...

Create the environment

Open a shell (GitBash, bash or Powershell) and execute the following instructions:

Note: If there is an error when starting Powershell and the conda base environment is not activated by default (you should see (base)), try with:
powershell -executionpolicy remotesigned
or in an already launched powershell: set-executionpolicy remotesigned
To check the policy:
get-executionpolicy (default is restricted)
You may set the default execution policy at any time.

Create a conda environment (recommended):
In the root directory of the repository, execute:
conda create --name py39 python=3.9
conda activate py39 Install pip and pipenv
pip install -U pip alternatively python.exe -m pip install -U pip
pip install pipenv

To create the development environment and install dependencies, go to the sources/development directory and execute:
pipenv install --dev
Activate the environment
pipenv shell

Please, note that the production/gateway and production/fron-end have their specific environments in order to keep these environments separated from the development environment and from each other.

To check the tensorflow version and GPU devices used (if any): python
import tensorflow as tf
tf.config.list_physical_devices('GPU')
[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]

1.- Development:

jupyter notebook
In a bash go to /sources/development and ensure that the development environment is active, then start jupyter notebook with: jupyter notebook

Go the browser and open notebook.ipynb
Run all cells check the tranning of models and model evaluation. The models are saved under the ./models directory by using checkpoints, which called when the model accuracy improves for each epoch.

Finally, the final model is trained with images of size of 299x299.

The best model is selected and tested with the test dataset. Finally, the model is converted into saved_model format under production\tf-serving directory by calling the convert_model.py script.

Check the model: Go to /sources/production/tf-serving/ and run saved_model_cli show --dir mango-model --all

IMPORTANT NOTE: Before deploying the model, it is needed to set the correct input and outputs in the gateway. To do this:

• Go to the directory /sources/production/tf-serving
• Run saved_model_cli show --all --dir mango-model
• Find the line signature_def['serving_default']:
• Take the name of the input. E.G. input_2 in the line inputs['input_2']
• the same for the output: dense_1 in outputs['dense_1'] Your numbers may be different.

Edit /sources/production/gateway/gateway.py and modify input_xx and dense_xx with your numbers

Train script
The model with the best parameters will be trained by using the full_train dataset and checking its performance with the test_dataset.
Is a shell with the development environment activated, run:
python train.py
The best checkpoint is automatically selected and the model is converted into saved_model format under production\tf-serving directory by calling the convert_model.py script.

2.- Production:

Build the images:

Go to /sources/production/tf-serving/ and run:
docker build -t tf-serving-mango:v1 . Go to /sources/production/gateway/ and run:
docker build -t gateway-mango:v1 .

Test localy running services in docker containers:

Go to sources/production/gateway and activate the environment with: pipenv install --dev pipenv shell If you were in the development environment, run exit prevously, since nested environments are not permitted. Go to /sources/production/integration-tests and run:

docker-compose up
python test_tf_serving.py
python test_gateway.py
CTRL+C
docker-compose down

In both cases the results with the prediction for the sample image will be shown. Should be something similar to:
{'Anwar Ratool': 1.0, 'Chaunsa (Black)': 0.0, 'Chaunsa (Summer Bahisht)': 0.0, 'Chaunsa (White)': 5.0123284672731474e-37, 'Dosehri': 0 .0, 'Fajri': 2.4020681443702053e-25, 'Langra': 8.949817257817495e-34, 'Sindhri': 5.200761237684644e-35}

Test locally with Streamlit app "Mango game"

The application 'Mango game' is a Streamlit app that

• Shows a set of sample images as a reference
• Chooses a random image from another set of images
• Asks the user to predict the variety
• Predicts the variety by sending a request to the gateway
• Shows the winner and overall result

In order to run this app:

• In a new bash, Go to /sources/production/front-end/
• Run pipenv install and pipenv shell to activate the environment
• Run streamlit run client.py
• Go to the browser at http://localhost:8501
• Follow the instructions.

Create a local Kubernetes cluster with KinD

In a bash, go to /production/k8s/kind/ and run:
./kind create cluster --name tf-gateway
Check with:
kubectl cluster-info --context kind-tf-gateway

Load images to the cluster:
./kind load docker-image tf-serving-mango:v1 gateway-mango:v1 --name tf-gateway

Create the deployments and services:

kubectl apply -f model-deployment.yaml
kubectl apply -f model-service.yaml
kubectl apply -f gateway-deployment.yaml
kubectl apply -f gateway-service.yaml

Test gateway and its connection with tf-serving

In order to access the service (load balancer) in the cluster, we will use port forwarding, since the servicehas not been assined with an IP yet.

kubectl port-forward service/gateway 8080:80

Host port 8080 is forwarding to port 80 of the gateway service where it is listening to. Actually Port 80 of service forwards to port 9000 of container, where gunicorn is listening to.

Test the gateway service:
Ensure that the production environment is activated (the one under the production/gateway folder)
Go to production/integration-tests/ folder and run
python test_gateway.py

You may with to delete the cluster with (now, or after the performance tests):
./kind delete cluster tf-gateway

Performance tests

For some reason, I have not been able to run Locust within the virtual environment made by pipenv.
I've had to install Locust on the Anaconda environment and launch Locust from Anaconda environment (that is, not from the pipenv environment)
So if needed, use pip install locust in your conda environment (you may have already created one for this project)
Go to sources/production/integration_tests/ and execute locust -H http://localhost:8080
Be sure to have a local ML server running, whether docker-compose of with kind as explained before.
Then, go to http://localhost/8089 and enter 4 users.

Deploy to AWS EKS

Please, note that this will have associated costs to the AWS services deployed.

Update to the latest awscli version and eksctl version.

aws ecr create-repository --repository-name mango-repo

Get the "repository uri", for instance:
546106488772.dkr.ecr.eu-west-1.amazonaws.com/mango-repo

Tag your local images to point to the ECR repository:
docker tag tf-serving-mango:v1 546106488772.dkr.ecr.eu-west-1.amazonaws.com/mango-repo:tf-serving-mango-v1 docker tag gateway-mango:v1 546106488772.dkr.ecr.eu-west-1.amazonaws.com/mango-repo:gateway-mango-v1

Log into ECR with aws cli (change your account id and region accordingly):
aws ecr get-login-password --region ${var.region} | docker login --username AWS --password-stdin ${var.account_id}.dkr.ecr.${var.region}.amazonaws.com

For instance: aws ecr get-login-password --region eu-west-1 | docker login --username AWS --password-stdin 546106488772.dkr.ecr.eu-west-1.amazonaws.com

Push the images:
docker push 546106488772.dkr.ecr.eu-west-1.amazonaws.com/mango-repo:tf-serving-mango-v1 docker push 546106488772.dkr.ecr.eu-west-1.amazonaws.com/mango-repo:gateway-mango-v1

Edit gateway-deployment.yaml and tf-serving-deployment.yaml to use these your new tagged images

eksctl create cluster -f eks-config.yaml

kubectl will be contigured to work with eks automatically.

Apply the configurations:

kubectl apply -f model-deployment.yaml
kubectl apply -f model-service.yaml
kubectl apply -f gateway-deployment.yaml
kubectl apply -f gateway-service.yaml

Get the load balancer (service) external ip:
kubectl get service For instance: a5392f8c5030f410280c2db849511f09-2136527655.eu-west-1.elb.amazonaws.com Note: Wait some time for the DNS to propagate.

You may check the FastAPI by directly opening the API endpoint:
For instance: http://a5392f8c5030f410280c2db849511f09-2136527655.eu-west-1.elb.amazonaws.com/docs

Or go to /sources/production/front-end and run pipenv streamlit client-py

Introduce the url of the EKS load balancer followed by /predict
For instance, http://a5392f8c5030f410280c2db849511f09-2136527655.eu-west-1.elb.amazonaws.com/predict

Finally, delete the cluster: eksctl delete cluster --name ml-mango-eks

Useful snippets

Change kubectl context (cluster)
kubectl config use-context kind-

Get clusters
./kind get clusters

Check images loaded in a node
kubectl get nodes
Note: There should be one which is the control plane

winpty docker exec -ti <<cluster-name>-control-plane> bash
crictl  images

Delete cluster
kind delete cluster --name

Log in (bash) into a running container
winpty docker exec -it <container-id> bash

Load and send image via post with requests

import requests
url = 'http://..."
files = {'media': open('test.jpg', 'rb')}
requests.post(url, files=files)

Get image in Flask via POST

imagefile = flask.request.files.get('imagefile', '')

Convert image file from Flask to PIL
pil_image = Image.open(image)

To get the class of the highest prediction
np.array(classes)[np.argmax(preds,1)]

To evaluate a model with a dataset
X can be a keras iterator obtained from keras image data generator -> from dataframe or directory. It returns loss and metric defined in the model compilation.
model.evaluate(X)

Show debug messages with docker-compose
print('debug', flush=True)

Delete a commit
git reset --hard

Delete a file in the last commit
git reset --soft HEAD~1 (the files will be in the staged area where they can be removed or unstaged) git rm --cached <file>

Install Gunicorn and Uvicorn for multiprocess with FastAPI
pip install "uvicorn[standard]" gunicorn

Run Gunicorn within a docker container with several Uvicorn workers (Linux only)

ENTRYPOINT [ "gunicorn", "gateway:app", "--workers=4", "--worker-class=uvicorn.workers.UvicornWorker",   "--bind=0.0.0.0:9000"]

Run uvicorn for testing FastAPI
uvicorn gateway_template:app --reload
uvicorn gateway:app --reload --host 127.0.0.1 --port 8080
On Windows 0.0.0.0 will launch the server, but clients using 0.0.0.0 or 127.0.0.1 or localhost will not work.
On Windows, port 9000 is denied (permissions).

Send image with curl in multipart form data format
curl -X 'POST' 'http://localhost:8000/predict' -H 'accept: application/json' -H 'Content-Type: multipart/form-data' -F 'file=@IMG_20210630_102920.jpg;type=image/jpeg'

Get the key with the max value in a dict
print(max(x, key=x.get))

Example of simple makefile

deps:
	poetry install

dev: deps
	cd app && poetry run python main.py

lint: lint-black lint-flake lint-pycodestyle

lint-black:
	poetry run black --line-length 100 app

lint-flake:
	poetry run flake8 --max-line-length 100 app

lint-pycodestyle:
	poetry run pycodestyle --max-line-length 100 ./app