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Build machine learning apps quickly and easily with SAP BTP, Kyma and streamlit

Sumin
Advisor
Advisor
‎12-21-2022 3:43 AM
How can we easy and fast way to build machine learning applications?
And how can we share the application to other people?

This is the prototype application for today's goal. The example video is simple image classification web app using machine learning to detect banana ripeness.

Let’s find the solution with SAP BTP Kyma and streamlit!


Overview


We can quickly build machine learning application based on SAP BTP, Kyma runtime and integrate with streamline which is open-source Python library to create custom web apps for machine learning and data science.

Steps to solving ML problem


In general, the following steps are followed to solve a machine learning problem.


In this posting, we will focus on the deployment and monitoring phase using the conceptual architecture and requirements using SAP BTP Kyma and open source app framework - streamline.


 


Steps to solve machine learning problem


 

Deploy & Monitor


When deploying and monitoring machine learning projects, we can choose prototyping tools from the options listed below. Today, we will use streamlit with SAP BTP kyma runtime.

  • Jupyter Notebook

  • Streamlit; Simple Edit and Test

  • Flask: Backend + Frontend

  • Dash: Python dashboard library

  • Voila: Visualization for Jupyter Notebook

  • …


Instead of using Flask, Streamlit makes building and sharing machine learning apps quick and easy without backend development and http request (Link to streamlit page)


Application


We will use these following applications to deploy our application:





Architecture


This is technical architecture to build machine learning apps. We will use SAP BTP, Kyma runtime which is fully managed Kubernetes runtime based on the oepn-source project "Kyma". For the model container, it is assumed that you have already completed training the machine learning model. For application containers, pre-trained machine learning generates predictions.

 


Technical Architecture



Prerequisites



  • Install Docker Engine 

  • SAP BTP Kyma

  • Pre-trained model

  • Check network port accessibility: port 8501

  • requirements.txt


tensorflow==2.9.1

streamlit

numpy==1.22.3         

pillow==9.2.0

Flask==2.1.1

keras==2.9.0

scipy==1.7.3

matplot

 

Steps


There are four parts to follow in these steps to build a machine learning app in this tutorial.

  1. Create application using python

  2. Build docker

  3. SAP BTP Kyma runtime

  4. Upload yaml file



 

1. Create application using python 


We will create python application to get predictions using machine learning model when user uploads an image.

1.1 Import library


import tensorflow as tf

from PIL import Image, ImageOps

import numpy as np

import streamlit as st



st.write('''

# Banana Ripeness Detection 🍌

''')

st.write("A Image Classification Web App That Detects the Ripeness Stage of Banana")



file = st.file_uploader("", type=['jpg','png','jpeg'])

 

1.2 Prediction code using machine learning


Depending on the pre-trained model, we need to load the model and preprocess the image data.
def predict_stage(image_data,model):

    size = (224, 224)

    image = ImageOps.fit(image_data,size, Image.ANTIALIAS)

    image_array = np.array(image)

    normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1

    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)

    data[0] = normalized_image_array

    preds = ""

    prediction = model.predict(data)

    if np.argmax(prediction)==0:

        preds = f"Overripe😫"

    elif np.argmax(prediction)==1:

        preds = f"ripe😄"

    else :

        preds = f"Unripe😑"

    return preds

 

2. Build Docker 


2.1 Create a Docker container


According to the Dockerfile, docker builds images by reading the instructions. Let's create Dockerfile as following steps:
# STEP 1: Install Python base image

FROM python:3.9-slim



# Step 2: Add requirements.txt file 

COPY requirements.txt /requirements.txt

#

# Step 3:  Install required pyhton dependencies from requirements file

RUN pip install -r requirements.txt



# Step 4: Copy source code in the current directory to the container

ADD . /app



COPY app.py ./app.py

COPY ripeness.h5 ./ripeness.h5



# Step 5: Install git 

RUN apt-get update && apt-get install -y \

    build-essential \

    software-properties-common \

    git \

    && rm -rf /var/lib/apt/lists/*



# If you have git repo, you can clone your code that lives in a remote repo to WORKDIR

# RUN git clone https://github.com/streamlit/streamlit-example.git .



# Step 6: Set working directory to previously added app directory

WORKDIR /app



# # Step 7: Expose the port is running on

EXPOSE 8501



# Step 8: Run the application

ENTRYPOINT ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

2.2 Build a Docker image


When you running following command, Dockerfile build docker image. You can change name instead of banana-ml/streamlit:latest
docker build -t banana-ml/streamlit:latest .

2.3 Run the Docker container


We can run the container by executing:
docker run -p 8501:8501 banana-ml/streamlit:latest

2.4 Docker Push


We can push our Docker image into our Docker Hub repository by executing:
docker push banana-ml/streamlist:latest


 

3. SAP BTP Kyma runtime


3.1 Create namespace from SAP BTP Kyma


In this tutorial, we create namespace with banana-ml from SAP BTP kyma. 


Create NamespaceSave Namespace


 

3.2 Create yaml file


We will deploy yaml file including Kind:Deployment, Service and APIRule in the same file - k8s/streamlit.yaml .

3.2.1 Deployment


apiVersion: apps/v1

kind: Deployment

metadata:

  name: streamlit-deployment

  labels:

    app: streamlit

spec:

  replicas: 1

  selector:

    matchLabels:

      app: streamlit

  template:

    metadata:

      labels:

        app: streamlit

    spec:

      containers:

        - name: streamlit

          image: banana-ml/streamlit:latest

          imagePullPolicy: Always

          ports:

            - containerPort: 8501

          livenessProbe:

            httpGet:

              path: /healthz

              port: 8501

              scheme: HTTP

            timeoutSeconds: 1

          readinessProbe:

            httpGet:

              path: /healthz

              port: 8501

              scheme: HTTP

            timeoutSeconds: 1

          resources:

            limits:

              cpu: 1

              memory: 2Gi

            requests:

              cpu: 100m

              memory: 745Mi


3.2.2 Service


---

apiVersion: v1

kind: Service

metadata:

  name: streamlit-service

spec:

  type: LoadBalancer

  selector:

    app: streamlit

  ports:

    - name: streamlit-port

      protocol: TCP

      port: 8501

      targetPort: 8501


3.2.3 API Rule


API rule allows us for exposing a service externally. We will get URL like https://bananastreamlit-api.c xxxxxxx.kyma.shoot.live.k8s-hana.ondemand.com .
---

apiVersion: gateway.kyma-project.io/v1alpha1

kind: APIRule

metadata:

  name: bananastreamlit-api-rule

  namespace: banana-ml

  labels:

    app.kubernetes.io/name: bananastreamlit

spec:

  gateway: kyma-gateway.kyma-system.svc.cluster.local

  service:

    name: streamlit-service

    port: 8501

    host: bananastreamlit-api

  rules:

    - accessStrategies:

      - config: {}

        handler: allow 

      methods:

      - GET

      - POST

      path: /.*

 

4. Upload yaml file


4.1 Export kube configuration


We can download from cluster overview using Download Kubeconfig
export KUBECONFIG=/Users/.kube/kubeconfig.yaml


Download Kubeconfig



4.2 Upload yamlfile


kubectl apply -f k8s/streamlit.yaml

4.3 Check deployment


Please wait until the deployment is successful from the Healthy Resources.


Healthy Resources



4.4 Check and Test with API Rules


You can get the URL to access machine learning application.


API Rules




Congratulations 👏👏👏

Now we can build our cool machine learning app and we can share app to other people as well✨ I hope this posting is helpful for you!

 

Reference



 
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