upload docker image and deploy kubernets

project-ml-microservice-kubernetes/Dockerfile

@@ -1,18 +1,21 @@
-FROM python:3.7.3-stretch
+FROM python:3.9.6
 
 ## Step 1:
 # Create a working directory
-
+WORKDIR /app
 ## Step 2:
 # Copy source code to working directory
-
+COPY app.py /app/
+COPY model_data /app/model_data
+COPY requirements.txt /app/
 ## Step 3:
 # Install packages from requirements.txt
 # hadolint ignore=DL3013
-
+RUN pip install --upgrade pip &&\ 
+    pip install --trusted-host pypi.python.org -r requirements.txt
 ## Step 4:
 # Expose port 80
-
+EXPOSE 80
 ## Step 5:
 # Run app.py at container launch
-
+CMD ["python", "app.py"]

project-ml-microservice-kubernetes/app.py

@@ -3,7 +3,7 @@
 import logging
 
 import pandas as pd
-from sklearn.externals import joblib
+import joblib
 from sklearn.preprocessing import StandardScaler
 
 app = Flask(__name__)
@@ -20,7 +20,7 @@ def scale(payload):
 
 @app.route("/")
 def home():
-    html = f"<h3>Sklearn Prediction Home</h3>"
+    html = "<h3>Sklearn Prediction Home</h3>"
     return html.format(format)
 
 @app.route("/predict", methods=['POST'])
@@ -61,11 +61,11 @@ def predict():
     # scale the input
     scaled_payload = scale(inference_payload)
     # get an output prediction from the pretrained model, clf
+    # load pretrained model as clf
+    clf = joblib.load("./model_data/boston_housing_prediction.joblib")
     prediction = list(clf.predict(scaled_payload))
     # TO DO:  Log the output prediction value
     return jsonify({'prediction': prediction})
 
 if __name__ == "__main__":
-    # load pretrained model as clf
-    clf = joblib.load("./model_data/boston_housing_prediction.joblib")
     app.run(host='0.0.0.0', port=80, debug=True) # specify port=80

project-ml-microservice-kubernetes/output_txt_files/docker_out.txt

@@ -1 +1,22 @@
-<paste log output from Docker prediction, here>
+<paste log output from Docker prediction, here>
+REPOSITORY   TAG       IMAGE ID       CREATED          SIZE                                                                                                                                                                                  
+api          latest    a05ce67946c3   7 seconds ago    1.26GB                                                                                                                                                                                
+<none>       <none>    e15b07d66fc6   14 minutes ago   1.26GB
+ * Serving Flask app "app" (lazy loading)
+ * Environment: production
+   WARNING: Do not use the development server in a production environment.
+   Use a production WSGI server instead.
+ * Debug mode: on
+ * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 194-414-639
+[2023-11-19 11:38:58,322] INFO in app: JSON payload: 
+{'CHAS': {'0': 0}, 'RM': {'0': 6.575}, 'TAX': {'0': 296.0}, 'PTRATIO': {'0': 15.3}, 'B': {'0': 396.9}, 'LSTAT': {'0': 4.98}}
+[2023-11-19 11:38:58,343] INFO in app: Inference payload DataFrame: 
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+[2023-11-19 11:38:58,354] INFO in app: Scaling Payload: 
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+172.17.0.1 - - [19/Nov/2023 11:38:58] "POST /predict HTTP/1.1" 200 -

project-ml-microservice-kubernetes/output_txt_files/kubernetes_out.txt

@@ -1 +1,33 @@
-<paste log output from Kubernetes-mediated prediction, here>
+<paste log output from Kubernetes-mediated prediction, here>
+pod/api-microservices created
+NAMESPACE     NAME                                                READY   STATUS    RESTARTS   AGE
+default       api-microservices                                   1/1     Running   0          12s
+kube-system   coredns-66bff467f8-cbzpm                            1/1     Running   0          19h
+kube-system   coredns-66bff467f8-nppkt                            1/1     Running   0          19h
+kube-system   etcd-khaled-virtual-machine                         1/1     Running   0          12m
+kube-system   kube-apiserver-khaled-virtual-machine               1/1     Running   0          19h
+kube-system   kube-controller-manager-khaled-virtual-machine      1/1     Running   1          19h
+kube-system   kube-proxy-c7lr2                                    1/1     Running   0          19h
+kube-system   kube-scheduler-khaled-virtual-machine               1/1     Running   1          19h
+kube-system   storage-provisioner                                 1/1     Running   1          19h
+Forwarding from 127.0.0.1:8000 -> 80
+Forwarding from [::1]:8000 -> 80
+
+ * Serving Flask app "app" (lazy loading)
+ * Environment: production
+   WARNING: Do not use the development server in a production environment.
+   Use a production WSGI server instead.
+ * Debug mode: on
+ * Running on http://0.0.0.0:80/ (Press CTRL+C to quit)
+ * Restarting with stat
+ * Debugger is active!
+ * Debugger PIN: 161-774-587
+[2023-11-19 19:38:23,813] INFO in app: JSON payload:
+{'CHAS': {'0': 0}, 'RM': {'0': 6.575}, 'TAX': {'0': 296.0}, 'PTRATIO': {'0': 15.3}, 'B': {'0': 396.9}, 'LSTAT': {'0': 4.98}}
+[2023-11-19 19:38:23,864] INFO in app: Inference payload DataFrame:
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+[2023-11-19 19:38:23,872] INFO in app: Scaling Payload:
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+172.17.0.1 - - [19/Nov/2023 19:38:23] "POST /predict HTTP/1.1" 200 -

project-ml-microservice-kubernetes/requirements.txt

@@ -1,14 +1,14 @@
-Click==7.0
-Flask==1.0.2
-itsdangerous==1.1.0
-Jinja2==2.10.3
-MarkupSafe==1.1.1
-numpy==1.17.2
-pandas==0.24.2
-python-dateutil==2.8.0
-pytz==2019.3
-scikit-learn==0.20.3
-scipy==1.3.1
-six==1.12.0
-Werkzeug==0.16.0
-pylint==2.4.4
+Click
+Flask
+itsdangerous
+Jinja2
+MarkupSafe
+numpy
+pandas
+python-dateutil
+pytz
+scikit-learn
+scipy
+six
+Werkzeug
+pylint

project-ml-microservice-kubernetes/run_docker.sh

@@ -4,9 +4,13 @@
 
 # Step 1:
 # Build image and add a descriptive tag
+docker build --tag=api .
 
 # Step 2: 
 # List docker images
+docker image ls
 
 # Step 3: 
 # Run flask app
+docker run -p 8000:80 api
+

project-ml-microservice-kubernetes/run_kubernetes.sh

@@ -5,14 +5,18 @@
 # Step 1:
 # This is your Docker ID/path
 # dockerpath=<>
+#list nodes
+dockerpath=longtony/api-microservices:v1.0.0
 
 # Step 2
 # Run the Docker Hub container with kubernetes
-
+minikube start
+kubectl create deploy api-microservice --image=$dockerpath
 
 # Step 3:
 # List kubernetes pods
+kubectl get nodes
 
 # Step 4:
 # Forward the container port to a host
-
+kubectl port-forward pod/api-microservice 8000:80

project-ml-microservice-kubernetes/setup_env.sh

@@ -0,0 +1,7 @@
+#!/usr/bin/env bash
+wget https://github.com/hadolint/hadolint/releases/download/v2.12.0/hadolint-Linux-x86_64
+sudo mv hadolint-Linux-x86_64 /usr/local/bin/hadolint
+sudo chmod +x /usr/local/bin/hadolint
+
+curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
+sudo install minikube-linux-amd64 /usr/local/bin/minikube

project-ml-microservice-kubernetes/upload_docker.sh

@@ -6,10 +6,13 @@
 # Step 1:
 # Create dockerpath
 # dockerpath=<your docker ID/path>
-
+dockerpath=longtony/api-microservices:v1.0.0
+docker tag api longtony/api-microservices:v1.0.0
 # Step 2:  
 # Authenticate & tag
 echo "Docker ID and Image: $dockerpath"
+docker login
 
 # Step 3:
 # Push image to a docker repository
+docker push longtony/api-microservices:v1.0.0