Circleci project setup

.circleci/config.yml

@@ -0,0 +1,49 @@
+version: 2.1
+
+jobs:
+  build:
+    docker:
+      - image: circleci/python:3.7.3-stretch
+
+    working_directory: ~/repo
+
+    steps:
+      - checkout
+
+      # Set up Docker environment
+      - setup_remote_docker
+
+      # Download and cache dependencies
+      - restore_cache:
+          keys:
+          - v1-dependencies-{{ checksum "requirements.txt" }}
+          # fallback to using the latest cache if no exact match is found
+          - v1-dependencies-
+
+      - run:
+          name: install dependencies
+          command: |
+            ls
+            cd $(eval echo "$CIRCLE_WORKING_DIRECTORY/project-ml-microservice-kubernetes")
+            python3 -m venv venv
+            . venv/bin/activate
+            pip install -r requirements.txt
+      - save_cache:
+          paths:
+            - ./venv
+          key: v1-dependencies-{{ checksum "requirements.txt" }}
+
+      # run tests!
+      - run:
+          name: run tests
+          command: |
+            cd $(eval echo "$CIRCLE_WORKING_DIRECTORY/project-ml-microservice-kubernetes")
+            . venv/bin/activate
+            make test
+      # run lints!
+      - run:
+          name: run lint
+          command: |
+            cd $(eval echo "$CIRCLE_WORKING_DIRECTORY/project-ml-microservice-kubernetes")
+            . venv/bin/activate
+            make lint

.gitignore

@@ -0,0 +1,2 @@
+*.devops/
+*.env

project-ml-microservice-kubernetes/.circleci/config.yaml

@@ -0,0 +1,48 @@
+version: 2.1
+
+jobs:
+  build:
+    docker:
+      - image: circleci/python:3.7.3-stretch
+
+    working_directory: ~/repo
+
+    steps:
+      - checkout
+
+      # Set up Docker environment
+      - setup_remote_docker
+
+      # Download and cache dependencies
+      - restore_cache:
+          keys:
+          - v1-dependencies-{{ checksum "requirements.txt" }}
+          # fallback to using the latest cache if no exact match is found
+          - v1-dependencies-
+
+      - run:
+          name: install dependencies
+          command: |
+            cd $(eval echo "$CIRCLE_WORKING_DIRECTORY/DevOps_Microservices/project-ml-microservice-kubernetes")
+            python3 -m venv venv
+            . venv/bin/activate
+            pip install -r requirements.txt
+      - save_cache:
+          paths:
+            - ./venv
+          key: v1-dependencies-{{ checksum "requirements.txt" }}
+
+      # run tests!
+      - run:
+          name: run tests
+          command: |
+            cd $(eval echo "$CIRCLE_WORKING_DIRECTORY/DevOps_Microservices/project-ml-microservice-kubernetes")
+            . venv/bin/activate
+            make test
+      # run lints!
+      - run:
+          name: run lint
+          command: |
+            cd $(eval echo "$CIRCLE_WORKING_DIRECTORY/DevOps_Microservices/project-ml-microservice-kubernetes")
+            . venv/bin/activate
+            make lint

project-ml-microservice-kubernetes/Dockerfile

@@ -2,17 +2,22 @@ FROM python:3.7.3-stretch
 
 ## Step 1:
 # Create a working directory
+WORKDIR /app
 
 ## Step 2:
 # Copy source code to working directory
+COPY requirements.txt ./
 
 ## Step 3:
 # Install packages from requirements.txt
 # hadolint ignore=DL3013
+RUN pip install --no-cache-dir -r requirements.txt
 
+COPY . .
 ## Step 4:
 # Expose port 80
+EXPOSE 80
 
 ## Step 5:
 # Run app.py at container launch
-
+CMD [ "python", "app.py" ]

project-ml-microservice-kubernetes/Makefile

@@ -9,6 +9,7 @@ setup:
 	# Create python virtualenv & source it
 	# source ~/.devops/bin/activate
 	python3 -m venv ~/.devops
+	source ~/.devops/bin/activate
 
 install:
 	# This should be run from inside a virtualenv
@@ -22,8 +23,10 @@ test:
 
 lint:
 	# See local hadolint install instructions:   https://github.com/hadolint/hadolint
+	wget -O ./hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64 &&\
+	chmod +x ./hadolint
 	# This is linter for Dockerfiles
-	hadolint Dockerfile
+	./hadolint Dockerfile
 	# This is a linter for Python source code linter: https://www.pylint.org/
 	# This should be run from inside a virtualenv
 	pylint --disable=R,C,W1203,W1202 app.py

project-ml-microservice-kubernetes/README.md

@@ -1,4 +1,5 @@
-<include a CircleCI status badge, here>
+
+[![lx0612](https://circleci.com/gh/lx0612/DevOps_Microservices.svg?style=svg)](https://app.circleci.com/pipelines/github/lx0612/DevOps_Microservices)
 
 ## Project Overview
 
@@ -44,7 +45,24 @@ source .devops/bin/activate
 
 ### Kubernetes Steps
 
-* Setup and Configure Docker locally
-* Setup and Configure Kubernetes locally
-* Create Flask app in Container
-* Run via kubectl
+1. **Setup and Configure Docker**
+   - Go to the Docker Desktop website at https://www.docker.com/products/docker-desktop/ and follow the instructions to install Docker Desktop.
+   - Verify : `docker --version`.
+
+2. **Setup and Configure Kubernetes**
+   - For Windows users, the recommended way is to use Docker Desktop. Open Docker Desktop, go to Settings, navigate to Kubernetes, and check "Enable Kubernetes."
+   - Verify the Kubernetes configuration by running: `kubectl version --output json`.
+
+3. **Create Flask App in a Container**
+   - Build the Docker image for the Flask app using the following command: `docker build --tag udacity-pj4:v1.0.0 .`
+   - Run the container with the created image: `docker run -d --rm -p 8000:80 udacity-pj4:v1.0.0`
+
+4. **Deploy Flask App via Kubernetes**
+   - Create an environment file `.env` and set variable `DOCKER_PASSWORD=<your-docker-hub-pw>`.
+   - run: `source .env`.
+   - Export your Docker Hub ID using: `export docker_path=<your-docker-hub-id>`.
+   - Log in to Docker Hub to push the image: `echo "$DOCKER_PASSWORD" | docker login --username $docker_path --password-stdin`.
+   - Tag and push the Docker image to Docker Hub: `docker image tag udacity-pj4:v1.0.0 $docker_path/udacity-pj4:v1.0.0 && docker image push $docker_path/udacity-pj4:v1.0.0`.
+   - Create a Kubernetes deployment: `kubectl create deploy udacity-pj4 --image="$docker_path/udacity-pj4:v1.0.0"`.
+   - Check whether the pod is in the READY state: `kubectl get pods`.
+   - Wait pods ready, forward the port to access the Flask app locally: `kubectl port-forward deployment.apps/udacity-pj4 8000:80`.

project-ml-microservice-kubernetes/app.py

@@ -1,4 +1,8 @@
-from flask import Flask, request, jsonify
+from flask import (
+    Flask,
+    request,
+    jsonify
+    )
 from flask.logging import create_logger
 import logging
 
@@ -63,6 +67,7 @@ def predict():
     # get an output prediction from the pretrained model, clf
     prediction = list(clf.predict(scaled_payload))
     # TO DO:  Log the output prediction value
+    LOG.info(f'prediction value: {prediction}')
     return jsonify({'prediction': prediction})
 
 if __name__ == "__main__":

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

@@ -1 +1,10 @@
-<paste log output from Docker prediction, here>
+[2023-11-18 17:43:35,145] 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}}
+[2023-11-18 17:43:35,157] INFO in app: Inference payload DataFrame: 
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+[2023-11-18 17:43:35,165] INFO in app: Scaling Payload: 
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+[2023-11-18 17:43:35,168] INFO in app: prediction value: [20.35373177134412]
+172.17.0.1 - - [18/Nov/2023 17:43:35] "POST /predict HTTP/1.1" 200 -

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

@@ -1 +1,29 @@
-<paste log output from Kubernetes-mediated prediction, here>
+<paste log output from Kubernetes-mediated prediction, here>
+### K8s:
+voclabs:~/environment/DevOps_Microservices/project-ml-microservice-kubernetes (master) $ kubectl get pods --all-namespaces                                                                   
+NAMESPACE     NAME                               READY   STATUS    RESTARTS      AGE
+kube-system   coredns-5dd5756b68-rt42b           1/1     Running   0             21m
+kube-system   etcd-minikube                      1/1     Running   0             21m
+kube-system   kube-apiserver-minikube            1/1     Running   0             21m
+kube-system   kube-controller-manager-minikube   1/1     Running   0             21m
+kube-system   kube-proxy-qgfbj                   1/1     Running   0             21m
+kube-system   kube-scheduler-minikube            1/1     Running   0             21m
+kube-system   storage-provisioner                1/1     Running   1 (20m ago)   21m
+voclabs:~/environment/DevOps_Microservices/project-ml-microservice-kubernetes (master) $ ./run_kubernetes.sh                                                                                 
+lx96/udacity-pj4:v1.0.0
+deployment.apps/udacity-pj4 created
+NAME                           READY   STATUS              RESTARTS   AGE
+udacity-pj4-7759b46ccd-hv79b   0/1     ContainerCreating   0          0s
+error: unable to forward port because pod is not running. Current status=Pending
+voclabs:~/environment/DevOps_Microservices/project-ml-microservice-kubernetes (master) $ kubectl port-forward deployment.apps/udacity-pj4 --address 0.0.0.0 8000:80
+Forwarding from 0.0.0.0:8000 -> 80
+Handling connection for 8000
+
+### Call API
+voclabs:~/environment/DevOps_Microservices/project-ml-microservice-kubernetes (master) $ ./make_prediction.sh 
+Port: 8000
+{
+  "prediction": [
+    20.35373177134412
+  ]
+}

project-ml-microservice-kubernetes/run_docker.sh

@@ -3,10 +3,16 @@
 ## Complete the following steps to get Docker running locally
 
 # Step 1:
+image_name=udacity-pj4
+image_tag=v1.0.0
+container_name=udacity-pj4
+
 # Build image and add a descriptive tag
+docker build -t $image_name:$image_tag .
 
-# Step 2: 
 # List docker images
+docker image list
 
-# Step 3: 
 # Run flask app
+docker run --name $container_name -p 8000:80 --rm $image_name:$image_tag
+

project-ml-microservice-kubernetes/run_kubernetes.sh

@@ -1,18 +1,26 @@
 #!/usr/bin/env bash
 
 # This tags and uploads an image to Docker Hub
+image_name=udacity-pj4
+image_tag=v1.0.0
+deployment_name=udacity-pj4
 
 # Step 1:
 # This is your Docker ID/path
-# dockerpath=<>
+docker_path=lx96
 
+echo $docker_path/$image_name:$image_tag
 # Step 2
 # Run the Docker Hub container with kubernetes
+kubectl create deploy $deployment_name --image=$docker_path/$image_name:$image_tag
 
 
 # Step 3:
 # List kubernetes pods
+kubectl get pods
 
 # Step 4:
 # Forward the container port to a host
+kubectl port-forward deployment.apps/$deployment_name --address 0.0.0.0 8000:80
+
 

project-ml-microservice-kubernetes/upload_docker.sh

@@ -1,15 +1,27 @@
-#!/usr/bin/env bash
-# This file tags and uploads an image to Docker Hub
+#!/bin/bash
 
-# Assumes that an image is built via `run_docker.sh`
+image_name=udacity-pj4
+image_tag=v1.0.0
 
-# Step 1:
-# Create dockerpath
-# dockerpath=<your docker ID/path>
+# Create docker_path
+docker_path=lx96
 
-# Step 2:  
-# Authenticate & tag
-echo "Docker ID and Image: $dockerpath"
+# Add variable DOCKER_PASSWORD
+source .env
+
+# Log in to Docker
+echo docker login --username "$docker_path" --password-stdin $DOCKER_PASSWORD
+
+# Check if login was successful
+if [ $? -eq 0 ]; then
+    echo "Docker authentication successful!"
+else
+    echo "Docker authentication failed!"
+fi
+
+echo "Docker ID and Image: $docker_path"
+
+docker image tag "$image_name:$image_tag" "$docker_path/$image_name:$image_tag"
 
-# Step 3:
 # Push image to a docker repository
+docker push "$docker_path/$image_name:$image_tag"