remove some setup files

changed an auther to myself

<?xml

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+<?xml version='1.0' encoding='UTF-8'?><Error><Code>NoSuchKey</Code><Message>The specified key does not exist.</Message><Details>No such object: kubernetes-release/release/&lt;</Details></Error>

Dockerfile

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+FROM python:3.7.3-stretch
+
+## 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
+
+## Step 3:
+# Install packages from requirements.txt
+# hadolint ignore=DL3013
+
+RUN pip install --upgrade pip &&\
+    ls &&\
+    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"]
+
+
+
+

Makefile

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+## The Makefile includes instructions on environment setup and lint tests
+# Create and activate a virtual environment
+# Install dependencies in requirements.txt
+# Dockerfile should pass hadolint
+# app.py should pass pylint
+# (Optional) Build a simple integration test
+
+setup:
+	# Create python virtualenv & source it
+	# source ~/.devops/bin/activate
+	python3 -m venv ~/.devops
+
+install:
+	# This should be run from inside a virtualenv
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+	sudo wget -O /bin/hadolint https://github.com/hadolint/hadolint/releases/download/v1.16.3/hadolint-Linux-x86_64
+	sudo chmod +x /bin/hadolint
+
+test:
+	# Additional, optional, tests could go here
+	#python -m pytest -vv --cov=myrepolib tests/*.py
+	#python -m pytest --nbval notebook.ipynb
+
+lint:
+	# See local hadolint install instructions:   https://github.com/hadolint/hadolint
+	# This is linter for Dockerfiles
+	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
+
+all: install lint test

README.md

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+<include a CircleCI status badge, here>
+
+## Project Overview
+
+In this project, you will apply the skills you have acquired in this course to operationalize a Machine Learning Microservice API. 
+
+You are given a pre-trained, `sklearn` model that has been trained to predict housing prices in Boston according to several features, such as average rooms in a home and data about highway access, teacher-to-pupil ratios, and so on. You can read more about the data, which was initially taken from Kaggle, on [the data source site](https://www.kaggle.com/c/boston-housing). This project tests your ability to operationalize a Python flask app—in a provided file, `app.py`—that serves out predictions (inference) about housing prices through API calls. This project could be extended to any pre-trained machine learning model, such as those for image recognition and data labeling.
+
+### Project Tasks
+
+Your project goal is to operationalize this working, machine learning microservice using [kubernetes](https://kubernetes.io/), which is an open-source system for automating the management of containerized applications. In this project you will:
+* Test your project code using linting
+* Complete a Dockerfile to containerize this application
+* Deploy your containerized application using Docker and make a prediction
+* Improve the log statements in the source code for this application
+* Configure Kubernetes and create a Kubernetes cluster
+* Deploy a container using Kubernetes and make a prediction
+* Upload a complete Github repo with CircleCI to indicate that your code has been tested
+
+You can find a detailed [project rubric, here](https://review.udacity.com/#!/rubrics/2576/view).
+
+**The final implementation of the project will showcase your abilities to operationalize production microservices.**
+
+---
+
+## Setup the Environment
+
+* Create a virtualenv with Python 3.7 and activate it. Refer to this link for help on specifying the Python version in the virtualenv. 
+```bash
+python3 -m pip install --user virtualenv
+# You should have Python 3.7 available in your host. 
+# Check the Python path using `which python3`
+# Use a command similar to this one:
+python3 -m virtualenv --python=<path-to-Python3.7> .devops
+source .devops/bin/activate
+```
+* Run `make install` to install the necessary dependencies
+
+### Running `app.py`
+
+1. Standalone:  `python app.py`
+2. Run in Docker:  `./run_docker.sh`
+3. Run in Kubernetes:  `./run_kubernetes.sh`
+
+### Kubernetes Steps
+
+* Setup and Configure Docker locally
+* Setup and Configure Kubernetes locally
+* Create Flask app in Container
+* Run via kubectl

app.py

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+from flask import Flask, request, jsonify
+from flask.logging import create_logger
+import logging
+
+import pandas as pd
+from sklearn.externals import joblib
+from sklearn.preprocessing import StandardScaler
+
+app = Flask(__name__)
+LOG = create_logger(app)
+LOG.setLevel(logging.INFO)
+
+def scale(payload):
+    """Scales Payload"""
+
+    LOG.info(f"Scaling Payload: \n{payload}")
+    scaler = StandardScaler().fit(payload.astype(float))
+    scaled_adhoc_predict = scaler.transform(payload.astype(float))
+    return scaled_adhoc_predict
+
+@app.route("/")
+def home():
+    html = f"<h3>Sklearn Prediction Home</h3>"
+    return html.format(format)
+
+@app.route("/predict", methods=['POST'])
+def predict():
+    """Performs an sklearn prediction
+        
+        input looks like:
+        {
+        "CHAS":{
+        "0":0
+        },
+        "RM":{
+        "0":6.575
+        },
+        "TAX":{
+        "0":296.0
+        },
+        "PTRATIO":{
+        "0":15.3
+        },
+        "B":{
+        "0":396.9
+        },
+        "LSTAT":{
+        "0":4.98
+        }
+        
+        result looks like:
+        { "prediction": [ <val> ] }
+        
+        """
+
+    # Logging the input payload
+    json_payload = request.json
+    LOG.info(f"JSON payload: \n{json_payload}")
+    inference_payload = pd.DataFrame(json_payload)
+    LOG.info(f"Inference payload DataFrame: \n{inference_payload}")
+    # scale the input
+    scaled_payload = scale(inference_payload)
+    # 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 values: \n {prediction}')
+
+    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

docker_out.txt

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+[2022-09-01 18:49:06,364] 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}}
+[2022-09-01 18:49:06,385] INFO in app: Inference payload DataFrame: 
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+[2022-09-01 18:49:06,397] INFO in app: Scaling Payload: 
+   CHAS     RM    TAX  PTRATIO      B  LSTAT
+0     0  6.575  296.0     15.3  396.9   4.98
+[2022-09-01 18:49:06,401] INFO in app: Prediction values: 
+ [20.35373177134412]
+172.17.0.1 - - [01/Sep/2022 18:49:06] "POST /predict HTTP/1.1" 200 -

kubernetes.out.txt

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+^C(.devops) voclabs:~/environment/project-ml-microservice-kubernetes $ bash run_kubernes.sh 
+Error from server (AlreadyExists): pods "udapeople" already exists
+NAME        READY   STATUS    RESTARTS      AGE
+udapeople   1/1     Running   2 (44s ago)   3m34s
+Forwarding from 127.0.0.1:8000 -> 80
+Forwarding from [::1]:8000 -> 80
+Handling connection for 8000
+
+
+(.devops) voclabs:~/environment/project-ml-microservice-kubernetes $ bash make_prediction.sh Port: 8000
+{
+  "prediction": [
+    20.35373177134412
+  ]
+}
+

make_prediction.sh

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+#!/usr/bin/env bash
+
+PORT=8000
+echo "Port: $PORT"
+
+# POST method predict
+curl -d '{  
+   "CHAS":{  
+      "0":0
+   },
+   "RM":{  
+      "0":6.575
+   },
+   "TAX":{  
+      "0":296.0
+   },
+   "PTRATIO":{  
+      "0":15.3
+   },
+   "B":{  
+      "0":396.9
+   },
+   "LSTAT":{  
+      "0":4.98
+   }
+}'\
+     -H "Content-Type: application/json" \
+     -X POST http://localhost:$PORT/predict