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‎README.md

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cookiecutter https://github.com/sampathweb/cc-iris-api
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### Credits:
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‎cookiecutter.json

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{
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"project_name": "project_name",
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"project_name": "iris-api",
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"repo_name": "{{ cookiecutter.project_name|replace(' ', '_') }}",
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"author_name": "Your name (or your organization/company/team)",
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"description": "A short description of the project.",

‎{{cookiecutter.repo_name}}/README.md

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# Sample IRIS API Application
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Build IRIS Machine Learning Model using Scikit-Learn and deploy using Tornado Web Framework on AWS.
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Build IRIS Machine Learning Model using Scikit-Learn and deploy using Tornado Web Framework.
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## Setup Environment on Local Machine
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Api works!
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### Push code to your own git repo. (Optional - We will not do in tutorial)
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```
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# These git Steps are Optional for Pydata Tutorial.
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git init
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git add --all
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git commit -m "first commit"
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git remote add origin https://github.com/<your github username>/<remote repo name>.git
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git push --set-upstream origin master
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```
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## Deploy Steps for AWS Ubuntu 14.04 LTS EC2 Instance
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### Login to AWS Instance:
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`ssh -i <your AWS Pem key file> ubuntu@<aws ip>`
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### Install Python / Git
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```
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sudo apt-get update
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sudo apt-get upgrade
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# Install GIT
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sudo apt-get install git
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# Install Anaconda (Miniconda)
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wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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# To update the path (Make sure you said Yes when it asked to update path in the Miniconda install steps)
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source ~/.bashrc
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```
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### Download App Source Code:
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```
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git clone https://github.com/sampathweb/iris-api.git
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cd iris-api-app
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python env/create_env.py
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source activate env/venv
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python env/install_packages.py
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python ml_src/build_model.py
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python run.py (Confirm that App is running)
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sudo apt-get install supervisor
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sudo vi /etc/supervisor/conf.d/iris-api.conf
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<press i insert mode>
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[program:iris-api]
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autorestart = true
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command = /home/ubuntu/iris-api/env/venv/bin/python /home/ubuntu/iris-api/run.py --debug=False --port=80
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numprocs = 2
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startsecs = 10
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stderr_logfile = /var/log/supervisor/iris-api.log
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stdout_logfile = /var/log/supervisor/iris-api.log
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# stderr_logfile = syslog
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# stdout_logfile = syslog
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environment = PYTHONPATH="/home/ubuntu/iris-api/env/bin/"
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<escape :wq>
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sudo supervisorctl reload
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<Your APP is live now>
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```
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### Test the App
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1. Open Browser: `http://<AWS IP>` (App is Live!)
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2. Test in Command Line (Optional):
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```
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curl -i http://<aws ip address>/api/iris/predict -X POST -d '{ "sepal_length": 2, "sepal_width": 5, "petal_length": 3, "petal_width": 4}'
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```
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Congratulations you have deployed your App
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## Credits:
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Template from https://github.com/sampathweb/cc-iris-api

‎{{cookiecutter.repo_name}}/app/settings.py

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# !/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Appliction configuration settings
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"""

‎{{cookiecutter.repo_name}}/deploy_steps_aws.md

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## Deploy Steps for AWS Ubuntu 14.04 LTS EC2 Instance
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### Login to AWS Instance:
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`ssh -i <your AWS Pem key file> ubuntu@<aws ip>`
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### Install Python / Git
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```
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sudo apt-get update
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sudo apt-get upgrade
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# Install GIT
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sudo apt-get install git
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# Install Anaconda (Miniconda)
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wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
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bash Miniconda3-latest-Linux-x86_64.sh
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# To update the path (Make sure you said Yes when it asked to update path in the Miniconda install steps)
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source ~/.bashrc
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```
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### Download App Source Code:
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```
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git clone https://github.com/sampathweb/iris-api.git
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cd iris-api-app
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python env/create_env.py
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source activate env/venv
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python env/install_packages.py
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python ml_src/build_model.py
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python run.py (Confirm that App is running)
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sudo apt-get install supervisor
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sudo vi /etc/supervisor/conf.d/iris-api.conf
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<press i insert mode>
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[program:iris-api]
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autorestart = true
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command = /home/ubuntu/iris-api/env/venv/bin/python /home/ubuntu/iris-api/run.py --debug=False --port=80
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numprocs = 1
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startsecs = 10
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stderr_logfile = /var/log/supervisor/iris-api.log
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stdout_logfile = /var/log/supervisor/iris-api.log
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environment = PYTHONPATH="/home/ubuntu/iris-api/env/bin/"
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<escape :wq>
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sudo supervisorctl reload
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<Your APP is live now>
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```
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### Test the App
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1. Open Browser: `http://<AWS IP>` (App is Live!)
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2. Test in Command Line (Optional):
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```
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curl -i http://<aws ip address>/api/iris/predict -X POST -d '{ "sepal_length": 2, "sepal_width": 5, "petal_length": 3, "petal_width": 4}'
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```
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Congratulations you have deployed your App
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