Master Containerization with AWS.txt

Krish Naik Sir's course link:https://youtu.be/8vmKtS8W7IQ?si=qXkM9cawo0X4HhM9

Section 2 Reference:
---------------------

Dockerfile:
--------------
FROM python:3.7
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python","app.py"]

FROM

The FROM instruction specifies the base image that the container will be built on top of. This instruction is typically the first one in a Dockerfile and is used to set the base image for the container. The format of the instruction is:
FROM <image>

COPY

Use the COPY instruction to copy local files from the host machine to the current working directory:
COPY package.json /app/

WORKDIR

In a Dockerfile, the WORKDIR instruction sets the working directory for any command that follows it in the Dockerfile. This means that any commands that run in the container will be executed relative to the specified directory. Below is the format of the instruction :
WORKDIR <directory>


RUN
Use the “RUN” instruction to execute commands that will run during the image build process:
RUN <command_name>
 

CMD

In a Dockerfile, the CMD instruction sets the command that will be executed when a container is run from the image. The format of the instruction is:

CMD ["executable","param1","param2",...]

ENV

Use the ENV instruction to set environment variables inside the image which will be available during build time as well as in a running container. For example, to set the NODE_ENV environment variable to production, you would use the following command:
ENV NODE_ENV production

Step 1:
-------
AWS Linux
sudo yum update -y
sudo amazon-linux-extras install docker
sudo service docker start
sudo usermod -a -G docker ec2-user

Step 2:
-------
Logout -- login

Step 3:
-------
check if the docker is running or not
docker info

Step 4:
--------
sudo yum -y install git
git --version
mkdir hello_docker 
git clone https://github.com/krishnaik06/Docker-For-Data-Science.git hello_docker
cd hello_docker
ls
cd 'Hello World'

Step 5:
--------
Converts your Dockerfile into an image: docker build -t welcome-app .

To view all the images: docker image ls

run command uses the created docker image to run a container: docker run -p 5000:5000 welcome-app


To view all running containers you can use any of below 2 commands--
i)docker container ls
ii)docker ps
These commands essentially serve the same purpose, displaying information about running containers. The commands are interchangeable, and they provide similar output.
To display all containers, including stopped ones: docker ps -a


To stop a container: docker stop container id(docker stop ca661e7a7e11)
Note: Although we can stop container , but still images will be there : docker images


To delete a conatiner:docker container rm <container_id_or_name>


To delete an image: docker image rm <image_id_or_name>


Section 3 Reference:
---------------------
https://docs.aws.amazon.com/lambda/latest/dg/python-image.html#python-image-instructions


Section 4 Reference:
---------------------

Docker install:
-----------------
sudo yum update -y
sudo amazon-linux-extras install -y docker
sudo service docker start
sudo usermod -a -G docker ec2-user

Create directory:
------------------
mkdir hello_docker 
cd hello_docker


Dockerfile:
------------
FROM python:3.8
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python","app.py"]


vi app.py:
-----------

from flask import Flask,request
import requests

app=Flask(__name__)

@app.route('/')
def hello_world():
    return "Hello World , Welcome to Bitcoin API"

@app.route('/get_bitcoin_price',methods=['GET'])
def data_extractor():
    message=requests.get('https://api.coinbase.com/v2/prices/btc-usd/spot')
    return message.text
	
if __name__=='__main__':
    app.run(host="0.0.0.0",port=5000)

vi requirements.txt:
---------------------
Flask
requests
urllib3==1.26.6

Run Docker:
-------------
Converts your Dockerfile into an image: docker build -t welcome-app .

To view all the images: docker image ls

run command uses the created docker image to run a container: docker run -p 5000:5000 welcome-app
--------------------------------------------------------------------------------------------------------------------------




Section 5 Reference:
---------------------
Docker install:
-----------------
sudo yum update -y
sudo amazon-linux-extras install -y docker
sudo service docker start
sudo usermod -a -G docker ec2-user

Create directory:
------------------
mkdir hello_docker 
cd hello_docker


Dockerfile:
------------
FROM python:3.8
COPY . /app
WORKDIR /app
RUN pip install -r requirements.txt
CMD ["python","app.py"]


vi app.py:
-----------
import requests
import boto3
import pytz
from datetime import datetime
import uuid

# Set your AWS credentials and region for DynamoDB
region_name = 'us-east-1'
ist = pytz.timezone('Asia/Kolkata')  # Indian Standard Time
current_time = datetime.now(ist).isoformat()
# Set your DynamoDB table name
table_name = 'bitcoin_price_storer'
# Set the REST API endpoint
api_url = 'https://api.coinbase.com/v2/prices/btc-usd/spot'

# Create a DynamoDB client
dynamodb = boto3.client('dynamodb',region_name=region_name)

# Function to create an item in DynamoDB table
def put_item_to_dynamodb(item):
    dynamodb.put_item(TableName=table_name, Item=item)

def main():
  # Fetch data from the REST API
  response = requests.get(api_url)
  data = response.json()

  data_to_ingest={"amount":{"S":data["data"]["amount"]},"base":{"S":data["data"]["base"]},"currency":{"S":data["data"]["currency"]},"timestamp":{"S":current_time},"uuid":{"S":str(uuid.uuid4())}}
  put_item_to_dynamodb(data_to_ingest)
  print(f'Item {data_to_ingest} added to DynamoDB table {table_name}.')

  print('Data transfer complete.')

main()

vi requirements.txt:
---------------------
requests
urllib3==1.26.6
boto3
pytz
uuid

Run Docker:
-------------
Converts your Dockerfile into an image: docker build -t welcome-app .

To view all the images: docker image ls

docker run  welcome-app