If you already have an account, skip this step.

Go to this link and follow the instructions. You will need a valid debit or credit card. You will not be charged, it is only to validate your ID.

Install the AWS CLI Version 1 for your operating system. Please follow the appropriate link below based on your operating system.

• macOS

• Windows

** Please make sure you add the AWS CLI version 1 executable to your command line Path. Verify that AWS CLI is installed correctly by running aws --version.

• You should see something similar to aws-cli/1.17.0 Python/3.7.4 Darwin/18.7.0 botocore/1.14.0.

You need to retrieve AWS credentials that allow your AWS CLI to access AWS resources.

1. Sign into the AWS console. This simply requires that you sign in with the email and password you used to create your account. If you already have an AWS account, be sure to log in as the root user.
2. Choose your account name in the navigation bar at the top right, and then choose My Security Credentials.
3. Expand the Access keys (access key ID and secret access key) section.
4. Press Create New Access Key.
5. Press Download Key File to download a CSV file that contains your new AccessKeyId and SecretKey. Keep this file somewhere where you can find it easily.

Now, you can configure your AWS CLI with the credentials you just created and downloaded.

1. In your Terminal, run aws configure.

   i. Enter your AWS Access Key ID from the file you downloaded.
   ii. Enter the AWS Secret Access Key from the file.
   iii. For Default region name, enter us-east-1.
   iv. For Default output format, enter json.

2. Run aws s3 ls in your Terminal. If your AWS CLI is configured correctly, you should see nothing (because you do not have any existing AWS S3 buckets) or if you have created AWS S3 buckets before, they will be listed in your Terminal window.

** If you get an error, then please try to configure your AWS CLI again.

Follow the instructions of your operating system:

macOS

Windows

Install Docker Desktop. Use one of the links below to download the proper Docker application depending on your operating system. Create a DockerHub account if asked.

• For macOS, follow this link.

• For Windows 10 64-bit Home, follow this link

i. Excecute the files "first.bat" and "second.bat" in order, as administrator.

ii. Restart your computer.

iii.Excecute the following commands in terminal, as administrator.

 ```
 REG ADD "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion" /f /v EditionID /t REG_SZ /d "Professional"
 REG ADD "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion" /f /v ProductName /t REG_SZ /d "Windows 10 Pro"
 ```

iv. Follow this link to install Docker.

v. Restart your computer, do not log out.

vi. Excecute the following commands in terminal, as administrator.

 ```
 REG ADD "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion" /v EditionID /t REG_SZ /d "Core"\
 REG ADD "HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Windows NT\CurrentVersion" /v ProductName /t REG_SZ /d "Windows 10 Home"
 ```

Open a Terminal window and type docker run hello-world to make sure Docker is installed properly . It should appear the following message:

Hello from Docker!
This message shows that your installation appears to be working correctly.

Finally, in the Terminal window excecute docker pull tensorflow/tensorflow:2.1.0-py3-jupyter.

Follow the instructions for your operating system.

• For macOS, follow this link
• For Windows, follow this link

Follow the instructions for your operating system.
If you already have a prefered text editor, skip this step.

1. Run requirements.txt

pip install -U -r requirements.txt

This command will instal all the required packages and update any older packages.

2. Now that we have our enviornment set up, we will create an S3 bucket.

Follow this link and create two buckets, an input bucket and a outbucket and configure them in the pipeline files.

3. Now, run the pipelines in the following order, you find the instructions of running each pipeline in their respective folders:
   a. Run the Annotation Pipeline testp.py to get the labelled dataset
   b. Run the Training Pipeline training_pipeline.py to get the trained model
   c. Start the micro service by running the docker MicroSerivce from the Microservice folder.
   d. Run the Inference Pipeline inference_pipeline.py to get the final output

4. At the end you should get a csv file with sentence and a sentiment, which will look like this:

5. Celebrate with drinks, but only soft drinks, remember kids, alcohol kills brain cells