Installation & Setup

Prerequisites

• You must have Python installed.
• You must have pip installed.
• You must have Docker installed.
• You need VS Code.

AWS Prerequisites

• You must have the AWS CLI installed and an AWS account with an access key and secret key stored in ~/.aws/credentials:

  • Create a new user:

    aws iam create-user --user-name MyIAMUser
    

  • Attach policies to this user:

    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/AmazonEC2ContainerRegistryFullAccess
    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/AmazonEC2ReadOnlyAccess
    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/AmazonECS_FullAccess
    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/AmazonS3FullAccess
    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/AWSAppRunnerFullAccess
    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/IAMFullAccess
    aws iam attach-user-policy --user-name MyIAMUser --policy-arn arn:aws:iam::aws:policy/IAMReadOnlyAccess
    

    <!-- - Create an IAM Role:

    aws iam create-role --role-name AppRunnerECRAccessRole \
    --assume-role-policy-document '{
        "Version": "2012-10-17",
        "Statement": [
        {
            "Effect": "Allow",
            "Principal": {
            "Service": "build.apprunner.amazonaws.com"
            },
            "Action": "sts:AssumeRole"
        }
        ]
    }'
    

    - Attach policies to this role:

    bash aws iam attach-role-policy --role-name AppRunnerECRAccessRole --policy-arn arn:aws:iam::aws:policy/service-role/AWSAppRunnerServicePolicyForECRAccess aws iam attach-role-policy --role-name AppRunnerECRAccessRole --policy-arn arn:aws:iam::aws:policy/CloudWatchLogsFullAccess →

  bash aws iam get-role --role-name AppRunnerECRAccessRole --query 'Role.Arn' --output text

  • Create an access key:

    bash aws iam create-access-key --user-name MyIAMUser

  • Add the access key and secret key to the AWS CLI using the elements AccessKeyId and SecretAccessKey generated previously:

    bash aws configure --profile MyIAMUser

• You need to create a S3 bucket:

  aws s3api create-bucket --bucket dsba-mlops-project --region eu-west-3 --create-bucket-configuration LocationConstraint=eu-west-3 --profile MyIAMUser
  

  • Notes:
    • You can chose a different bucket name but you will need to update the .env file mentioned at step 5 of the installation accordingly.
    • You can check that the bucket was created by running aws s3 ls.

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Installation

1. Download the repository

   • Go to mlops-project.
   • Click on "Code", then "Download ZIP".
   • Extract the ZIP file to your ~/Desktop for easy access.

   Or clone the repository using the following command:

   cd Desktop
   git clone https://github.com/allisterk2703/mlops-project.git
   

2. Open the project in VS Code

   • Open the mlops-project folder in VS Code.
   • Open a terminal in VS Code: Press Control ^ + Shift ⇧ + <.

3. Set up the virtual environment

   Run the following commands in the terminal:

   pip install hatch
   hatch env create
   hatch shell
   pip install -e .
   

4. Set environment variables

   To ensure the correct paths are recognized, add the following lines to your ~/.zshrc file:

   echo 'export PYTHONPATH="$PYTHONPATH:$HOME/Desktop/dsba-platform/src"' >> ~/.zshrc
   echo 'export DSBA_MODELS_ROOT_PATH="$HOME/Desktop/dsba-platform/models"' >> ~/.zshrc
   

   Then, run the following command to apply the changes:

   source ~/.zshrc
   

5. Create a .env file

   At the root of the project, create a .env file and add the following (using the AWS credentials and S3 bucket name you created earlier):

   AWS_ACCESS_KEY_ID=<YOUR_AWS_ACCESS_KEY_ID>
   AWS_SECRET_ACCESS_KEY=<YOUR_AWS_SECRET_ACCESS_KEY>
   AWS_REGION=<YOUR_AWS_REGION>
   S3_BUCKET_NAME=dsba-mlops-project
   

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