Orchestration of Model Deployment to Cloud with GitHub Actions
PythonTo orchestrate a model deployment to a cloud provider using GitHub Actions, you will first need to create a GitHub repository that includes your model code and any necessary deployment scripts. Then, you can define a GitHub Actions workflow that uses Pulumi to provision and manage the necessary cloud infrastructure.
Below you will find a detailed Python program that defines the cloud resources you might need using Pulumi. As a general process, the GitHub Actions workflow will trigger Pulumi to execute this code every time there's a change that should be deployed.
This example will use AWS as the cloud provider, and it will include resources for a simple web application hosted on an EC2 instance. The application will be served from a load-balancer, and the infrastructure includes security groups and an IAM role.
Here's how the Pulumi program might look:
import pulumi import pulumi_aws as aws # Create an IAM role for the EC2 instance. ec2_role = aws.iam.Role('ec2Role', assume_role_policy=json.dumps({ 'Version': '2012-10-17', 'Statement': [{ 'Action': 'sts:AssumeRole', 'Effect': 'Allow', 'Principal': {'Service': 'ec2.amazonaws.com'}, }], })) # Attach the AmazonSSMManagedInstanceCore policy to the IAM role. policy_attachment = aws.iam.RolePolicyAttachment('ssmPolicyAttachment', role=ec2_role.name, policy_arn='arn:aws:iam::aws:policy/AmazonSSMManagedInstanceCore' ) # Create a security group for the EC2 instance. sec_group = aws.ec2.SecurityGroup('secGroup', description='Enable HTTP access', ingress=[ {'protocol': 'tcp', 'from_port': 80, 'to_port': 80, 'cidr_blocks': ['0.0.0.0/0']}, ], ) # Create an EC2 instance to host the application. app_server = aws.ec2.Instance('appServer', instance_type='t2.micro', vpc_security_group_ids=[sec_group.id], ami='ami-0c55b159cbfafe1f0', # Update with a valid Amazon Linux AMI for your region. iam_instance_profile=ec2_role.name, ) # Create a load balancer to distribute traffic to the application. app_load_balancer = aws.lb.LoadBalancer('appLoadBalancer', internal=False, security_groups=[sec_group.id], subnets=[] # Update with the IDs of your VPC subnets. ) # Create a load balancer target group. app_target_group = aws.lb.TargetGroup('appTargetGroup', port=80, protocol='HTTP', target_type='instance', vpc_id='' # Update with the ID of your VPC. ) # Create a load balancer listener. app_listener = aws.lb.Listener('appListener', load_balancer_arn=app_load_balancer.arn, port=80, default_actions=[{ 'type': 'forward', 'target_group_arn': app_target_group.arn }], ) # Output the URL of the load balancer to access the application. pulumi.export('app_url', app_load_balancer.dns_name)
This Pulumi program creates an EC2 instance with an associated security group and IAM role, and it sets up a load balancer with a listener and target group. The URL to access the application is exported at the end of the program.
GitHub Actions Workflow Integration
In your GitHub repository, you will typically define a
.github/workflows/deploy.yml
file like this:name: Deploy Model on: push: branches: - main # Or any branch you want to use for deployment. jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout Repo uses: actions/checkout@v2 - name: Set up Python uses: actions/setup-python@v2 with: python-version: '3.8' - name: Install Pulumi CLI uses: pulumi/action-install-pulumi-cli@v2 - name: Deploy with Pulumi uses: pulumi/actions@v3 with: command: up stack-name: dev # Or your Pulumi stack name. env: PULUMI_ACCESS_TOKEN: ${{ secrets.PULUMI_ACCESS_TOKEN }} AWS_ACCESS_KEY_ID: ${{ secrets.AWS_ACCESS_KEY_ID }} AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_SECRET_ACCESS_KEY }} AWS_REGION: 'us-west-2' # Or your AWS region.
The workflow includes steps to check out the repository code, set up the Python environment, install the Pulumi CLI, and finally, deploy the infrastructure using Pulumi. You will need to provide your Pulumi access token and AWS credentials as secrets in your GitHub repository settings.
On each push to the branch specified, GitHub Actions will run this workflow, using Pulumi to update your cloud infrastructure based on the Pulumi program you have written. Changes to the infrastructure are reviewed and applied, making sure that the live environment reflects the state of your codebase.