1. EC2-backed JupyterHub for AI Development and Experimentation

    Python

    To set up an EC2-backed JupyterHub for AI development and experimentation, we'll use AWS resources. JupyterHub allows users to create and manage multiple Jupyter Notebook environments, making it an ideal tool for collaboration and experimentation amongst data scientists and machine learning engineers.

    Here's an overview of the steps we'll follow in the Pulumi program:

    1. Create an EC2 instance that will host our JupyterHub server.
    2. Set up the necessary security groups to allow HTTP/HTTPS access.
    3. Install and configure the JupyterHub software on the EC2 instance.
    4. Create IAM roles and policies for any additional AWS services that might be needed, such as S3 buckets for data storage.

    Below is the Python Pulumi program that performs these steps. Please note that you'll need an AWS account and Pulumi CLI configured already. Ensure you have the necessary permissions to create resources in AWS.

    import pulumi import pulumi_aws as aws # Choose an appropriate size for the instance # For AI and heavy computation, a more powerful instance might be required. instance_type = "t2.medium" # Configure the EC2 instance ec2_instance = aws.ec2.Instance("JupyterHubEC2Instance", instance_type=instance_type, ami="ami-0c55b159cbfafe1f0", # This is an Amazon Linux 2 AMI. Use an AMI as per your region. tags={"Name": "JupyterHubInstance"}, ) # Create a Security Group for the JupyterHub EC2 instance to allow inbound traffic # on the HTTP and HTTPS ports security_group = aws.ec2.SecurityGroup("JupyterHubSecurityGroup", description="Allow HTTP and HTTPS inbound traffic", ingress=[ {"description": "HTTP", "from_port": 80, "to_port": 80, "protocol": "tcp", "cidr_blocks": ["0.0.0.0/0"]}, {"description": "HTTPS", "from_port": 443, "to_port": 443, "protocol": "tcp", "cidr_blocks": ["0.0.0.0/0"]}, ], ) # Associate the security group with the EC2 instance aws.ec2.InstanceSecurityGroupAssociation("SecurityGroupAssociation", instance_id=ec2_instance.id, security_group_id=security_group.id, ) # Export the URL of the instance to be accessed by clients # Note that this URL will only work once JupyterHub is installed and configured on the instance pulumi.export("jupyterhub_url", ec2_instance.public_dns.apply(lambda dns: f"http://{dns}"))

    This program sets up a single EC2 instance with a security group allowing HTTP/HTTPS traffic. After you run pulumi up, this will output the DNS address that you can navigate to in your web browser to access JupyterHub. However, first, you must manually install and set up JupyterHub on that instance.

    Remember, in a real-world scenario, you would also configure key pairs for SSH access, IAM roles for accessing other AWS services from within JupyterHub, and perhaps scalability settings like Auto Scaling Groups.

    For more information on EC2 instances with Pulumi, you can visit EC2 Instance | Pulumi, and for information on security groups, check Security Group | Pulumi. After you've gotten the instance started, you will need to follow the JupyterHub documentation to install and configure JupyterHub on your EC2 instance.


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