1. AWS EC2 for Inference Server Hosting


    To host an inference server on AWS EC2, we need to create an EC2 instance that will run the machine learning models for inference. Here's what we'll do:

    1. EC2 Instance: The core of our inference server will be an EC2 instance. This is a virtual server in Amazon's Elastic Compute Cloud (EC2) for running applications.
    2. AMI: We will specify an Amazon Machine Image (AMI) which contains the necessary software configurations, including the operating system and the inference code.
    3. Instance Type: We'll choose an instance type based on the inference workload. AWS provides various instance types that are optimized for compute, memory, or I/O, and we can select one that best fits our needs.
    4. Security Group: This acts as a virtual firewall to control the traffic to the instance. We need to open the specific ports that the inference server listens on.
    5. Key Pair: To securely SSH into our instance, we need a key pair.
    6. Optional - Elastic IP: If we want our server to have a static IP address, we can allocate an Elastic IP. This is helpful if our inference server needs to be accessible from the internet with a consistent address.

    Here is a Pulumi program that will create such an EC2 instance:

    import pulumi import pulumi_aws as aws # Define the AMI to be used for our inference server (replace with a specific AMI ID available in your region) ami_id = "ami-0c55b159cbfafe1f0" # Choose an instance type. instance_type = "t2.medium" # This is a general-purpose instance type. You may choose a different one based on your workload. # Create an AWS key pair for SSH access (you should replace the public key with your actual public key content) key_pair = aws.ec2.KeyPair("keyPair", public_key="ssh-rsa AAAAB3Nza...") # Your public key goes here # Security Group to allow SSH (port 22) and any other ports your inference server needs. sec_group = aws.ec2.SecurityGroup('secGroup', description='Enable SSH access', ingress=[{ 'protocol': 'tcp', 'from_port': 22, 'to_port': 22, 'cidr_blocks': [''] }]) # Create an EC2 instance for the inference server. instance = aws.ec2.Instance("inferenceServer", instance_type=instance_type, security_groups=[sec_group.name], # Reference the security group. ami=ami_id, # Reference the AMI chosen for the server. key_name=key_pair.key_name, # Use the key pair created earlier. ) # Optionally create an Elastic IP if you need a static public IP. elastic_ip = aws.ec2.Eip("elasticIp", instance=instance.id) # Output the public IP address of the EC2 instance so we know how to connect to it. pulumi.export('publicIp', instance.public_ip) # Output the Elastic IP address if you've created one. pulumi.export('elasticIp', elastic_ip.public_ip)

    In this program, we import the necessary Pulumi AWS SDK to interact with AWS services. We define an AWS key pair to connect securely to our instance, and a security group that opens port 22 for SSH access. We then create an EC2 instance with the type t2.medium, using our predefined AMI and security group.

    If you're following best practices, you'll want to use your own AMI that contains the inference server. Ensure your inference code and any additional dependencies are baked into the AMI. You might opt for an instance type that's tailored for machine learning, such as p3.2xlarge if you're using powerful models or need GPU acceleration.

    With this Pulumi program, after running pulumi up, you will get an output displaying the public IP of your inference EC2 instance and the Elastic IP if you've chosen to create one. You can SSH into the server with the private key corresponding to the AWS key pair and start the inference server to handle requests.