Real-time Model Inference Serving with NATS Streaming

Question

Pulumi · Accepted Answer

Model inference serving is a term used for the deployment of machine learning models in production, making them available to provide predictions (inferences) in real time on incoming data. While the Pulumi Registry Results mention several services related to real-time data processing, such as Azure Stream Analytics, HDInsight, AWS Kinesis, and Amazon MSK Serverless, they do not directly mention NATS Streaming.

NATS Streaming is a data streaming system powered by NATS, which can be used for messaging that enables real-time data streaming use cases. Model inference serving with NATS Streaming would typically involve publishing data to a NATS subject, and having consumers that use trained machine learning models to perform inference on the data as it arrives.

While Pulumi doesn't have direct support for NATS Streaming in the registry results provided, it is possible to set up infrastructure on AWS, Azure, or GCP that can host a NATS Streaming server, and then deploy a model serving application that uses NATS Streaming for real-time inferences.

Below is an example of how you might deploy a model serving application that communicates with a NATS Streaming server on AWS using Pulumi:

1. Provision an EC2 instance which will run both the NATS Streaming server and the model inference application.
2. Setup security groups to enable communication on the ports used by NATS.
3. Deploy the application by bootstrapping it on the instance, which subscribes to a NATS subject to receive data and uses a machine learning model to perform real-time inferences.
4. Ensure the instance profile has sufficient permissions if the model is retrieved from an AWS service (e.g., S3).

```python
import pulumi
import pulumi_aws as aws

# Define the size of our EC2 instance. The instance type will determine compute,
# memory, storage, and networking capacity.
instance_type = 't2.micro'  # this is a cost-effective type for a small app

# Provision a new EC2 key pair for SSH access to the instance.
key_pair = aws.ec2.KeyPair("keyPair", public_key="ssh-rsa AAAAB3NzaC1yc2EAAAADAQABAAABAQD ... user@host")

# Create a new security group for the instance to allow SSH access and NATS communication.
secgroup = aws.ec2.SecurityGroup('secgroup',
    description='Enable SSH access and NATS communication',
    ingress=[
        # SSH access from anywhere
        {"protocol": "tcp", "from_port": 22, "to_port": 22, "cidr_blocks": ["0.0.0.0/0"]},
        # NATS default port for client access to NATS streaming
        {"protocol": "tcp", "from_port": 4222, "to_port": 4222, "cidr_blocks": ["0.0.0.0/0"]},
    ],
    egress=[{"protocol": "-1", "from_port": 0, "to_port": 0, "cidr_blocks": ["0.0.0.0/0"]}],
)

# Create a new EC2 Instance to host our model serving application.
instance = aws.ec2.Instance("ModelServerInstance",
    instance_type=instance_type,
    security_groups=[secgroup.name],  # Referencing the port-defined SecurityGroup
    key_name=key_pair.key_name,  # Referencing the created KeyPair
    ami="ami-0c55b159cbfafe1f0",  # This is the latest Amazon Linux 2 AMI in us-east-1
    user_data="""#!/bin/bash
        # Commands to install and start the NATS Streaming server and model serving application.
        # This script runs once when the instance is created.
        # -- Install Docker --
        yum update -y
        amazon-linux-extras install docker
        service docker start
        usermod -a -G docker ec2-user
        # -- Run NATS Streaming server --
        docker run -d --name nats-streaming -p 4222:4222 nats-streaming
        # -- Deploy the model inference application --
        # This would include pulling the Docker container, setting environment variables, and running it.
    """
)

# Export the public IP address of the EC2 instance so we can SSH to it.
pulumi.export('instance_public_ip', instance.public_ip)
# Export the public DNS name of the instance to connect our application.
pulumi.export('instance_public_dns', instance.public_dns)
```

In this program:

- We created an EC2 instance where both the NATS Streaming server and the model inference application will reside.
- We set up a security group that allows traffic on the relevant ports (SSH for access to the EC2 instance, and the default NATS port 4222).
- We defined a script that will be executed when the instance starts; it installs Docker, runs a NATS Streaming Docker container, and sets up the model serving application.

Please note that this is a high-level example and many details need to be addressed according to the specific requirements of the infrastructure and model serving application, such as managing secrets, configuring the NATS security, and installing application-specific dependencies. Additionally, because NATS Streaming and the specifics of model serving setup are not directly supported by Pulumi, this example focuses on establishing the infrastructure that you could use for running your model serving workloads.