Scalable AI Inference Workloads with AWS Batch

Question

Pulumi · Accepted Answer

To run scalable AI inference workloads with AWS Batch, we'll need to set up a few components:

1. **Compute Environment**: This provides the hardware that will run your inference jobs. In AWS Batch, you can choose between managed or unmanaged compute environments. A managed compute environment lets AWS Batch manage your computer resources.

2. **Job Queue**: This is where your inference jobs wait to be run on the compute resources. Job queues have priorities, and jobs in higher priority queues get scheduled before those in lower ones.

3. **Job Definition**: This defines how AWS Batch should run your jobs. For AI inference, you'll include details such as the Docker container image with your inference code and the required computational resources.

4. **Scheduling Policy** (optional): If you have multiple job queues, a scheduling policy helps determine how jobs are prioritized across the queues.

Here's a step-by-step Pulumi program that will create a managed compute environment, job queue, and job definition for inference workloads.

```python
import pulumi
import pulumi_aws as aws

# Define the AWS Batch Compute Environment
# This will launch EC2 instances to process batch jobs.
compute_environment = aws.batch.ComputeEnvironment("ai_compute_environment",
    service_role=aws.iam.Role("batch_service_role", # Create a new IAM role for the Batch service
        assume_role_policy="""{
            "Version": "2012-10-17",
            "Statement": [{
                "Action": "sts:AssumeRole",
                "Effect": "Allow",
                "Principal": {
                    "Service": "batch.amazonaws.com"
                }
            }]
        }""").arn,
    compute_resources={
        "instance_type": "m4.large",  # Choose instance based on your inference workload
        "max_vcpus": 16,
        "min_vcpus": 0,
        "type": "EC2",
        "subnets": [aws_subnet.subnet_id for aws_subnet in aws.ec2.get_subnet_ids()],  # Replace with your subnet IDs
        "security_group_ids": [aws_security_group.security_group_id],  # Replace with your security group IDs
    },
    type="MANAGED"
)

# Define the AWS Batch Job Queue
# This queue will hold all the jobs to be processed.
job_queue = aws.batch.JobQueue("ai_job_queue",
    state="ENABLED",
    priority=1,
    compute_environments=[compute_environment.arn]
)

# Define the AWS Batch Job Definition
# This describes how jobs should be containerized and run.
job_definition = aws.batch.JobDefinition("ai_job_definition",
    type="container",
    platform_capabilities=["FARGATE"],
    container_properties="""{
        "image": "my_inference_image",  # Replace with your Docker image
        "vcpus": 4,
        "memory": 8192,
        "command": ["python", "run_inference.py"],  # Replace with your command
        "jobRoleArn": "arn:aws:iam::123456789012:role/my-job-role"  # Replace with your IAM job role ARN
    }"""
)

# Output the ARN of the job queue which we will use to submit jobs
pulumi.export("job_queue_arn", job_queue.arn)

# Output the ARN of the job definition which we will use to submit jobs
pulumi.export("job_definition_arn", job_definition.arn)
```

In this program:
- A managed compute environment `ai_compute_environment` is created with EC2 `m4.large` instances. Replace the instance type with what matches your workload.
- A job queue `ai_job_queue` is created with a priority of `1`, and the compute environment attached to it.
- A job definition `ai_job_definition` is set up, which defines container properties for the inference jobs, including the image to use, the vCPUs, memory, and the command to run. You would replace `my_inference_image` with the URI of your Docker image that contains the AI model and `run_inference.py` with the script that triggers your inference.

To use this Pulumi program:
- Replace placeholders for subnet IDs and security group IDs with your own VPC settings.
- Replace the Docker image reference with the image repository URI containing your inference code.
- Ensure you have a suitable IAM role for the job queue and use its ARN.
- You may need to adjust the command, depending on how your inference script is set up or if you have a different entry point.
- After deploying the stack and getting the job queue ARN and job definition ARN, you can submit inference jobs to the job queue for processing.