Dynamic Resource Allocation for AI Batch Jobs

Question

Pulumi · Accepted Answer

When you are looking to perform dynamic resource allocation for AI batch jobs, you will typically want to ensure that compute resources are allocated efficiently depending on the workload requirements. This could involve setting up autoscaling policies, job queues, and scheduling policies to manage jobs that require various amounts of CPU, memory, and other resources.

In the context of cloud providers and Pulumi, there are different services and resources that can be used to set up dynamic resource allocation for AI batch jobs. Here we are going to focus on AWS, using the `aws.batch` service. This service provides the necessary components to run batch computing workloads within the AWS cloud. We will define a compute environment that specifies the type and amount of resources for our AI jobs, a job queue that prioritizes jobs, and a scheduling policy that controls how jobs are distributed across the compute resources.

Below is a Pulumi program that sets up a simple dynamic resource allocation for AI batch jobs on AWS using Pulumi with Python. We will leverage AWS Batch, which is suitable for the AI batch processing workloads.

```python
import pulumi
import pulumi_aws as aws

# Create an AWS Batch Compute Environment
# This defines the resources that will be used to run your batch jobs.
batch_compute_environment = aws.batch.ComputeEnvironment("ai_batch_compute_env",
    service_role=aws.iam.Role("batch_service_role",
        assume_role_policy="""{
            "Version": "2012-10-17",
            "Statement": [{
                "Effect": "Allow",
                "Principal": {"Service": "batch.amazonaws.com"},
                "Action": "sts:AssumeRole"
            }]
        }"""
    ).arn,
    compute_resources=aws.batch.ComputeEnvironmentComputeResourcesArgs(
        max_vcpus=128,
        min_vcpus=0,
        desired_vcpus=0,
        instance_type="m4.large",
        instance_role=aws.iam.InstanceProfile("ecs_instance_profile",
            role=aws.iam.Role("ecs_instance_role",
                assume_role_policy="""{
                    "Version": "2008-10-17",
                    "Statement": [{
                        "Effect": "Allow",
                        "Principal": {"Service": "ec2.amazonaws.com"},
                        "Action": "sts:AssumeRole"
                    }]
                }"""
            ).name
        ).arn,
        allocation_strategy="BEST_FIT_PROGRESSIVE",
        subnets=[subnet.id for subnet in aws.ec2.get_subnet_ids(vpc_id=vpc.id).ids],
        security_group_ids=[security_group.id for security_group in aws.ec2.get_security_group_ids(vpc_id=vpc.id).ids],
    ),
    type="MANAGED"
)

# Create an AWS Batch Job Queue
# The job queue connects the compute resources with the job definitions and enables prioritization of jobs.
batch_job_queue = aws.batch.JobQueue("ai_job_queue",
    compute_environments=[
        aws.batch.JobQueueComputeEnvironmentOrderArgs(
            compute_environment=batch_compute_environment.arn,
            order=1,
        )
    ],
    priority=1,
    state="ENABLED"
)

# Define a Scheduling Policy
# This policy will manage how jobs are prioritized and distributed across the compute environment.
scheduling_policy = aws.batch.SchedulingPolicy("ai_scheduling_policy",
    fair_share_policy=aws.batch.SchedulingPolicyFairSharePolicyArgs(
        share_decay_seconds=3600,
        compute_reservation=100,
        share_distributions=[
            aws.batch.SchedulingPolicyFairSharePolicyShareDistributionArgs(
                share_identifier="high_priority",
                weight_factor=1.0,
            ),
            aws.batch.SchedulingPolicyFairSharePolicyShareDistributionArgs(
                share_identifier="low_priority",
                weight_factor=0.5,
            ),
        ],
    )
)

# Now, you can define job definitions and submit jobs to the job queue which will be handled by the compute environment.

# Exporting output variables to help access some important attributes
pulumi.export("compute_environment_arn", batch_compute_environment.arn)
pulumi.export("job_queue_arn", batch_job_queue.arn)
pulumi.export("scheduling_policy_arn", scheduling_policy.arn)
```

Here's what this Pulumi program does:
- Sets up an AWS Batch Compute Environment with a specific instance type and flexible vCPU count (min, desired, max).
- Creates an IAM Role and Instance Profile for the Batch Compute Environment so it has the necessary permissions.
- Initiates a Job Queue with a prioritization of 1 (highest).
- Defines a Scheduling Policy with a fair share distribution to manage workload across high and low priority jobs.
- Exports ARNs for these resources, which could be used in other parts of your cloud infrastructure or for reference.

The `max_vcpus` specifies the maximum number of vCPUs that the compute environment can scale up to, while `min_vcpus` specifies the minimum, with `desired_vcpus` as the desired target count. You can adjust these according to the AI batch job requirements.

When you submit jobs to AWS Batch, they are placed in the job queue where they wait for the AWS Batch scheduler to place them onto compute resources based on the scheduling policy and the compute environment settings.

With AWS Batch and Pulumi, you get the fine-grained control needed to efficiently manage the dynamic resource allocation for your AI workloads.