Batch Processing for AI Model Training Workloads

Question

Pulumi · Accepted Answer

When you want to create batch processing for AI model training workloads, you'll typically be looking at a cloud service that can handle the job orchestration, provide the necessary CPU or GPU resources, and scale according to the workload. Depending on the cloud provider you're using, there are various services available to you, such as AWS Batch, Azure Batch, Google Cloud's AI Platform Jobs, or Kubernetes Jobs for any cloud or on-premises environment.

For this purpose, you can use Pulumi to define your infrastructure as code, which allows you to create, update, and manage your cloud resources in a repeatable and predictable way. We will define a batch processing job with a given container image that runs your AI model training logic. The configuration entails defining a job queue, computing environments, and job definitions for specifying how jobs should be run.

Here is an example program that sets up AWS Batch for AI training workloads using Pulumi's Python SDK. This program will create:

- A job queue that manages how jobs are prioritized and executed.
- A compute environment where your jobs will run. This consists of managing the required resources, such as CPU, GPU, and memory.
- A job definition that describes how the batch jobs should run, which specifies the docker image containing your AI model training code.

Please replace the placeholder values with your actual Docker image, IAM roles, and any specific resource requirements for your workload.

```python
import pulumi
import pulumi_aws as aws

# Create an IAM role for AWS Batch Service to assume
batch_service_role = aws.iam.Role("batch_service_role",
    assume_role_policy=aws.iam.get_policy_document(statements=[{
        "actions": ["sts:AssumeRole"],
        "principals": [{
            "identifiers": ["batch.amazonaws.com"],
            "type": "Service",
        }],
    }]).json)

# Managed policy ARNs for AWS Batch Service Role
attach_execution_policy = aws.iam.RolePolicyAttachment("batch_execution_policy_attachment",
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSBatchServiceRole",
    role=batch_service_role.name)

# Create an instance role for ECS container instances
instance_role = aws.iam.Role("instance_role",
    assume_role_policy=aws.iam.get_policy_document(statements=[{
        "actions": ["sts:AssumeRole"],
        "principals": [{
            "identifiers": ["ec2.amazonaws.com"],
            "type": "Service",
        }],
    }]).json)

# Attach the AWS managed role policy for ECS container instances to the instance role
attach_instance_role_policy = aws.iam.RolePolicyAttachment("instance_role_policy_attachment",
    policy_arn="arn:aws:iam::aws:policy/service-role/AmazonEC2ContainerServiceforEC2Role",
    role=instance_role.name)

# Create an ECS instance profile that will be used by the container instances
instance_profile = aws.iam.InstanceProfile("instance_profile",
    role=instance_role.name)

# Define a compute environment for the batch jobs
compute_environment = aws.batch.ComputeEnvironment("compute_environment",
    service_role=batch_service_role.arn,
    compute_resources={
        "type": "EC2",  # For on-demand instances use "EC2", for spot instances use "SPOT"
        "min_vcpus": 2,
        "max_vcpus": 8,
        "instance_type": ["m4.large"],  # Specify your instance types
        "instance_role": instance_profile.arn
    })

# Create a job queue for the batch jobs
job_queue = aws.batch.JobQueue("job_queue",
    state="ENABLED",
    priority=100,
    compute_environments=[compute_environment.arn])

# Define the job definition for AI training jobs
job_definition = aws.batch.JobDefinition("job_definition",
    type="container",
    container_properties=pulumi.Output.all(instance_role.arn).apply(lambda arn: f"""
        {{
            "command": ["/bin/sh", "-c", "run-training-scripts.sh"],  # Replace the command as necessary
            "image": "your_docker_image",  # Replace with your Docker image ID
            "memory": 2048,
            "vcpus": 2,
            "jobRoleArn": "{arn}"
        }}
    """))

pulumi.export('job_queue_name', job_queue.name)
pulumi.export('job_definition_arn', job_definition.arn)
pulumi.export('compute_environment_arn', compute_environment.arn)
```

In this program:

- We start by creating IAM roles required for AWS Batch to function properly. The `batch_service_role` is for the Batch service itself, while the `instance_role` is for the compute resources that will run the job containers.
- The `aws.batch.ComputeEnvironment` resource is where you define the type of instances that your jobs will run on, how many vCPUs you want to allocate, and link it to the `instance_role`.
- The `aws.batch.JobQueue` resource is used to manage and prioritize the execution of jobs. It is attached to the compute environment we created above.
- The `aws.batch.JobDefinition` is where you define the details about the job, including the Docker image that contains your training logic and the necessary vCPU and memory it requires.

Please make sure you replace `"your_docker_image"` with your actual Docker image and adjust the command and compute resources according to your workload needs.

You can then create and manage your batch workloads by defining jobs that reference your job definition and submit them to the queue created. As your training jobs complete, AWS Batch will handle the scaling down of resources automatically.