Parallel Data Processing using AWS Batch for AI

Question

Pulumi · Accepted Answer

To perform parallel data processing using AWS Batch, you'll want to set up a compute environment, job queues, and job definitions that can handle your processing needs. AWS Batch enables you to run batch computing workloads on the AWS Cloud, making it a good choice for parallel data processing tasks such as AI workloads.

Here are the steps we'll take to set up parallel data processing using AWS Batch with Pulumi:

1. Create a Compute Environment: A place where your jobs will run. This can be either managed by AWS or can use your own compute resources.
2. Create Job Queues: These act as a mediator between the Compute Environment and the Job Definitions. They prioritize and route the jobs to Compute Environments.
3. Create Job Definitions: These specify how the batch jobs should run, including the Docker image to use, memory and CPU requirements, and other settings.
4. (Optional) Define a Scheduling Policy: This is used to define how jobs are prioritized within the job queue.

Let's start by installing the required Pulumi AWS package, if you haven't done so already:

```bash
$ pip install pulumi_aws
```

Now, we will write a Pulumi program to create these resources in Python:

```python
import pulumi
import pulumi_aws as aws

# Define the IAM roles for AWS Batch
batch_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"
        }]
    }""")

batch_instance_role = aws.iam.Role("batch_instance_role",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [{
            "Effect": "Allow",
            "Principal": {"Service": "ec2.amazonaws.com"},
            "Action": "sts:AssumeRole"
        }]
    }""")
    
# Attaching necessary policies to the instance role
instance_role_policy_attachment = aws.iam.RolePolicyAttachment("instance_role_policy_attachment",
    role=batch_instance_role.name,
    policy_arn=aws.iam.ManagedPolicy.AMAZON_EC2_CONTAINER_SERVICE_FOR_EC2_ROLE.arn)

# Create an instance profile that will be used by the compute resources
instance_profile = aws.iam.InstanceProfile("instance_profile",
    role=batch_instance_role.name)

# Create a compute environment
compute_environment = aws.batch.ComputeEnvironment("compute_environment",
    service_role=batch_service_role.arn,
    compute_resources=aws.batch.ComputeEnvironmentComputeResourcesArgs(
        instance_role=instance_profile.arn,
        max_vcpus=16,
        min_vcpus=0,
        type="EC2",
        desired_vcpus=4,
        instance_types=["m4.large"],
        subnets=["subnet-xxxxxxxxxxxx"],  # Replace with your subnet id
        security_group_ids=["sg-xxxxxxxxxxxx"],  # Replace with your security group id
        tags={
            "Name": "pulumi-batch-compute-env"
        }
    ),
    type="MANAGED")

# Create a job queue
job_queue = aws.batch.JobQueue("job_queue",
    state="ENABLED",
    priority=1,
    compute_environment_order=[
        aws.batch.JobQueueComputeEnvironmentOrderArgs(
            order=1,
            compute_environment=compute_environment.arn
        )
    ])

# Create a job definition
job_definition = aws.batch.JobDefinition("job_definition",
    type="container",
    container_properties="""{
        "image": "my-docker-image",  # Replace with your docker image
        "vcpus": 1,
        "memory": 512,
        "command": ["echo", "hello world"],  # Replace with the command to run
        "jobRoleArn": "arn:aws:iam::123456789012:role/my-batch-job-role"  # Replace with your IAM job role
    }""")

# (Optional) Create a scheduling policy
scheduling_policy = aws.batch.SchedulingPolicy("scheduling_policy",
    fair_share_policy=aws.batch.SchedulingPolicyFairSharePolicyArgs(
        share_decay_seconds=3600,
        compute_reservation=1
    ))

# Export the names
pulumi.export('compute_environment_name', compute_environment.name)
pulumi.export('job_queue_name', job_queue.name)
pulumi.export('job_definition_name', job_definition.name)
pulumi.export('scheduling_policy_name', scheduling_policy.name)
```

In this program:

- We create IAM roles and policies for the AWS Batch service and instances that will execute the jobs.
- We define an instance profile that EC2 instances will use when launched as part of our compute environment.
- We set up a compute environment with a specific instance type and maximum and minimum vCPUs.
- A job queue is created that will use the compute environment defined previously.
- We specify a job definition with details like the Docker image, CPU, memory, and command to run.
- As an optional step, we define a scheduling policy with a fair share policy to prioritize the execution of jobs.

Make sure to replace the `subnet-xxxxxxxxxxxx`, `sg-xxxxxxxxxxxx`, `my-docker-image`, and `arn:aws:iam::123456789012:role/my-batch-job-role` placeholders with your own values.

This Pulumi program should be enough to get you started with parallel data processing using AWS Batch. You can execute this program by running `pulumi up` in the directory containing the program.