1. Parallel Data Processing using AWS Batch for AI


    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:

    $ pip install pulumi_aws

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