1. Distributed Model Training Coordination with AWS Batch


    To facilitate distributed model training coordination using AWS Batch, we will need to set up various components of AWS Batch including a Compute Environment, a Job Queue, and a Job Definition. This will allow you to distribute training jobs across a fleet of compute resources that you define. AWS Batch efficiently manages the underlying compute resources for you, enabling you to run large-scale parallel and high-throughput workloads.

    Here's how to use AWS Batch for distributed model training coordination:

    1. Compute Environment: A compute environment is a set of managed or unmanaged compute resources that an AWS Batch job queue can draw upon to run jobs. Here, we define the type of instance, desired, minimum, and maximum vCPUs for the environment, along with the instance role and optional ECS configurations for when using Elastic Container Service.

    2. Job Queue: A job queue is a logical grouping of jobs. Jobs are submitted to a job queue, where they reside until they are able to be scheduled onto a compute environment. The job queue has a priority that is used by AWS Batch to determine which jobs to run first.

    3. Job Definition: Job definitions include details about how jobs are to be run. Among other things, they include a Docker image, memory, CPU requirements, and the commands to run within the container. This can be thought of as the blueprint for the jobs that are run within the compute environment.

    Below is a Pulumi program written in Python that sets up these resources. Please note that you will need to have Docker images ready that contain your model training code or application for use in the job definitions.

    import pulumi import pulumi_aws as aws # Create an AWS Batch Compute Environment compute_environment = aws.batch.ComputeEnvironment("myComputeEnvironment", service_role=aws.iam.Role("batchServiceRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "batch.amazonaws.com" } }] }""").arn, compute_resources=aws.batch.ComputeEnvironmentComputeResourcesArgs( max_vcpus=16, min_vcpus=0, desired_vcpus=4, instance_types=["m4.large"], subnets=[aws.ec2.Subnet("subnet").id], security_group_ids=[aws.ec2.SecurityGroup("sg").id], type="EC2", instance_role=aws.iam.InstanceProfile("ecsInstanceRole", role=aws.iam.Role("ecsInstanceRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Effect": "Allow", "Principal": {"Service": "ec2.amazonaws.com"}, "Action": "sts:AssumeRole" }] }""").arn).arn, ), type="MANAGED" ) # Create an AWS Batch Job Queue job_queue = aws.batch.JobQueue("myJobQueue", state="ENABLED", priority=1, compute_environments=[ aws.batch.JobQueueComputeEnvironmentArgs( compute_environment=compute_environment.arn, order=1 ), ] ) # Create an AWS Batch Job Definition job_definition = aws.batch.JobDefinition("myJobDefinition", type="container", container_properties="""{ "image": "my-docker-image", "vcpus": 1, "memory": 512 }""" ) pulumi.export("compute_environment_arn", compute_environment.arn) pulumi.export("job_queue_arn", job_queue.arn) pulumi.export("job_definition_arn", job_definition.arn)

    In this program, the first part creates a Compute Environment with a given instance role and EC2 configurations. Then, a Job Queue is created with a specified priority and linked to the Compute Environment. Finally, a Job Definition is set up with the specifications for running the job in a Docker container which should include your model training application.

    To run distributed model training jobs, you would submit jobs using the AWS CLI or AWS SDKs, pointing towards the Job Definition and Job Queue created above. AWS Batch will then orchestrate the instantiation of required computing resources and the execution of these jobs, scaling resources as defined in the Compute Environment.

    Remember, this is just the infrastructure setup using Pulumi. Actual job submission and monitoring will happen through AWS Batch interfaces (AWS Management Console, AWS CLI, or SDKs) once this infrastructure is provisioned.