Containerized Model Training Jobs on ECS Clusters

Question

Pulumi · Accepted Answer

Containerized model training on AWS ECS (Elastic Container Service) involves several steps which can be implemented using Pulumi, an infrastructure as code tool. Each step will be discussed below, and we’ll create a Pulumi program using AWS resources in Python.

First, we will define an ECS cluster where our containerized tasks will run. The ECS cluster acts as a logical grouping of tasks or services.

Next, we will set up a task definition. Task definitions are required to run Docker containers in ECS. It comprises the container definitions, volumes, and networking settings.

We will also define a Compute Environment on AWS Batch to manage and run our containerized jobs. AWS Batch will enable us to define compute resources, scheduling policies, and will integrate with ECS to manage the workload.

Lastly, we will create a job definition and a job queue. The job definition specifies how jobs are to be run, and the job queue receives the submitted jobs and places them in order for execution.

Here’s how you can implement these steps using Pulumi in Python:

```python
import pulumi
import pulumi_aws as aws

# Create an ECS cluster to house our services
ecs_cluster = aws.ecs.Cluster("ecs_cluster")

# Define an IAM role for ECS tasks
task_exec_role = aws.iam.Role("task_exec_role", assume_role_policy=aws.iam.get_policy_document(
    statements=[aws.iam.GetPolicyDocumentStatementArgs(
        actions=["sts:AssumeRole"],
        effect="Allow",
        principals=[aws.iam.GetPolicyDocumentStatementPrincipalArgs(
            type="Service",
            identifiers=["ecs-tasks.amazonaws.com"]
        )]
    )]
).json)

# Attach the required policy to the role
task_exec_policy_attachment = aws.iam.RolePolicyAttachment("task_exec_policy_attachment",
    role=task_exec_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AmazonECSTaskExecutionRolePolicy")

# Define an ECS task definition for our containerized job
task_definition = aws.ecs.TaskDefinition("model_training_task",
    family="model-training",
    cpu="256",
    memory="512",
    network_mode="awsvpc",
    requires_compatibilities=["FARGATE"],
    execution_role_arn=task_exec_role.arn,
    container_definitions=pulumi.Output.all().apply(lambda args: '[{"name":"model-training-container","image":"my-model-training-image","cpu":256,"memory":512}]'))

# Create a Compute Environment for our Batch jobs
compute_environment = aws.batch.ComputeEnvironment("model_training_compute_env",
    compute_environment_name="model-training-compute",
    compute_resources=aws.batch.ComputeEnvironmentComputeResourcesArgs(
        type="EC2",
        minv_cpus=0,
        maxv_cpus=16,
        instance_types=["optimal"],
        subnets=["subnet-XXXXXXXX"], # Replace with your actual subnet IDs
        security_group_ids=["sg-XXXXXXXX"], # Replace with your actual security group IDs
    ),
    service_role=task_exec_role.arn)

# Define a Batch job queue that will accept and run submitted jobs
job_queue = aws.batch.JobQueue("model_training_job_queue",
    state="ENABLED",
    priority=1,
    compute_environments=[compute_environment.arn])

# Define a Batch job definition, which references our ECS task definition
job_definition = aws.batch.JobDefinition("model_training_job_def",
    type="container",
    container_properties="""{
        "image": "my-model-training-image",
        "vcpus": 1,
        "memory": 512,
        "command": ["python", "train.py"],
        "executionRoleArn": "${task_exec_role.arn}"
    }""")

# Export the ECS cluster name and ARNs for the job queue and job definition
pulumi.export("ecs_cluster_name", ecs_cluster.name)
pulumi.export("job_queue_arn", job_queue.arn)
pulumi.export("job_definition_arn", job_definition.arn)
```

This program does the following:
1. Sets up an ECS cluster which will host our containerized tasks.
2. Creates an IAM role for tasks with the policy needed to execute tasks on ECS.
3. Defines an ECS task definition outlining how our containers should run, including the container image and resource requirements.
4. Specifies a compute environment for AWS Batch, which describes the compute resources your jobs will use.
5. Sets up a job queue that will handle incoming training jobs and distribute them to the compute resources.
6. Creates a job definition that will be used to submit our training jobs, containing details about container properties and the command to run for model training.

Replace `my-model-training-image` with the actual Docker image you want to use for training and ensure to change the subnet and security group IDs appropriately.

Please make sure you have the Pulumi AWS provider set up and authenticated before running this program. After defining this infrastructure code, run `pulumi up` to provision the resources in your AWS account.