Create a Job Definition using Fargate setting resourceRequirements instead computeResources

aws_batch_fargate_job_definition_create.py

import os
import boto3

COMPUTE_ENVIRONMENT_VERSION = 'v0_01'

aws_iam_boto_client \
    = boto3.client('iam')

aws_batch_boto_client \
    = boto3.client('batch')

application_service_role \
    = iam_boto_client.get_role(
        RoleName='your_generic_executon_role'
        )


def job_definition_create(
    aws_batch_boto_client=aws_batch_boto_client,
    version=COMPUTE_ENVIRONMENT_VERSION,
    ):
    
    """Create a job definition in AWS Batch using FARGATE
    setting resourceRequirements instead computeResources,
    in a more clear manner than the Boto3 documentation

    Parameters
    ----------
    aws_batch_boto_client : Boto3 client object
        Boto3 client related with the AWS Batch service
    version : str
        Version related with the Compute Environment 
        that will follow all downstream resources
        as job definitions, jobs queues and jobs.

    Returns
    -------
    dict
        
        {
           "ResponseMetadata":{
              "RequestId":"00000000-0000-0000-0000-0000000000",
              "HTTPStatusCode":200,
              "HTTPHeaders":{
                 "date":"Sat, 01 Jan 2022 00:00:00 GMT",
                 "content-type":"application/json",
                 "content-length":"171",
                 "connection":"keep-alive",
                 "x-amzn-requestid":"00000000-0000-0000-0000-0000000000",
                 "access-control-allow-origin":"*",
                 "x-amz-apigw-id":"ABCDEFGHIJLMNO=",
                 "access-control-expose-headers":"X-amzn-errortype,X-amzn-requestid,X-amzn-errormessage,X-amzn-trace-id,X-amz-apigw-id,date",
                 "x-amzn-trace-id":"Root=1-00000000-0000-0000-0000-0000000000"
              },
              "RetryAttempts":0
           },
           "jobDefinitionName":"ml_batch_prediction_job_definition_v0_01",
           "jobDefinitionArn":"arn:aws:batch:sa-east-1:012345678910:job-definition/ml_batch_prediction_job_definition_v0_01:1",
           "revision":1
        }
    """



    aws_response = aws_batch_boto_client.register_job_definition(
        jobDefinitionName=f'ml_batch_prediction_job_definition_{version}',
        type='container',
        platformCapabilities=['FARGATE'],
        containerProperties={
            'image': '012345678910.dkr.ecr.sa-east-1.amazonaws.com/ml_batch_prediction_:latest',
            'resourceRequirements': [
                {
                    'value': '64000',
                    'type': 'MEMORY'
                },
                {
                    'value': '8',
                    'type': 'VCPU'
                }
            ],
            'networkConfiguration': {
                'assignPublicIp': 'ENABLED'
            },
            'command': [
                'python',
                'main.py'
            ],
            'jobRoleArn': application_service_role['Role']['Arn'],
            'executionRoleArn': application_service_role['Role']['Arn'],
            'environment': [
                {
                    'name': 'DB_USER',
                    'value': f"{os.environ.get('DB_USER')}",
                },
                {
                    'name': 'DB_HOST',
                    'value': f"{os.environ.get('DB_HOST')}",
                },
                {
                    'name': 'DB_PASS',
                    'value': f"{os.environ.get('DB_PASS')}",
                },
                {
                    'name': 'DB_PORT',
                    'value': f"{os.environ.get('DB_PORT')}",
                },
                {
                    'name': 'DB_NAME',
                    'value': f"{os.environ.get('DB_NAME')}",
                },
                {
                    'name': 'S3_BUCKET',
                    'value': f"{os.environ.get('S3_BUCKET')}",
                },
            ],
        },
    )

    print(aws_response)