要在AWS Batch中始终为每个作业启动新的EC2实例,您可以使用EC2资源类型的Compute Environment,并将实例类型设置为EC2实例。以下是一个代码示例,说明如何创建一个具有此配置的AWS Batch作业定义。
import boto3
# 创建AWS Batch作业定义
def create_job_definition():
batch_client = boto3.client('batch')
# 定义作业容器的属性
container_properties = {
'image': 'your_docker_image',
'vcpus': 2,
'memory': 4096,
'command': ['your_script.py']
}
# 创建作业定义
response = batch_client.register_job_definition(
jobDefinitionName='your_job_definition',
type='container',
containerProperties=container_properties,
retryStrategy={
'attempts': 1
}
)
return response['jobDefinitionArn']
# 创建AWS Batch作业队列
def create_job_queue():
batch_client = boto3.client('batch')
# 创建计算环境
response = batch_client.create_compute_environment(
computeEnvironmentName='your_compute_environment',
type='EC2',
state='ENABLED',
computeResources={
'type': 'EC2',
'minvCpus': 0,
'maxvCpus': 10,
'instanceTypes': ['your_instance_type'],
'instanceRole': 'your_instance_role',
'subnets': ['your_subnet_id'],
'securityGroupIds': ['your_security_group_id']
}
)
# 创建作业队列
response = batch_client.create_job_queue(
jobQueueName='your_job_queue',
state='ENABLED',
priority=1,
computeEnvironmentOrder=[
{
'order': 1,
'computeEnvironment': 'your_compute_environment'
}
]
)
return response['jobQueueArn']
# 提交AWS Batch作业
def submit_job():
batch_client = boto3.client('batch')
response = batch_client.submit_job(
jobName='your_job',
jobQueue='your_job_queue',
jobDefinition='your_job_definition',
parameters={
'param1': 'value1',
'param2': 'value2'
}
)
return response['jobId']
# 主函数
def main():
job_definition_arn = create_job_definition()
job_queue_arn = create_job_queue()
job_id = submit_job()
print('Job submitted. Job ID: {}'.format(job_id))
if __name__ == '__main__':
main()
请注意,上述代码示例仅提供了一个基本的框架,并假设您已经设置了AWS凭证和适当的权限。