Airflow和Luigi是两个常用的任务调度工具，可以用于自动化AWS EMR集群的创建和pyspark任务的部署。

下面是一个使用Airflow和Boto3库来自动创建AWS EMR集群的示例代码：

import boto3
from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from datetime import datetime

# AWS配置信息
AWS_ACCESS_KEY = ''
AWS_SECRET_KEY = ''
AWS_REGION = ''
EMR_CLUSTER_NAME = ''
EMR_RELEASE_LABEL = ''
EMR_MASTER_INSTANCE_TYPE = ''
EMR_SLAVE_INSTANCE_TYPE = ''
EMR_NUM_CORE_NODES = 

# 创建EMR集群
def create_emr_cluster():
    emr_client = boto3.client('emr', region_name=AWS_REGION,
                              aws_access_key_id=AWS_ACCESS_KEY,
                              aws_secret_access_key=AWS_SECRET_KEY)

    response = emr_client.run_job_flow(
        Name=EMR_CLUSTER_NAME,
        ReleaseLabel=EMR_RELEASE_LABEL,
        Instances={
            'InstanceGroups': [
                {
                    'Name': 'Master',
                    'Market': 'ON_DEMAND',
                    'InstanceRole': 'MASTER',
                    'InstanceType': EMR_MASTER_INSTANCE_TYPE,
                    'InstanceCount': 1,
                },
                {
                    'Name': 'Core',
                    'Market': 'ON_DEMAND',
                    'InstanceRole': 'CORE',
                    'InstanceType': EMR_SLAVE_INSTANCE_TYPE,
                    'InstanceCount': EMR_NUM_CORE_NODES,
                }
            ],
            'KeepJobFlowAliveWhenNoSteps': True,
            'TerminationProtected': False,
        },
        Applications=[
            {'Name': 'Spark'},
        ],
        VisibleToAllUsers=True,
        JobFlowRole='EMR_EC2_DefaultRole',
        ServiceRole='EMR_DefaultRole',
        Tags=[
            {
                'Key': 'Name',
                'Value': EMR_CLUSTER_NAME,
            },
        ],
    )

    cluster_id = response['JobFlowId']
    print(f'EMR Cluster created: {cluster_id}')

# 创建DAG
default_args = {
    'owner': 'airflow',
    'start_date': datetime(2022, 1, 1),
}

dag = DAG('emr_cluster_creation', default_args=default_args, schedule_interval='@once')

create_cluster_task = PythonOperator(
    task_id='create_emr_cluster',
    python_callable=create_emr_cluster,
    dag=dag
)

create_cluster_task

上述代码使用Boto3库与AWS EMR API进行通信，创建一个EMR集群。你可以根据实际情况修改AWS配置信息、EMR集群参数等。

接下来是一个使用Luigi来部署pyspark任务到AWS EMR集群的示例代码：

import luigi
from luigi.contrib.emr import PySparkStep
from datetime import date

# AWS配置信息
AWS_ACCESS_KEY = ''
AWS_SECRET_KEY = ''
AWS_REGION = ''
EMR_CLUSTER_ID = ''

# pyspark任务
class MyPySparkTask(luigi.contrib.emr.EMRJobRunnerTask):
    def requires(self):
        return []

    def output(self):
        return luigi.LocalTarget('/path/to/output')

    def emr_job_runner_steps(self):
        return [
            PySparkStep(
                name='My PySpark Job',
                script='s3:///path/to/your_script.py',
                py_files=['s3:///path/to/dependencies.py'],
                action_on_failure='CONTINUE',
                step_args=['arg1', 'arg2'],
                main_class=None,
                spark_submit=None,
                spark_submit_args=None,
                hadoop_streaming_jar=None,
                hadoop_streaming_main_class=None,
                spark_jars=None,
                spark_jars_ivy=None,
                spark_packages=None,
                spark_packages_ivy=None,
                spark_submit_env=None,
                spark_submit_classpath=None,
                spark_submit_py_files=None,
                spark_submit_files=None,
                spark_submit_conf=None,
                spark_submit_deploy_mode=None,
                spark_submit_driver_memory=None,
                spark_submit_driver_java_options=None,
                spark_submit_executor_memory=None,
                spark_submit_proxy_user=None,
                spark_submit_verbose=None,
                spark_submit_spark_conf=None,
                spark_submit_spark_properties=None,
                spark_submit_spark_files=None,
                spark_submit_spark_j