from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.google.cloud.operators.vertex_ai import CustomContainerTrainingJobOperator
from datetime import datetime
import logging

default_args = {
    'owner': 'data_engineering',
    'retries': 3,
    'retry_delay': timedelta(minutes=5)
}

with DAG(
    dag_id='llm_fine_tuning_pipeline',
    default_args=default_args,
    start_date=datetime(2024, 1, 1),
    schedule_interval='@weekly',
    catchup=False,
    description='Orchestrates end-to-end fine-tuning of generative AI models'
) as dag:

def prepare_training_data():
    from google.cloud import storage
    import pandas as pd
    import json

    # Initialize cloud storage client
    client = storage.Client()
    bucket = client.bucket('your-training-data-bucket')

    # Download raw dataset
    blob = bucket.blob('raw/llm_dataset.jsonl')
    raw_data = blob.download_as_text()

    # Preprocess and tokenize data
    processed_data = []
    for line in raw_data.split('\n'):
        if line.strip():
            record = json.loads(line)
            # Add preprocessing logic here
            processed_data.append(record)

    # Upload processed data back to cloud storage
    processed_blob = bucket.blob('processed/llm_dataset_processed.jsonl')
    processed_blob.upload_from_string('\n'.join([json.dumps(rec) for rec in processed_data]))

    logging.info("Data preparation completed successfully")

prepare_data_task = PythonOperator(
    task_id='prepare_training_data',
    python_callable=prepare_training_data,
    provide_context=True
)

training_task = CustomContainerTrainingJobOperator(
    task_id='fine_tune_llm',
    project_id='your-generative-ai-project',
    region='us-central1',
    display_name='llm-fine-tuning-task-{{ ds_nodash }}',
    container_uri='gcr.io/your-project/llm-training-container:latest',
    model_serving_container_image_uri='us-docker.pkg.dev/vertex-ai/prediction/tf2-gpu.2-12:latest',
    dataset_id='gs://your-training-data-bucket/processed/llm_dataset_processed.jsonl',
    machine_type='n1-highmem-32',
    accelerator_type='NVIDIA_TESLA_V100',
    accelerator_count=4,
    training_fraction_split=0.8,
    validation_fraction_split=0.2,
    args=['--epochs=10', '--batch_size=32', '--learning_rate=0.0001']
)

def evaluate_model_performance():
    from sklearn.metrics import accuracy_score, precision_score, recall_score
    import numpy as np

    # Load validation results from cloud storage
    # Calculate evaluation metrics
    metrics = {
        'perplexity': calculate_perplexity(),
        'accuracy': 0.92,
        'training_loss': 1.23
    }

    # Log metrics to cloud monitoring
    logging.info(f"Model evaluation completed: {metrics}")
    return metrics

evaluate_task = PythonOperator(
    task_id='evaluate_model',
    python_callable=evaluate_model_performance
)

from airflow.operators.python import BranchPythonOperator

def deployment_decision(**context):
    metrics = context['ti'].xcom_pull(task_ids='evaluate_model')
    if metrics.get('accuracy', 0) > 0.9:
        return 'deploy_model'
    else:
        return 'retrain_notification'

deployment_branch = BranchPythonOperator(
    task_id='deployment_decision',
    python_callable=deployment_decision,
    provide_context=True
)

# Define additional tasks for deployment and notification
deploy_task = PythonOperator(task_id='deploy_model', ...)
notify_task = PythonOperator(task_id='retrain_notification', ...)

# Establish complete workflow
prepare_data_task >> training_task >> evaluate_task >> deployment_branch
deployment_branch >> [deploy_task, notify_task]

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.amazon.aws.hooks.s3 import S3Hook
from airflow.providers.amazon.aws.operators.sagemaker import SageMakerTrainingOperator
from datetime import datetime, timedelta
import boto3

default_args = {
    'owner': 'generative_ai_team',
    'depends_on_past': False,
    'retries': 3,
    'retry_delay': timedelta(minutes=10),
    'email_on_retry': False
}

with DAG(
    'advanced_llm_fine_tuning_pipeline',
    default_args=default_args,
    description='Advanced pipeline for generative AI model development',
    start_date=datetime(2024, 1, 1),
    schedule_interval='@weekly',
    max_active_runs=1,
    catchup=False
) as dag:

def fetch_and_validate_training_data():
    s3_hook = S3Hook(aws_conn_id='aws_default')

    # Download dataset from S3
    training_file = s3_hook.download_file(
        key='datasets/llm/raw/training_data.jsonl',
        bucket_name='generative-ai-bucket',
        local_path='/tmp/'
    )

    # Validate data quality
    validation_results = validate_dataset_format(training_file)
    if not validation_results['valid']:
        raise ValueError(f"Dataset validation failed: {validation_results['errors']}")

    return training_file

fetch_data_task = PythonOperator(
    task_id='fetch_training_data',
    python_callable=fetch_and_validate_training_data,
    execution_timeout=timedelta(minutes=30)
)

def preprocess_generative_ai_data(**context):
    input_file = context['ti'].xcom_pull(task_ids='fetch_training_data')

    # Implement sophisticated preprocessing for generative AI
    preprocessor = GenerativeAIDataPreprocessor(
        tokenizer_name='EleutherAI/gpt-neox-20b',
        max_length=2048,
        chunk_size=512
    )

    processed_data = preprocessor.process_file(input_file)

    # Upload processed data to cloud storage
    s3_client = boto3.client('s3')
    s3_client.upload_file(
        processed_data['output_file'],
        'generative-ai-bucket',
        'datasets/llm/processed/training_data_processed.parquet'
    )

    return processed_data['metadata']

preprocess_task = PythonOperator(
    task_id='preprocess_data',
    python_callable=preprocess_generative_ai_data,
    provide_context=True
)

training_config = {
    'TrainingJobName': 'llm-fine-tuning-{{ ds_nodash }}',
    'AlgorithmSpecification': {
        'TrainingImage': '763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.13.1-transformers4.26.0-gpu-py39-cu117-ubuntu20.04',
        'TrainingInputMode': 'File'
    },
    'RoleArn': 'arn:aws:iam::123456789012:role/SageMakerRole',
    'InputDataConfig': [
        {
            'ChannelName': 'training',
            'DataSource': {
                'S3DataSource': {
                    'S3DataType': 'S3Prefix',
                    'S3Uri': 's3://generative-ai-bucket/datasets/llm/processed/',
                    'S3DataDistributionType': 'FullyReplicated'
                }
            }
        }
    ],
    'OutputDataConfig': {
        'S3OutputPath': 's3://generative-ai-bucket/models/llm/'
    },
    'ResourceConfig': {
        'InstanceType': 'ml.p4d.24xlarge',
        'InstanceCount': 2,
        'VolumeSizeInGB': 500
    },
    'StoppingCondition': {
        'MaxRuntimeInSeconds': 86400
    }
}

training_task = SageMakerTrainingOperator(
    task_id='fine_tune_llm',
    config=training_config,
    wait_for_completion=True,
    check_interval=60
)

def evaluate_generative_model(**context):
    training_job_name = context['ti'].xcom_pull(task_ids='fine_tune_llm')

    # Download model artifacts from cloud storage
    # Implement comprehensive evaluation metrics
    evaluation_metrics = {
        'perplexity': calculate_perplexity(),
        'bleu_score': calculate_bleu_score(),
        'human_evaluation_score': conduct_human_evaluation(),
        'bias_metrics': assess_model_bias()
    }

    # Log metrics to cloud monitoring service
    cloudwatch = boto3.client('cloudwatch')
    cloudwatch.put_metric_data(
        Namespace='GenerativeAI/ModelEvaluation',
        MetricData=[
            {
                'MetricName': 'Perplexity',
                'Value': evaluation_metrics['perplexity'],
                'Unit': 'None'
            }
        ]
    )

    return evaluation_metrics

evaluate_task = PythonOperator(
    task_id='evaluate_model',
    python_callable=evaluate_generative_model,
    provide_context=True
)

from airflow.operators.python import BranchPythonOperator

def deployment_criteria(**context):
    metrics = context['ti'].xcom_pull(task_ids='evaluate_model')

    deployment_thresholds = {
        'perplexity': 15.0,
        'bleu_score': 0.85,
        'human_evaluation_score': 4.0
    }

    if all(metrics[key] >= deployment_thresholds[key] for key in deployment_thresholds):
        return 'deploy_to_production'
    elif metrics['perplexity'] < 20.0:
        return 'deploy_to_staging'
    else:
        return 'trigger_retraining'

deployment_decision = BranchPythonOperator(
    task_id='deployment_decision',
    python_callable=deployment_criteria,
    provide_context=True
)

fetch_data_task >> preprocess_task >> training_task >> evaluate_task >> deployment_decision

# Define subsequent tasks for each branch
deploy_prod_task = PythonOperator(task_id='deploy_to_production', ...)
deploy_staging_task = PythonOperator(task_id='deploy_to_staging', ...)
retrain_task = PythonOperator(task_id='trigger_retraining', ...)

deployment_decision >> [deploy_prod_task, deploy_staging_task, retrain_task]

# Add the official Airflow Helm repository
helm repo add apache-airflow https://airflow.apache.org
helm repo update

# Install Airflow with custom values for generative AI workloads
helm install generative-ai-airflow apache-airflow/airflow \
  --namespace airflow \
  --create-namespace \
  --set executor=KubernetesExecutor \
  --set workers.keda.enabled=true \
  --set workers.resources.requests.memory="8Gi" \
  --set workers.resources.requests.cpu="2" \
  --set workers.resources.limits.memory="16Gi" \
  --set workers.resources.limits.cpu="4"

from airflow.models import Connection
from airflow import settings
import json

# Configure Google Cloud Storage connection for data access
gcs_connection = Connection(
    conn_id='generative_ai_gcs',
    conn_type='google_cloud_platform',
    extra=json.dumps({
        'extra__google_cloud_platform__project': 'your-generative-ai-project',
        'extra__google_cloud_platform__keyfile_dict': {
            'type': 'service_account',
            'project_id': 'your-project-id',
            'private_key_id': 'key-id',
            'private_key': '-----BEGIN PRIVATE KEY-----\n...\n-----END PRIVATE KEY-----\n',
            'client_email': 'service-account@your-project.iam.gserviceaccount.com',
            'client_id': 'client-id',
            'auth_uri': 'https://accounts.google.com/o/oauth2/auth',
            'token_uri': 'https://oauth2.googleapis.com/token'
        }
    })
)

# Configure AWS connection for SageMaker integration
aws_connection = Connection(
    conn_id='generative_ai_aws',
    conn_type='aws',
    extra=json.dumps({
        'region_name': 'us-east-1',
        'aws_access_key_id': 'your-access-key',
        'aws_secret_access_key': 'your-secret-key'
    })
)

# Add connections to Airflow database
session = settings.Session()
session.add_all([gcs_connection, aws_connection])
session.commit()

from airflow.operators.python import PythonOperator
from transformers import AutoTokenizer
import pandas as pd

def preprocess_llm_data():
    # Initialize tokenizer for generative AI model
    tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
    tokenizer.add_special_tokens({'pad_token': '[PAD]'})

    # Load and preprocess dataset from cloud storage
    dataset = load_dataset_from_gcs('gs://your-bucket/raw_data.jsonl')

    # Tokenize and chunk data for training
    processed_data = []
    for text in dataset:
        tokens = tokenizer(
            text,
            truncation=True,
            padding='max_length',
            max_length=2048,
            return_tensors='np'
        )
        processed_data.append(tokens)

    # Save processed data to cloud storage
    save_processed_data_to_gcs(processed_data, 'gs://your-bucket/processed_data/')

    return len(processed_data)

preprocess_task = PythonOperator(
    task_id='preprocess_llm_data',
    python_callable=preprocess_llm_data,
    execution_timeout=timedelta(hours=2)
)

from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from kubernetes.client import models as k8s

# Define resource requirements for generative AI training
resources = k8s.V1ResourceRequirements(
    requests={
        'memory': '64Gi',
        'cpu': '8',
        'nvidia.com/gpu': '4'
    },
    limits={
        'memory': '128Gi',
        'cpu': '16',
        'nvidia.com/gpu': '4'
    }
)

training_task = KubernetesPodOperator(
    task_id="train_generative_model",
    name="llm-training-pod",
    namespace="airflow",
    image="gcr.io/your-project/llm-training:latest",
    cmds=["python", "train_llm.py"],
    arguments=[
        "--dataset-path", "/mnt/data/processed",
        "--model-name", "EleutherAI/gpt-neox-20b",
        "--epochs", "10",
        "--batch-size", "4",
        "--learning-rate", "0.0001"
    ],
    volumes=[
        k8s.V1Volume(
            name="training-data",
            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                claim_name="generative-ai-pvc"
            )
        )
    ],
    volume_mounts=[
        k8s.V1VolumeMount(
            name="training-data",
            mount_path="/mnt/data",
            read_only=False
        )
    ],
    resources=resources,
    get_logs=True,
    is_delete_operator_pod=True,
    cluster_context="your-gke-cluster",
    config_file="/path/to/kubeconfig",
    startup_timeout_seconds=300
)

def evaluate_trained_model(**context):
    # Pull training results from XCom
    training_metrics = context['ti'].xcom_pull(task_ids='train_generative_model')

    # Implement evaluation logic
    evaluation_results = {
        'perplexity': calculate_perplexity(training_metrics),
        'training_loss': training_metrics['final_loss'],
        'convergence_rate': analyze_convergence(training_metrics['loss_history'])
    }

    # Deploy if metrics meet threshold
    if evaluation_results['perplexity'] < 20.0:
        deploy_to_serving_platform(training_metrics['model_path'])

    return evaluation_results

evaluate_task = PythonOperator(
    task_id='evaluate_trained_model',
    python_callable=evaluate_trained_model,
    provide_context=True
)

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.operators.dummy_operator import DummyOperator
from airflow.providers.google.cloud.operators.bigquery import BigQueryExecuteQueryOperator
from airflow.providers.google.cloud.sensors.gcs import GCSObjectExistenceSensor
from datetime import datetime, timedelta
import json
import requests

default_args = {
    'owner': 'generative_ai_team',
    'start_date': datetime(2024, 1, 1),
    'retries': 2,
    'retry_delay': timedelta(minutes=5),
    'execution_timeout': timedelta(hours=6)
}

with DAG('advanced_generative_ai_pipeline',
         default_args=default_args,
         schedule_interval='@daily',
         catchup=False,
         max_active_runs=1) as dag:

    # Sensor to wait for new input data
    wait_for_data = GCSObjectExistenceSensor(
        task_id='wait_for_new_data',
        bucket='generative-ai-input-bucket',
        object='daily_prompts/{{ ds }}/prompts.json',
        timeout=3600,
        poke_interval=300
    )

    # Data validation and preparation
    def validate_and_preprocess_data(**context):
        from google.cloud import storage
        import pandas as pd

        client = storage.Client()
        bucket = client.bucket('generative-ai-input-bucket')
        blob = bucket.blob(f"daily_prompts/{{{{ ds }}}}/prompts.json")

        # Download and validate prompt data
        prompt_data = json.loads(blob.download_as_text())

        validation_results = {
            'total_prompts': len(prompt_data),
            'valid_prompts': 0,
            'invalid_reasons': []
        }

        validated_prompts = []
        for prompt in prompt_data:
            if validate_prompt_structure(prompt):
                validated_prompts.append(preprocess_prompt(prompt))
                validation_results['valid_prompts'] += 1
            else:
                validation_results['invalid_reasons'].append('Invalid structure')

        # Save validated data for processing
        processed_bucket = client.bucket('generative-ai-processing-bucket')
        processed_blob = processed_bucket.blob(f"processed_prompts/{{{{ ds }}}/validated.json")
        processed_blob.upload_from_string(json.dumps(validated_prompts))

        context['ti'].xcom_push(key='validation_metrics', value=validation_results)
        return validation_results

    validate_task = PythonOperator(
        task_id='validate_and_preprocess_data',
        python_callable=validate_and_preprocess_data,
        provide_context=True
    )

    # Model inference with error handling and retry logic
    def call_generative_ai_api(**context):
        import time
        from tenacity import retry, stop_after_attempt, wait_exponential

        @retry(stop=stop_after_attempt(3), wait=wait_exponential(multiplier=1, min=4, max=10))
        def make_api_request(prompt):
            headers = {
                'Authorization': f'Bearer {API_KEY}',
                'Content-Type': 'application/json'
            }

            payload = {
                'model': 'gpt-4',
                'prompt': prompt,
                'max_tokens': 1000,
                'temperature': 0.7
            }

            response = requests.post(
                'https://api.openai.com/v1/completions',
                headers=headers,
                json=payload,
                timeout=30
            )

            if response.status_code == 429:
                raise Exception("Rate limit exceeded")
            response.raise_for_status()

            return response.json()['choices'][0]['text']

        validation_metrics = context['ti'].xcom_pull(
            task_ids='validate_and_preprocess_data',
            key='validation_metrics'
        )

        generated_responses = []
        for i in range(validation_metrics['valid_prompts']):
            try:
                response = make_api_request(f"Prompt {i}")
                generated_responses.append({
                    'prompt_id': i,
                    'generated_text': response,
                    'timestamp': datetime.utcnow().isoformat()
                })
            except Exception as e:
                logging.error(f"Failed to generate response for prompt {i}: {str(e)}")
                generated_responses.append({
                    'prompt_id': i,
                    'error': str(e),
                    'timestamp': datetime.utcnow().isoformat()
                })

        return generated_responses

    inference_task = PythonOperator(
        task_id='call_generative_ai_api',
        python_callable=call_generative_ai_api,
        provide_context=True,
        execution_timeout=timedelta(minutes=45)
    )

    # Post-processing and quality assessment
    def process_and_store_results(**context):
        generated_responses = context['ti'].xcom_pull(task_ids='call_generative_ai_api')

        # Implement quality filtering
        quality_metrics = {
            'total_generated': len(generated_responses),
            'successful_generations': 0,
            'quality_scores': []
        }

        processed_results = []
        for response in generated_responses:
            if 'generated_text' in response:
                quality_score = calculate_quality_score(response['generated_text'])
                response['quality_score'] = quality_score
                quality_metrics['quality_scores'].append(quality_score)

                if quality_score > 0.7:  # Quality threshold
                    quality_metrics['successful_generations'] += 1
                    processed_results.append(response)

        # Store results in BigQuery for analysis
        from google.cloud import bigquery
        client = bigquery.Client()

        table_ref = client.dataset('generative_ai').table('daily_generations')
        errors = client.insert_rows_json(table_ref, processed_results)

        if errors:
            raise Exception(f"BigQuery insertion errors: {errors}")

        return quality_metrics

    process_task = PythonOperator(
        task_id='process_and_store_results',
        python_callable=process_and_store_results,
        provide_context=True
    )

    # Define workflow dependencies
    start_pipeline = DummyOperator(task_id='start_pipeline')
    end_pipeline = DummyOperator(task_id='end_pipeline')

    start_pipeline >> wait_for_data >> validate_task >> inference_task >> process_task >> end_pipeline

from airflow.providers.google.cloud.operators.gcs import GCSToLocalFilesystemOperator
from airflow.operators.python import PythonOperator

def validate_generative_ai_dataset(**context):
    import pandas as pd
    from great_expectations.core import ExpectationSuite
    from great_expectations.dataset import PandasDataset

    local_file = context['ti'].xcom_pull(task_ids='download_training_data')
    df = pd.read_parquet(local_file)

    # Implement comprehensive data validation
    dataset = PandasDataset(df)
    validation_results = dataset.validate(
        expectation_suite=load_expectation_suite('generative_ai_data_validation')
    )

    if not validation_results['success']:
        raise ValueError(f"Data validation failed: {validation_results['results']}")

    return validation_results['statistics']

download_task = GCSToLocalFilesystemOperator(
    task_id='download_training_data',
    bucket='generative-ai-datasets',
    object_name='raw/training_data.parquet',
    filename='/tmp/training_data.parquet',
)

validate_task = PythonOperator(
    task_id='validate_dataset',
    python_callable=validate_generative_ai_dataset,
    provide_context=True
)

from airflow.providers.google.cloud.operators.dataflow import DataflowCreatePythonJobOperator

processing_task = DataflowCreatePythonJobOperator(
    task_id="process_training_data",
    job_name="generative-ai-data-processing-{{ ds_nodash }}",
    py_file='gs://generative-ai-scripts/data_processing.py',
    options={
        'project': 'your-generative-ai-project',
        'region': 'us-central1',
        'staging_location': 'gs://generative-ai-temp/staging/',
        'temp_location': 'gs://generative-ai-temp/temp/',
        'runner': 'DataflowRunner'
    },
    dataflow_default_options={
        'num_workers': 10,
        'max_num_workers': 50,
        'machine_type': 'n1-standard-4',
        'disk_size_gb': 100
    }
)

from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from kubernetes.client import models as k8s

training_task = KubernetesPodOperator(
    task_id="train_generative_model",
    name="generative-ai-training-{{ ds_nodash }}",
    namespace="airflow",
    image="gcr.io/your-project/generative-ai-training:latest",
    cmds=["python", "train_model.py"],
    arguments=[
        "--dataset-path", "/mnt/data/processed",
        "--model-type", "gpt-neo-2.7b",
        "--epochs", "5",
        "--batch-size", "8",
        "--learning-rate", "0.0002"
    ],
    volumes=[
        k8s.V1Volume(
            name="training-storage",
            persistent_volume_claim=k8s.V1PersistentVolumeClaimVolumeSource(
                claim_name="generative-ai-pvc"
            )
        )
    ],
    volume_mounts=[
        k8s.V1VolumeMount(
            name="training-storage",
            mount_path="/mnt/data",
            read_only=False
        )
    ],
    resources=k8s.V1ResourceRequirements(
        requests={
            'memory': '32Gi',
            'cpu': '8',
            'nvidia.com/gpu': '2'
        },
        limits={
            'memory': '64Gi',
            'cpu': '16',
            'nvidia.com/gpu': '2'
        }
    ),
    get_logs=True,
    is_delete_operator_pod=True,
    cluster_context="your-cloud-kubernetes-cluster",
    startup_timeout_seconds=600
)

def evaluate_and_deploy_model(**context):
    from sklearn.metrics import accuracy_score, classification_report
    import numpy as np

    # Load model artifacts and validation data
    model_metrics = context['ti'].xcom_pull(task_ids='train_generative_model')
    validation_data = load_validation_dataset()

    # Comprehensive model evaluation
    evaluation_results = {
        'perplexity': calculate_perplexity(model_metrics, validation_data),
        'accuracy': evaluate_accuracy(model_metrics, validation_data),
        'bias_metrics': assess_model_bias(model_metrics, validation_data),
        'resource_utilization': model_metrics['resource_usage']
    }

    # Deployment decision logic
    if (evaluation_results['perplexity'] < 15.0 and 
        evaluation_results['accuracy'] > 0.85):
        deploy_to_serving_infrastructure(model_metrics['model_path'])
        evaluation_results['deployment_status'] = 'success'
    else:
        evaluation_results['deployment_status'] = 'failed_metrics'

    # Log results to cloud monitoring
    log_evaluation_metrics(evaluation_results)

    return evaluation_results

evaluate_deploy_task = PythonOperator(
    task_id='evaluate_and_deploy_model',
    python_callable=evaluate_and_deploy_model,
    provide_context=True
)

def cleanup_training_resources(**context):
    from google.cloud import compute_v1

    # Clean up temporary cloud resources
    client = compute_v1.InstancesClient()

    # Identify and delete temporary training instances
    instances = client.list(project='your-project', zone='us-central1-a')

    for instance in instances:
        if instance.name.startswith('training-temp-'):
            client.delete(
                project='your-project',
                zone='us-central1-a',
                instance=instance.name
            )
            logging.info(f"Deleted temporary instance: {instance.name}")

    return {'cleaned_resources': len([i for i in instances if i.name.startswith('training-temp-')])}

cleanup_task = PythonOperator(
    task_id='cleanup_resources',
    python_callable=cleanup_training_resources,
    provide_context=True
)

download_task >> validate_task >> processing_task >> training_task >> evaluate_deploy_task >> cleanup_task

from airflow.providers.amazon.aws.operators.ec2 import EC2CreateInstanceOperator
from airflow.providers.amazon.aws.operators.ecs import EcsRunTaskOperator

# Provision GPU-enabled infrastructure for model serving
provision_infra = EC2CreateInstanceOperator(
    task_id='provision_gpu_infrastructure',
    image_id='ami-0c02fb55956c7d316',  # Deep Learning AMI
    instance_type='g4dn.2xlarge',
    min_count=1,
    max_count=1,
    key_name='generative-ai-keypair',
    security_group_ids=['sg-0123456789abcdef0'],
    subnet_id='subnet-0123456789abcdef0',
    aws_conn_id='aws_generative_ai',
    tag_specifications=[{
        'ResourceType': 'instance',
        'Tags': [{'Key': 'Purpose', 'Value': 'Generative-AI-Serving'}]
    }]
)

from airflow.providers.amazon.aws.operators.ecs import EcsRegisterTaskDefinitionOperator

# Register task definition for model serving container
register_task = EcsRegisterTaskDefinitionOperator(
    task_id='register_model_serving_task',
    family='generative-ai-serving',
    container_definitions=[{
        'name': 'llm-serving-container',
        'image': 'your-registry/llm-serving:latest',
        'memory': 16000,
        'cpu': 4096,
        'portMappings': [{
            'containerPort': 8080,
            'hostPort': 8080,
            'protocol': 'tcp'
        }],
        'environment': [
            {'name': 'MODEL_PATH', 'value': '/opt/ml/model'},
            {'name': 'MAX_SEQUENCE_LENGTH', 'value': '2048'}
        ],
        'resourceRequirements': [
            {'type': 'GPU', 'value': '1'}
        ]
    }],
    requires_compatibilities=['EC2'],
    network_mode='bridge',
    aws_conn_id='aws_generative_ai'
)

from airflow.providers.amazon.aws.operators.ecs import EcsCreateServiceOperator

deploy_service = EcsCreateServiceOperator(
    task_id='deploy_serving_service',
    cluster='generative-ai-cluster',
    service_name='llm-serving-service',
    task_definition='generative-ai-serving',
    desired_count=2,
    launch_type='EC2',
    load_balancers=[{
        'targetGroupArn': 'arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/llm-tg/1234567890123456',
        'containerName': 'llm-serving-container',
        'containerPort': 8080
    }],
    health_check_grace_period_seconds=300,
    aws_conn_id='aws_generative_ai'
)

from airflow.operators.python import PythonOperator
from airflow.providers.http.sensors.http import HttpSensor

def validate_model_endpoint():
    import requests
    import json

    test_payload = {
        "prompt": "Explain the benefits of generative AI",
        "max_tokens": 100,
        "temperature": 0.7
    }

    response = requests.post(
        'http://llm-endpoint.example.com/generate',
        json=test_payload,
        timeout=30
    )

    if response.status_code != 200:
        raise Exception(f"Endpoint validation failed: {response.text}")

    # Validate response structure and content
    result = response.json()
    if not validate_generation_output(result):
        raise Exception("Invalid response format from model endpoint")

    return {'status': 'healthy', 'response_time': response.elapsed.total_seconds()}

endpoint_sensor = HttpSensor(
    task_id='wait_for_endpoint_ready',
    http_conn_id='llm_endpoint',
    endpoint='health',
    request_params={},
    response_check=lambda response: response.status_code == 200,
    timeout=300,
    poke_interval=30
)

validate_deployment = PythonOperator(
    task_id='validate_model_deployment',
    python_callable=validate_model_endpoint,
    execution_timeout=timedelta(minutes=5)
)

from airflow.providers.amazon.aws.operators.elb import ElbModifyListenerOperator

def implement_canary_deployment(**context):
    from airflow.providers.amazon.aws.hooks.elb import ElbHook

    elb_hook = ElbHook(aws_conn_id='aws_generative_ai')

    # Implement canary deployment with 10% traffic to new version
    response = elb_hook.conn.modify_listener(
        ListenerArn='arn:aws:elasticloadbalancing:us-east-1:123456789012:listener/app/llm-load-balancer/1234567890123456/1234567890123456',
        DefaultActions=[{
            'Type': 'forward',
            'ForwardConfig': {
                'TargetGroups': [
                    {
                        'TargetGroupArn': 'arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/llm-v1/1234567890123456',
                        'Weight': 90  # Current version
                    },
                    {
                        'TargetGroupArn': 'arn:aws:elasticloadbalancing:us-east-1:123456789012:targetgroup/llm-v2/1234567890123456',
                        'Weight': 10  # New version
                    }
                ]
            }
        }]
    )

    return {'canary_deployment_status': 'initiated', 'traffic_split': '90/10'}

canary_deployment = PythonOperator(
    task_id='implement_canary_deployment',
    python_callable=implement_canary_deployment,
    provide_context=True
)

from airflow.providers.amazon.aws.operators.application_autoscaling import ApplicationAutoscalingRegisterScalableTargetOperator

configure_autoscaling = ApplicationAutoscalingRegisterScalableTargetOperator(
    task_id='configure_autoscaling',
    service_namespace='ecs',
    resource_id='service/generative-ai-cluster/llm-serving-service',
    scalable_dimension='ecs:service:DesiredCount',
    min_capacity=2,
    max_capacity=10,
    role_arn='arn:aws:iam::123456789012:role/aws-service-role/ecs.application-autoscaling.amazonaws.com/AWSServiceRoleForApplicationAutoScaling_ECSService',
    aws_conn_id='aws_generative_ai'
)

provision_infra >> register_task >> deploy_service >> endpoint_sensor >> validate_deployment >> canary_deployment >> configure_autoscaling

from airflow.providers.microsoft.azure.operators.azure_machine_learning import AzureMachineLearningCreateComputeTargetOperator
from airflow.providers.microsoft.azure.operators.azure_machine_learning import AzureMachineLearningDeleteComputeTargetOperator

# Create scalable compute cluster for generative AI training
create_compute = AzureMachineLearningCreateComputeTargetOperator(
    task_id='create_aml_compute_cluster',
    compute_name='gpu-cluster-genai-{{ ds_nodash }}',
    vm_size='Standard_NC6s_v3',  # NVIDIA Tesla V100 instance
    min_nodes=0,
    max_nodes=4,
    idle_seconds_before_scaledown=1800,
    remote_login_port_public_access='Enabled',
    aml_conn_id='azure_ml_connection'
)

from airflow.providers.microsoft.azure.operators.azure_machine_learning import AzureMachineLearningCreatePipelineJobOperator

training_config = {
    'description': 'Distributed training for generative language model',
    'properties': {
        'computeType': 'AmlCompute',
        'nodeCount': 2,
        'processCountPerNode': 1,
        'trainingType': 'DistributedTraining'
    },
    'tags': {'purpose': 'generative-ai-training', 'model-type': 'llm'}
}

submit_training = AzureMachineLearningCreatePipelineJobOperator(
    task_id='submit_distributed_training',
    pipeline_job=training_config,
    experiment_name='generative-ai-experiment-{{ ds_nodash }}',
    compute_target='gpu-cluster-genai-{{ ds_nodash }}',
    environment_name='pytorch-1.13-gpu',
    inputs={
        'training_data': {
            'type': 'uri_file',
            'path': 'https://generativeaistorage.blob.core.windows.net/datasets/training.parquet'
        }
    },
    aml_conn_id='azure_ml_connection'
)

from airflow.operators.python import PythonOperator
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

def monitor_training_progress(**context):
    credential = DefaultAzureCredential()
    ml_client = MLClient(
        credential=credential,
        subscription_id="your-subscription-id",
        resource_group_name="generative-ai-rg",
        workspace_name="genai-workspace"
    )

    job_name = context['ti'].xcom_pull(task_ids='submit_distributed_training')
    job = ml_client.jobs.get(name=job_name)

    metrics = {}
    while job.status not in ['Completed', 'Failed', 'Canceled']:
        # Collect real-time metrics
        latest_metrics = ml_client.jobs.get_metrics(job_name)
        metrics.update(latest_metrics)

        # Log progress to cloud monitoring
        log_metrics_to_app_insights(metrics)

        # Check for training issues
        if detect_training_anomalies(metrics):
            trigger_early_stopping(job_name)
            break

        time.sleep(300)  # Check every 5 minutes
        job = ml_client.jobs.get(name=job_name)

    return {
        'final_status': job.status,
        'collected_metrics': metrics,
        'training_duration': job.creation_context.last_modified_at - job.creation_context.created_at
    }

monitor_task = PythonOperator(
    task_id='monitor_training_progress',
    python_callable=monitor_training_progress,
    provide_context=True
)

from airflow.providers.microsoft.azure.operators.azure_machine_learning import AzureMachineLearningCreateModelOperator

register_model = AzureMachineLearningCreateModelOperator(
    task_id='register_trained_model',
    name='generative-llm',
    version='{{ ds_nodash }}',
    path=f'azureml://jobs/{{{{ ti.xcom_pull(task_ids="submit_distributed_training") }}}}/outputs/model',
    type='custom_model',
    description='Fine-tuned generative language model for text generation',
    tags={
        'training_date': '{{ ds }}',
        'model_family': 'gpt-neo',
        'parameters': '2.7B'
    },
    properties={
        'accuracy': '0.92',
        'perplexity': '12.3'
    },
    aml_conn_id='azure_ml_connection'
)

cleanup_compute = AzureMachineLearningDeleteComputeTargetOperator(
    task_id='cleanup_compute_resources',
    compute_name='gpu-cluster-genai-{{ ds_nodash }}',
    aml_conn_id='azure_ml_connection'
)

def generate_cost_report(**context):
    from azure.mgmt.costmanagement import CostManagementClient
    from azure.identity import DefaultAzureCredential
    from datetime import datetime, timedelta

    credential = DefaultAzureCredential()
    client = CostManagementClient(credential)

    # Generate cost analysis for generative AI resources
    time_period = {
        'from': (datetime.utcnow() - timedelta(days=1)),
        'to': datetime.utcnow()
    }

    scope = f"/subscriptions/your-subscription-id/resourceGroups/generative-ai-rg"

    query = {
        'type': 'ActualCost',
        'timeframe': 'Custom',
        'time_period': time_period,
        'dataset': {
            'granularity': 'Daily',
            'aggregation': {
                'totalCost': {'name': 'PreTaxCost', 'function': 'Sum'}
            },
            'grouping': [
                {
                    'type': 'Dimension',
                    'name': 'ResourceType'
                }
            ]
        }
    }

    result = client.query.usage(scope, query)

    cost_breakdown = {}
    for row in result.rows:
        resource_type = row[1]
        cost = row[0]
        cost_breakdown[resource_type] = cost

    # Send cost alert if threshold exceeded
    if sum(cost_breakdown.values()) > 1000:  # $1000 daily threshold
        send_cost_alert(cost_breakdown)

    return cost_breakdown

cost_reporting = PythonOperator(
    task_id='generate_cost_report',
    python_callable=generate_cost_report,
    provide_context=True
)

create_compute >> submit_training >> monitor_task >> register_model >> cleanup_compute >> cost_reporting

from airflow import DAG
from airflow.operators.python_operator import PythonOperator
from airflow.providers.google.cloud.operators.vertex_ai import CustomContainerTrainingJobOperator
from airflow.providers.google.cloud.hooks.vertex_ai import PipelineJobHook
from datetime import datetime, timedelta
import logging

default_args = {
    'owner': 'generative_ai_team',
    'start_date': datetime(2024, 1, 1),
    'retries': 2,
    'retry_delay': timedelta(minutes=10),
    'execution_timeout': timedelta(hours=12)
}

def optimize_data_loading():
    """Optimized data loading with compression and caching"""
    from google.cloud import storage
    import pyarrow.parquet as pq
    import pandas as pd

    client = storage.Client()
    bucket = client.bucket('optimized-genai-data')

    # Use columnar format for faster loading
    blob = bucket.blob('training_data/tokenized_dataset.parquet')
    local_path = '/tmp/tokenized_dataset.parquet'
    blob.download_to_filename(local_path)

    # Memory-efficient loading with pyarrow
    table = pq.read_table(local_path)
    df = table.to_pandas()

    # Implement data caching for repeated runs
    cache_processed_data(df, 'preprocessed_cache')

    logging.info(f"Loaded and cached {len(df)} training samples")
    return len(df)

def hyperparameter_optimization(**context):
    """Implement Bayesian optimization for hyperparameter tuning"""
    from skopt import gp_minimize
    from skopt.space import Real, Integer
    from skopt.utils import use_named_args

    # Define search space for generative AI parameters
    space = [
        Real(0.0001, 0.01, name='learning_rate'),
        Integer(16, 64, name='batch_size'),
        Real(0.1, 0.5, name='dropout_rate'),
        Integer(1, 5, name='num_layers')
    ]

    @use_named_args(space)
    def objective(**params):
        # Train model with given parameters and return validation loss
        validation_loss = train_with_parameters(params)
        return validation_loss

    # Run optimization
    result = gp_minimize(objective, space, n_calls=50, random_state=42)

    best_params = {
        'learning_rate': result.x[0],
        'batch_size': result.x[1],
        'dropout_rate': result.x[2],
        'num_layers': result.x[3]
    }

    context['ti'].xcom_push(key='optimized_params', value=best_params)
    return best_params

def distributed_training_optimized(**context):
    """Optimized distributed training with gradient accumulation"""
    optimized_params = context['ti'].xcom_pull(
        task_ids='hyperparameter_optimization',
        key='optimized_params'
    )

    training_config = {
        'project_id': 'your-generative-ai-project',
        'region': 'us-central1',
        'display_name': f'optimized-training-{datetime.now().strftime("%Y%m%d")}',
        'container_uri': 'gcr.io/your-project/optimized-training:latest',
        'model_serving_container_image_uri': 'us-docker.pkg.dev/vertex-ai/prediction/tf2-gpu.2-12:latest',
        'machine_type': 'a2-highgpu-8g',
        'accelerator_type': 'NVIDIA_TESLA_A100',
        'accelerator_count': 8,
        'replica_count': 4,
        'args': [
            f"--learning_rate={optimized_params['learning_rate']}",
            f"--batch_size={optimized_params['batch_size']}",
            f"--gradient_accumulation_steps=4",
            "--mixed_precision=true",
            "--use_cuda_graph=true"
        ]
    }

    # Submit optimized training job
    hook = PipelineJobHook()
    operation = hook.submit_training_job(training_config)
    return operation.name

def model_quantization_optimization():
    """Implement model quantization for faster inference"""
    import torch
    from transformers import AutoModelForCausalLM

    # Load trained model
    model = AutoModelForCausalLM.from_pretrained('/mnt/models/trained_model')

    # Apply dynamic quantization for CPU inference
    quantized_model = torch.quantization.quantize_dynamic(
        model, {torch.nn.Linear}, dtype=torch.qint8
    )

    # Measure performance improvement
    inference_time_original = benchmark_inference(model)
    inference_time_quantized = benchmark_inference(quantized_model)

    improvement = (inference_time_original - inference_time_quantized) / inference_time_original * 100

    logging.info(f"Quantization improved inference speed by {improvement:.2f}%")

    # Save optimized model
    torch.save(quantized_model.state_dict(), '/mnt/models/quantized_model.pth')
    return improvement

with DAG('optimized_gen_ai_pipeline', 
         default_args=default_args, 
         schedule_interval='@daily',
         catchup=False,
         max_active_runs=1) as dag:

    data_loading = PythonOperator(
        task_id='optimize_data_loading',
        python_callable=optimize_data_loading
    )

    hyperparameter_tuning = PythonOperator(
        task_id='hyperparameter_optimization',
        python_callable=hyperparameter_optimization,
        provide_context=True
    )

    training_task = PythonOperator(
        task_id='distributed_training_optimized',
        python_callable=distributed_training_optimized,
        provide_context=True
    )

    quantization_task = PythonOperator(
        task_id='model_quantization_optimization',
        python_callable=model_quantization_optimization
    )

    # Performance monitoring and reporting
    def generate_performance_report(**context):
        training_metrics = context['ti'].xcom_pull(task_ids='distributed_training_optimized')
        quantization_improvement = context['ti'].xcom_pull(task_ids='model_quantization_optimization')

        report = {
            'training_duration': training_metrics.get('duration'),
            'final_loss': training_metrics.get('loss'),
            'quantization_improvement': quantization_improvement,
            'resource_utilization': training_metrics.get('resource_usage'),
            'cost_breakdown': calculate_training_cost(training_metrics)
        }

        # Send to cloud monitoring
        send_to_stackdriver(report)
        return report

    performance_report = PythonOperator(
        task_id='generate_performance_report',
        python_callable=generate_performance_report,
        provide_context=True
    )

    # Define optimized workflow
    data_loading >> hyperparameter_tuning >> training_task >> quantization_task >> performance_report

def memory_optimized_training():
    """Implement memory-efficient training strategies"""
    import torch
    from transformers import Trainer, TrainingArguments

    # Enable gradient checkpointing
    training_args = TrainingArguments(
        output_dir='./results',
        per_device_train_batch_size=4,
        gradient_accumulation_steps=8,
        gradient_checkpointing=True,  # Trade compute for memory
        fp16=True,  # Mixed precision training
        dataloader_pin_memory=False,  # Reduce memory usage
    )

def cost_optimized_resource_selection():
    """Dynamically select most cost-effective cloud resources"""
    from google.cloud import compute_v1

    client = compute_v1.InstancesClient()

    # Compare GPU instance pricing
    instance_types = {
        'n1-highmem-8': {'gpu': 'V100', 'cost_per_hour': 2.48},
        'a2-highgpu-1g': {'gpu': 'A100', 'cost_per_hour': 3.07},
        'g2-standard-8': {'gpu': 'L4', 'cost_per_hour': 1.56}
    }

    # Select based on training requirements and budget
    optimal_instance = select_optimal_instance(instance_types, training_requirements)
    return optimal_instance

from airflow import DAG
from airflow.providers.cncf.kubernetes.operators.kubernetes_pod import KubernetesPodOperator
from airflow.providers.google.cloud.sensors.gcs import GCSObjectsWithPrefixExistenceSensor
from airflow.operators.python import PythonOperator
from kubernetes.client import models as k8s
from datetime import datetime, timedelta
import json
import logging

default_args = {
    'owner': 'generative_ai_team',
    'start_date': datetime(2024, 1, 1),
    'retries': 3,
    'retry_delay': timedelta(minutes=5)
}

def analyze_workload_demand(**context):
    """Analyze incoming workload and determine scaling requirements"""
    from google.cloud import storage

    client = storage.Client()
    bucket = client.bucket('generative-ai-requests')

    # Count pending prompt requests
    blobs = bucket.list_blobs(prefix='pending/')
    request_count = sum(1 for _ in blobs)

    # Determine required resources based on workload
    if request_count > 1000:
        replicas = 10
        gpu_count = 4
    elif request_count > 100:
        replicas = 4
        gpu_count = 2
    else:
        replicas = 1
        gpu_count = 1

    scaling_config = {
        'replicas': replicas,
        'gpu_count': gpu_count,
        'estimated_duration': estimate_processing_time(request_count),
        'resource_class': select_optimal_instance_type(request_count)
    }

    context['ti'].xcom_push(key='scaling_config', value=scaling_config)
    return scaling_config

def create_scaled_resources(**context):
    """Dynamically create Kubernetes resources based on scaling requirements"""
    scaling_config = context['ti'].xcom_pull(task_ids='analyze_workload_demand', key='scaling_config')

    # Define resource requirements based on scaling analysis
    resources = k8s.V1ResourceRequirements(
        requests={
            'memory': f"{32 * scaling_config['gpu_count']}Gi",
            'cpu': f"{8 * scaling_config['gpu_count']}",
            'nvidia.com/gpu': str(scaling_config['gpu_count'])
        },
        limits={
            'memory': f"{64 * scaling_config['gpu_count']}Gi",
            'cpu': f"{16 * scaling_config['gpu_count']}",
            'nvidia.com/gpu': str(scaling_config['gpu_count'])
        }
    )

    # Create Horizontal Pod Autoscaler configuration
    hpa_config = {
        'minReplicas': 1,
        'maxReplicas': scaling_config['replicas'],
        'metrics': [{
            'type': 'Resource',
            'resource': {
                'name': 'cpu',
                'target': {
                    'type': 'Utilization',
                    'averageUtilization': 70
                }
            }
        }]
    }

    return {'resources': resources, 'hpa_config': hpa_config}

with DAG('scalable_generative_ai_workflow',
         default_args=default_args,
         schedule_interval='*/15 * * * *',  # Run every 15 minutes
         catchup=False,
         max_active_runs=2) as dag:

    # Sensor to detect new generative AI requests
    request_sensor = GCSObjectsWithPrefixExistenceSensor(
        task_id='detect_new_requests',
        bucket='generative-ai-requests',
        prefix='pending/',
        timeout=300,
        poke_interval=60
    )

    workload_analysis = PythonOperator(
        task_id='analyze_workload_demand',
        python_callable=analyze_workload_demand,
        provide_context=True
    )

    resource_planning = PythonOperator(
        task_id='create_scaled_resources',
        python_callable=create_scaled_resources,
        provide_context=True
    )

    # Dynamic task generation based on workload
    def create_parallel_tasks(**context):
        scaling_config = context['ti'].xcom_pull(task_ids='analyze_workload_demand', key='scaling_config')
        resource_config = context['ti'].xcom_pull(task_ids='create_scaled_resources')

        parallel_tasks = []
        for i in range(scaling_config['replicas']):
            task = KubernetesPodOperator(
                task_id=f"generate_images_batch_{i}",
                name=f"image-generation-pod-{i}",
                namespace="airflow",
                image="registry.hub.docker.com/stabilityai/stable-diffusion:latest",
                cmds=["python", "batch_inference.py"],
                arguments=[
                    "--batch-size", "16",
                    "--prompt-file", f"gs://generative-ai-requests/batch_{i}.json",
                    "--output-dir", f"gs://generative-ai-output/batch_{i}"
                ],
                resources=resource_config['resources'],
                get_logs=True,
                is_delete_operator_pod=True,
                startup_timeout_seconds=300
            )
            parallel_tasks.append(task)

        return parallel_tasks

    parallel_processing = PythonOperator(
        task_id='create_parallel_tasks',
        python_callable=create_parallel_tasks,
        provide_context=True
    )

    # Comprehensive monitoring implementation
    def implement_real_time_monitoring(**context):
        from google.cloud import monitoring_v3
        import time

        client = monitoring_v3.MetricServiceClient()
        project_name = f"projects/your-generative-ai-project"

        # Monitor GPU utilization
        gpu_metric = {
            'type': 'custom.googleapis.com/gpu/utilization',
            'labels': {
                'workflow': 'generative_ai_image_generation'
            }
        }

        # Monitor inference latency
        latency_metric = {
            'type': 'custom.googleapis.com/inference/latency',
            'labels': {
                'workflow': 'generative_ai_image_generation'
            }
        }

        # Continuous monitoring loop
        start_time = time.time()
        monitoring_data = {
            'gpu_utilization': [],
            'inference_latency': [],
            'success_rate': []
        }

        while time.time() - start_time < 3600:  # Monitor for 1 hour
            # Collect metrics from running pods
            current_metrics = collect_pod_metrics()
            monitoring_data['gpu_utilization'].append(current_metrics['gpu_usage'])
            monitoring_data['inference_latency'].append(current_metrics['latency'])

            # Auto-scale based on metrics
            if current_metrics['gpu_usage'] > 0.8:  # 80% utilization threshold
                trigger_scale_up(current_metrics['pod_count'])
            elif current_metrics['gpu_usage'] < 0.3:  # 30% utilization threshold
                trigger_scale_down(current_metrics['pod_count'])

            time.sleep(60)  # Collect metrics every minute

        return monitoring_data

    monitoring_task = PythonOperator(
        task_id='implement_real_time_monitoring',
        python_callable=implement_real_time_monitoring,
        provide_context=True,
        execution_timeout=timedelta(hours=2)
    )

    # Performance reporting and optimization
    def generate_performance_insights(**context):
        monitoring_data = context['ti'].xcom_pull(task_ids='implement_real_time_monitoring')

        insights = {
            'average_gpu_utilization': sum(monitoring_data['gpu_utilization']) / len(monitoring_data['gpu_utilization']),
            'p95_inference_latency': calculate_percentile(monitoring_data['inference_latency'], 95),
            'total_images_generated': calculate_total_output(),
            'cost_per_image': calculate_cost_efficiency()
        }

        # Generate optimization recommendations
        recommendations = generate_optimization_recommendations(insights)
        insights['recommendations'] = recommendations

        # Log to cloud monitoring
        log_performance_metrics(insights)

        return insights

    performance_analysis = PythonOperator(
        task_id='generate_performance_insights',
        python_callable=generate_performance_insights,
        provide_context=True
    )

    # Define workflow dependencies
    request_sensor >> workload_analysis >> resource_planning >> parallel_processing
    parallel_processing >> monitoring_task >> performance_analysis

from airflow.models import BaseOperator
from airflow.utils.decorators import apply_defaults
from google.cloud import compute_v1
from google.api_core import operation
import time
import logging

class GCPGPUClusterOperator(BaseOperator):
    """
    Custom operator for dynamic GPU cluster management on Google Cloud
    """

    @apply_defaults
    def __init__(self,
                 cluster_name: str,
                 zone: str,
                 base_machine_type: str,
                 gpu_type: str,
                 gpu_count: int,
                 min_nodes: int,
                 max_nodes: int,
                 preemptible: bool = True,
                 *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.cluster_name = cluster_name
        self.zone = zone
        self.base_machine_type = base_machine_type
        self.gpu_type = gpu_type
        self.gpu_count = gpu_count
        self.min_nodes = min_nodes
        self.max_nodes = max_nodes
        self.preemptible = preemptible

    def execute(self, context):
        client = compute_v1.InstancesClient()

        # Calculate optimal cluster size based on workload
        pending_workload = self.assess_pending_workload()
        required_nodes = self.calculate_required_nodes(pending_workload)

        # Create instance templates with GPU configuration
        template = self.create_instance_template(required_nodes)

        # Deploy managed instance group
        mig_operation = self.create_managed_instance_group(template, required_nodes)

        # Wait for deployment completion
        self.wait_for_deployment(mig_operation)

        logging.info(f"GPU cluster {self.cluster_name} deployed with {required_nodes} nodes")
        return {
            'cluster_name': self.cluster_name,
            'node_count': required_nodes,
            'gpu_config': f"{self.gpu_count}x{self.gpu_type}"
        }

    def assess_pending_workload(self):
        """Analyze pending generative AI jobs from queue"""
        from google.cloud import tasks_v2

        client = tasks_v2.CloudTasksClient()
        parent = client.queue_path('your-project', 'us-central1', 'genai-queue')

        tasks = client.list_tasks(parent=parent)
        return sum(1 for _ in tasks)

    def calculate_required_nodes(self, pending_tasks):
        """Calculate optimal node count based on workload"""
        tasks_per_node = 4  # Estimated concurrent tasks per GPU node
        required_nodes = max(self.min_nodes, 
                           min(self.max_nodes, 
                               (pending_tasks + tasks_per_node - 1) // tasks_per_node))
        return required_nodes

    def create_instance_template(self, node_count):
        """Create optimized instance template for generative AI workloads"""
        template_client = compute_v1.InstanceTemplatesClient()

        # Configure GPU accelerators
        accelerators = []
        if self.gpu_count > 0:
            accelerators.append(
                compute_v1.AcceleratorConfig(
                    accelerator_count=self.gpu_count,
                    accelerator_type=f"zones/{self.zone}/acceleratorTypes/{self.gpu_type}"
                )
            )

        template_config = compute_v1.InstanceTemplate(
            name=f"{self.cluster_name}-template-{int(time.time())}",
            properties=compute_v1.InstanceProperties(
                machine_type=f"zones/{self.zone}/machineTypes/{self.base_machine_type}",
                disks=[
                    compute_v1.AttachedDisk(
                        boot=True,
                        auto_delete=True,
                        initialize_params=compute_v1.AttachedDiskInitializeParams(
                            source_image="projects/deeplearning-platform-release/global/images/family/common-cu121"
                        )
                    )
                ],
                network_interfaces=[
                    compute_v1.NetworkInterface(
                        network="global/networks/default",
                        access_configs=[compute_v1.AccessConfig(type="ONE_TO_ONE_NAT")]
                    )
                ],
                guest_accelerators=accelerators,
                scheduling=compute_v1.Scheduling(
                    preemptible=self.preemptible,
                    on_host_maintenance="TERMINATE"
                ),
                service_accounts=[
                    compute_v1.ServiceAccount(
                        email="default",
                        scopes=["https://www.googleapis.com/auth/cloud-platform"]
                    )
                ],
                metadata=compute_v1.Metadata(items=[
                    compute_v1.Items(key="install-nvidia-driver", value="True"),
                    compute_v1.Items(key="startup-script", value=self.get_startup_script())
                ])
            )
        )

        operation = template_client.insert(
            project='your-generative-ai-project',
            instance_template_resource=template_config
        )
        operation.result()
        return template_config.name

    def get_startup_script(self):
        """Provide startup script for GPU node configuration"""
        return """#!/bin/bash
        # Install generative AI dependencies
        pip install transformers torch accelerate
        # Configure GPU monitoring
        git clone https://github.com/NVIDIA/nvidia-docker.git
        """

from airflow.sensors.base import BaseSensorOperator
from airflow.utils.decorators import apply_defaults
from google.cloud import monitoring_v3
from google.cloud.monitoring_v3 import query

class GPUUtilizationSensor(BaseSensorOperator):
    """
    Custom sensor to monitor GPU utilization and trigger scaling events
    """

    @apply_defaults
    def __init__(self,
                 project_id: str,
                 zone: str,
                 utilization_threshold: float = 0.8,
                 *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.project_id = project_id
        self.zone = zone
        self.utilization_threshold = utilization_threshold

    def poke(self, context):
        client = monitoring_v3.MetricServiceClient()

        # Query GPU utilization metrics
        query_str = f"""
        resource.type = "gce_instance"
        resource.label.zone = "{self.zone}"
        metric.type = "compute.googleapis.com/instance/gpu/utilization"
        """

        results = client.list_time_series(
            name=f"projects/{self.project_id}",
            filter=query_str,
            interval=monitoring_v3.TimeInterval({
                "end_time": {"seconds": int(time.time())},
                "start_time": {"seconds": int(time.time()) - 300}  # Last 5 minutes
            })
        )

        # Calculate average utilization across all instances
        total_utilization = 0
        instance_count = 0

        for time_series in results:
            for point in time_series.points:
                total_utilization += point.value.double_value
                instance_count += 1

        if instance_count == 0:
            return False

        average_utilization = total_utilization / instance_count
        context['ti'].xcom_push(key='gpu_utilization', value=average_utilization)

        # Trigger scaling if utilization exceeds threshold
        return average_utilization > self.utilization_threshold

from airflow.plugins_manager import AirflowPlugin

class GenerativeAIPlugins(AirflowPlugin):
    name = "generative_ai_plugins"
    operators = [GCPGPUClusterOperator]
    sensors = [GPUUtilizationSensor]

# DAG implementation using custom plugins
from airflow import DAG
from datetime import datetime
from generative_ai_plugins import GCPGPUClusterOperator, GPUUtilizationSensor

with DAG('optimized_generative_ai_workflow',
         start_date=datetime(2024, 1, 1),
         schedule_interval='@daily') as dag:

    # Monitor GPU utilization
    gpu_monitor = GPUUtilizationSensor(
        task_id='monitor_gpu_utilization',
        project_id='your-generative-ai-project',
        zone='us-central1-a',
        utilization_threshold=0.75,
        timeout=3600,
        poke_interval=300
    )

    # Scale cluster based on utilization
    scale_cluster = GCPGPUClusterOperator(
        task_id='scale_gpu_cluster',
        cluster_name='genai-training-cluster',
        zone='us-central1-a',
        base_machine_type='n1-highmem-8',
        gpu_type='nvidia-tesla-v100',
        gpu_count=4,
        min_nodes=1,
        max_nodes=8,
        preemptible=True
    )

    # Training task using scaled resources
    training_task = KubernetesPodOperator(
        task_id="generative_ai_training",
        name="training-pod",
        namespace="airflow",
        image="gcr.io/your-project/llm-training:latest",
        cmds=["python", "train.py"],
        arguments=[
            "--cluster-name", "genai-training-cluster",
            "--epochs", "10"
        ],
        resources=Resources(request_memory="32Gi", request_cpu="8"),
        get_logs=True
    )

    gpu_monitor >> scale_cluster >> training_task

class ModelOptimizationOperator(BaseOperator):
    """Custom operator for model optimization techniques"""

    def execute(self, context):
        # Implement model quantization
        quantized_model = self.apply_quantization()

        # Apply pruning
        pruned_model = self.apply_pruning(quantized_model)

        # Optimize for inference
        optimized_model = self.optimize_for_inference(pruned_model)

        # Benchmark performance improvements
        benchmarks = self.benchmark_optimizations(optimized_model)

        return benchmarks

    def apply_quantization(self):
        import torch
        from transformers import AutoModelForCausalLM

        model = AutoModelForCausalLM.from_pretrained('model_path')
        quantized_model = torch.quantization.quantize_dynamic(
            model, {torch.nn.Linear}, dtype=torch.qint8
        )
        return quantized_model

    def benchmark_optimizations(self, model):
        original_latency = self.measure_inference_latency(self.original_model)
        optimized_latency = self.measure_inference_latency(model)

        improvement = (original_latency - optimized_latency) / original_latency * 100

        return {
            'latency_improvement': improvement,
            'memory_reduction': self.measure_memory_usage(model),
            'model_size_reduction': self.calculate_size_reduction(model)
        }

from airflow.providers.google.cloud.operators.bigquery import BigQueryExecuteQueryOperator
from airflow.operators.python import PythonOperator
import pandas as pd

def update_vector_database(**context):
    """Update vector database with new training data for RAG pipelines"""
    from google.cloud import aiplatform
    import numpy as np

    # Query new data from BigQuery
    new_data = context['ti'].xcom_pull(task_ids='extract_training_data')

    # Generate embeddings using cloud AI services
    embeddings = aiplatform.Featurestore(
        featurestore_name='generative_ai_embeddings'
    ).batch_embed(new_data['text'])

    # Update vector database (e.g., Pinecone, Weaviate)
    vector_db.update_vectors(embeddings, new_data['metadata'])

    return {'updated_vectors': len(embeddings), 'database_size': vector_db.size()}

update_vectors = PythonOperator(
    task_id='update_vector_database',
    python_callable=update_vector_database,
    provide_context=True
)

from airflow.providers.google.cloud.operators.vertex_ai import CustomPythonPackageTrainingJobOperator

multimodal_training = CustomPythonPackageTrainingJobOperator(
    task_id="train_multimodal_generative_ai",
    project_id="your-generative-ai-project",
    region="us-central1",
    python_package_gcs_uri="gs://your-bucket/training/multimodal_trainer-1.0.0.tar.gz",
    args=[
        "--model_type", "vision-language",
        "--text_data", "gs://your-data/text/",
        "--image_data", "gs://your-data/images/",
        "--batch_size", "32",
        "--learning_rate", "0.0001",
        "--use_tpu", "true"
    ],
    machine_type="cloud-tpu",
    tpu_config={
        "tpu_resource_name": "projects/your-project/locations/us-central1/nodes/your-tpu",
        "tpu_cores": 8
    }
)

def implement_canary_deployment(**context):
    """Advanced canary deployment with real-time traffic analysis"""
    from sklearn.metrics import accuracy_score
    import requests

    # Deploy new model version to canary environment
    new_endpoint = deploy_to_canary(context['ti'].xcom_pull(task_ids='train_multimodal_generative_ai'))

    # Route 10% of traffic to new version
    configure_traffic_split(current_endpoint=new_endpoint, split=0.1)

    # Monitor real-time performance
    performance_metrics = monitor_canary_performance(new_endpoint)

    # Automated rollback if performance degrades
    if performance_metrics['error_rate'] > 0.05:
        rollback_deployment()
        return {'deployment_status': 'rolled_back', 'reason': 'high_error_rate'}

    # Gradually increase traffic if performance is satisfactory
    if performance_metrics['latency'] < 100 and performance_metrics['accuracy'] > 0.95:
        scale_traffic_split(new_endpoint, 0.5)

    return {'deployment_status': 'in_progress', 'metrics': performance_metrics}

canary_deployment = PythonOperator(
    task_id='implement_canary_deployment',
    python_callable=implement_canary_deployment,
    provide_context=True
)

def orchestrate_federated_learning(**context):
    """Orchestrate federated learning across multiple edge devices"""
    from airflow.providers.http.operators.http import SimpleHttpOperator

    # Initialize federated learning round
    fl_round = initialize_federated_round()

    # Distribute model to edge devices
    edge_tasks = []
    for device in get_edge_devices():
        task = SimpleHttpOperator(
            task_id=f'train_on_device_{device.id}',
            http_conn_id=f'device_{device.id}',
            endpoint='/train',
            method='POST',
            data=json.dumps({
                'model_weights': fl_round.global_weights,
                'training_data': device.local_data_reference
            })
        )
        edge_tasks.append(task)

    # Aggregate results from devices
    aggregated_weights = aggregate_federated_results(edge_tasks)

    # Update global model
    updated_model = update_global_model(aggregated_weights)

    return {'round_completed': fl_round.id, 'model_improvement': calculate_improvement(updated_model)}

federated_learning = PythonOperator(
    task_id='orchestrate_federated_learning',
    python_callable=orchestrate_federated_learning,
    provide_context=True
)

from airflow import DAG
from airflow.operators.python import PythonOperator
from airflow.providers.amazon.aws.operators.sagemaker import SageMakerTrainingOperator
from airflow.providers.amazon.aws.operators.sagemaker import SageMakerModelOperator
from datetime import datetime, timedelta
import boto3
import json

def implement_advanced_preprocessing():
    """Advanced data preprocessing for generative AI models"""
    from transformers import AutoTokenizer
    import pandas as pd
    import numpy as np

    # Initialize tokenizer with special tokens for generative tasks
    tokenizer = AutoTokenizer.from_pretrained('EleutherAI/gpt-neox-20b')
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    # Load and preprocess dataset with efficient chunking
    dataset = load_large_dataset('s3://generative-ai-data/raw/')

    # Implement efficient tokenization with parallel processing
    tokenized_data = parallel_tokenize_dataset(dataset, tokenizer, chunk_size=1000)

    # Apply advanced preprocessing techniques
    processed_data = apply_data_augmentation(tokenized_data)
    processed_data = balance_dataset(processed_data)

    # Upload to cloud storage for training
    save_to_cloud_storage(processed_data, 's3://generative-ai-data/processed/')

    return {'processed_samples': len(processed_data), 'vocab_size': len(tokenizer)}

def optimized_hyperparameter_tuning(**context):
    """Implement sophisticated hyperparameter optimization"""
    from ray import tune
    from ray.tune.schedulers import ASHAScheduler

    # Define search space for generative AI parameters
    config = {
        "learning_rate": tune.loguniform(1e-5, 1e-2),
        "batch_size": tune.choice([16, 32, 64]),
        "num_layers": tune.randint(6, 24),
        "hidden_size": tune.choice([512, 1024, 2048]),
        "attention_heads": tune.randint(8, 32)
    }

    scheduler = ASHAScheduler(
        max_t=100,
        grace_period=10,
        reduction_factor=2
    )

    # Execute distributed hyperparameter search
    result = tune.run(
        train_function,
        resources_per_trial={"cpu": 4, "gpu": 1},
        config=config,
        num_samples=50,
        scheduler=scheduler,
        metric="perplexity",
        mode="min"
    )

    best_config = result.best_config
    context['ti'].xcom_push(key='optimized_hyperparams', value=best_config)
    return best_config

def deploy_with_blue_green_strategy(**context):
    """Implement blue-green deployment for generative AI models"""
    from airflow.providers.amazon.aws.operators.elastic_beanstalk import ElasticBeanstalkCreateEnvironmentOperator
    from airflow.providers.amazon.aws.operators.elastic_beanstalk import ElasticBeanstalkSwapEnvironmentUrlsOperator

    # Create new environment (green)
    create_green_env = ElasticBeanstalkCreateEnvironmentOperator(
        task_id='create_green_environment',
        application_name='generative-ai-app',
        environment_name='genai-green-{{ ds_nodash }}',
        solution_stack_name='64bit Amazon Linux 2 v3.4.0 running Python 3.8',
        version_label='genai-v{{ ds_nodash }}',
        option_settings=[
            {
                'Namespace': 'aws:elasticbeanstalk:application:environment',
                'OptionName': 'MODEL_PATH',
                'Value': 's3://generative-ai-models/{{ ds_nodash }}/'
            }
        ]
    )

    # Test green environment
    def validate_green_environment():
        # Comprehensive validation including load testing
        test_results = perform_validation_tests('green-environment-url')
        if test_results['success_rate'] < 0.99:
            raise Exception("Green environment validation failed")
        return test_results

    validate_green = PythonOperator(
        task_id='validate_green_environment',
        python_callable=validate_green_environment
    )

    # Swap environments
    swap_environments = ElasticBeanstalkSwapEnvironmentUrlsOperator(
        task_id='swap_blue_green_environments',
        source_environment_name='genai-blue',
        destination_environment_name='genai-green-{{ ds_nodash }}'
    )

    return create_green_env >> validate_green >> swap_environments

with DAG('enterprise_generative_ai_pipeline',
         start_date=datetime(2024, 1, 1),
         schedule_interval='@weekly',
         max_active_runs=1,
         catchup=False) as dag:

    data_preprocessing = PythonOperator(
        task_id='advanced_data_preprocessing',
        python_callable=implement_advanced_preprocessing
    )

    hyperparameter_optimization = PythonOperator(
        task_id='optimized_hyperparameter_tuning',
        python_callable=optimized_hyperparameter_tuning,
        provide_context=True
    )

    # Cloud-optimized training with spot instances
    training_config = {
        'TrainingJobName': 'generative-ai-{{ ds_nodash }}',
        'AlgorithmSpecification': {
            'TrainingImage': '763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:latest',
            'TrainingInputMode': 'File',
            'EnableSageMakerMetricsTimeSeries': True
        },
        'ResourceConfig': {
            'InstanceType': 'ml.p3.16xlarge',
            'InstanceCount': 4,
            'VolumeSizeInGB': 500
        },
        'StoppingCondition': {
            'MaxRuntimeInSeconds': 86400
        },
        'EnableManagedSpotTraining': True,
        'CheckpointConfig': {
            'S3Uri': 's3://generative-ai-checkpoints/',
            'LocalPath': '/opt/ml/checkpoints'
        }
    }

    training_task = SageMakerTrainingOperator(
        task_id='cloud_optimized_training',
        config=training_config,
        wait_for_completion=True,
        check_interval=120
    )

    deployment_strategy = PythonOperator(
        task_id='implement_deployment_strategy',
        python_callable=deploy_with_blue_green_strategy,
        provide_context=True
    )

    # Establish complete workflow
    data_preprocessing >> hyperparameter_optimization >> training_task >> deployment_strategy