300 lines
12 KiB
Python
300 lines
12 KiB
Python
import json
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import logging
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import os
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import re
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import uuid
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from dataclasses import dataclass
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from datetime import datetime
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import boto3
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from aws_lambda_powertools import Tracer
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from common.const import PERMISSION_ENDPOINT_ALL, PERMISSION_ENDPOINT_CREATE
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from common.ddb_service.client import DynamoDbUtilsService
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from common.excepts import BadRequestException
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from common.response import bad_request, accepted
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from libs.data_types import EndpointDeploymentJob
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from libs.enums import EndpointStatus, EndpointType
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from libs.utils import response_error, permissions_check
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tracer = Tracer()
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sagemaker_endpoint_table = os.environ.get('ENDPOINT_TABLE_NAME')
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aws_region = os.environ.get('AWS_REGION')
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s3_bucket_name = os.environ.get('S3_BUCKET_NAME')
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async_success_topic = os.environ.get('SNS_INFERENCE_SUCCESS')
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async_error_topic = os.environ.get('SNS_INFERENCE_ERROR')
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inference_ecr_image_url = os.environ.get("INFERENCE_ECR_IMAGE_URL")
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queue_url = os.environ.get('COMFY_QUEUE_URL')
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sync_table = os.environ.get('COMFY_SYNC_TABLE')
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instance_monitor_table = os.environ.get('COMFY_INSTANCE_MONITOR_TABLE')
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esd_version = os.environ.get("ESD_VERSION")
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logger = logging.getLogger(__name__)
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logger.setLevel(os.environ.get('LOG_LEVEL') or logging.ERROR)
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sagemaker = boto3.client('sagemaker')
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ddb_service = DynamoDbUtilsService(logger=logger)
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@dataclass
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class CreateEndpointEvent:
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instance_type: str
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autoscaling_enabled: bool
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assign_to_roles: [str]
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initial_instance_count: str
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max_instance_number: str = "1"
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min_instance_number: str = "0"
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endpoint_name: str = None
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# real-time / async
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endpoint_type: str = None
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custom_docker_image_uri: str = None
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custom_extensions: str = ""
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# service for: sd / comfy
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service_type: str = "sd"
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# todo will be removed
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creator: str = ""
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def check_custom_extensions(event: CreateEndpointEvent):
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if event.custom_extensions:
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logger.info(f"custom_extensions: {event.custom_extensions}")
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extensions_array = re.split('[ ,\n]+', event.custom_extensions)
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extensions_array = list(set(extensions_array))
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extensions_array = list(filter(None, extensions_array))
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for extension in extensions_array:
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pattern = r'^https://github\.com/[^#/]+/[^#/]+\.git#[^#]+#[a-fA-F0-9]{40}$'
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if not re.match(pattern, extension):
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raise BadRequestException(
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message=f"extension format is invalid: {extension}, valid format is like "
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f"https://github.com/awslabs/stable-diffusion-aws-extension.git#main#"
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f"a096556799b7b0686e19ec94c0dbf2ca74d8ffbc")
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# make extensions_array to string again
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event.custom_extensions = ','.join(extensions_array)
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logger.info(f"formatted custom_extensions: {event.custom_extensions}")
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if len(extensions_array) >= 3:
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raise BadRequestException(message="custom_extensions should be at most 3")
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return event
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def get_docker_image_uri(event: CreateEndpointEvent):
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# if it has custom extensions, then start from file image
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if event.custom_docker_image_uri:
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return event.custom_docker_image_uri
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return inference_ecr_image_url
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# POST /endpoints
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@tracer.capture_lambda_handler
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def handler(raw_event, ctx):
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try:
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logger.info(json.dumps(raw_event))
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event = CreateEndpointEvent(**json.loads(raw_event['body']))
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permissions_check(raw_event, [PERMISSION_ENDPOINT_ALL, PERMISSION_ENDPOINT_CREATE])
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if event.endpoint_type not in EndpointType.List.value:
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raise BadRequestException(message=f"{event.endpoint_type} endpoint is not supported yet")
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if int(event.initial_instance_count) < 1:
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raise BadRequestException(f"initial_instance_count should be at least 1: {event.endpoint_name}")
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if event.autoscaling_enabled:
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if event.endpoint_type == EndpointType.RealTime.value and int(event.min_instance_number) < 1:
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raise BadRequestException(
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f"min_instance_number should be at least 1 for real-time endpoint: {event.endpoint_name}")
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if event.endpoint_type == EndpointType.Async.value and int(event.min_instance_number) < 0:
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raise BadRequestException(
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f"min_instance_number should be at least 0 for async endpoint: {event.endpoint_name}")
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event = check_custom_extensions(event)
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endpoint_id = str(uuid.uuid4())
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short_id = endpoint_id[:7]
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if event.endpoint_name:
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short_id = event.endpoint_name
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endpoint_type = event.endpoint_type.lower()
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model_name = f"{event.service_type}-model-{endpoint_type}-{short_id}"
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endpoint_config_name = f"{event.service_type}-config-{endpoint_type}-{short_id}"
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endpoint_name = f"{event.service_type}-{endpoint_type}-{short_id}"
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model_data_url = f"s3://{s3_bucket_name}/data/model.tar.gz"
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s3_output_path = f"s3://{s3_bucket_name}/sagemaker_output/"
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initial_instance_count = int(event.initial_instance_count) if event.initial_instance_count else 1
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instance_type = event.instance_type
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endpoint_rows = ddb_service.scan(sagemaker_endpoint_table, filters=None)
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for endpoint_row in endpoint_rows:
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endpoint = EndpointDeploymentJob(**(ddb_service.deserialize(endpoint_row)))
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# Compatible with fields used in older data, endpoint.status must be 'deleted'
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if endpoint.endpoint_status != EndpointStatus.DELETED.value and endpoint.status != 'deleted':
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for role in event.assign_to_roles:
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if role in endpoint.owner_group_or_role:
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return bad_request(
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message=f"role [{role}] has a valid endpoint already, not allow to have another one")
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_create_sagemaker_model(model_name, model_data_url, endpoint_name, endpoint_id, event)
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try:
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if event.endpoint_type == EndpointType.RealTime.value:
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_create_endpoint_config_provisioned(endpoint_config_name, model_name,
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initial_instance_count, instance_type)
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elif event.endpoint_type == EndpointType.Async.value:
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_create_endpoint_config_async(endpoint_config_name, s3_output_path, model_name,
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initial_instance_count, instance_type, event)
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except Exception as e:
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logger.error(f"error creating endpoint config with exception: {e}")
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sagemaker.delete_model(ModelName=model_name)
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return bad_request(message=str(e))
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try:
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response = sagemaker.create_endpoint(
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EndpointName=endpoint_name,
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EndpointConfigName=endpoint_config_name
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)
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logger.info(f"Successfully created endpoint: {response}")
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except Exception as e:
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logger.error(f"error creating endpoint with exception: {e}")
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sagemaker.delete_endpoint_config(EndpointConfigName=endpoint_config_name)
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sagemaker.delete_model(ModelName=model_name)
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return bad_request(message=str(e))
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data = EndpointDeploymentJob(
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EndpointDeploymentJobId=endpoint_id,
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endpoint_name=endpoint_name,
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startTime=str(datetime.now()),
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endpoint_status=EndpointStatus.CREATING.value,
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autoscaling=event.autoscaling_enabled,
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owner_group_or_role=event.assign_to_roles,
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current_instance_count="0",
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instance_type=instance_type,
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endpoint_type=event.endpoint_type,
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min_instance_number=event.min_instance_number,
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max_instance_number=event.max_instance_number,
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custom_extensions=event.custom_extensions,
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service_type=event.service_type,
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).__dict__
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ddb_service.put_items(table=sagemaker_endpoint_table, entries=data)
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logger.info(f"Successfully created endpoint deployment: {data}")
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return accepted(
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message=f"Endpoint deployment started: {endpoint_name}",
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data=data
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)
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except Exception as e:
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return response_error(e)
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@tracer.capture_method
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def _create_sagemaker_model(name, model_data_url, endpoint_name, endpoint_id, event: CreateEndpointEvent):
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tracer.put_annotation('endpoint_name', endpoint_name)
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image_url = get_docker_image_uri(event)
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primary_container = {
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'Image': image_url,
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'ModelDataUrl': model_data_url,
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'Environment': {
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'LOG_LEVEL': os.environ.get('LOG_LEVEL') or logging.ERROR,
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'S3_BUCKET_NAME': s3_bucket_name,
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'IMAGE_URL': image_url,
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'INSTANCE_TYPE': event.instance_type,
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'ENDPOINT_NAME': endpoint_name,
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'ENDPOINT_ID': endpoint_id,
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'EXTENSIONS': event.custom_extensions,
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'CREATED_AT': datetime.utcnow().isoformat(),
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'COMFY_QUEUE_URL': queue_url or '',
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'COMFY_SYNC_TABLE': sync_table or '',
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'COMFY_INSTANCE_MONITOR_TABLE': instance_monitor_table or '',
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'ESD_VERSION': esd_version,
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'SERVICE_TYPE': event.service_type,
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'ON_DOCKER': 'true',
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},
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}
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tracer.put_metadata('primary_container', primary_container)
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logger.info(f"Creating model resource PrimaryContainer: {primary_container}")
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response = sagemaker.create_model(
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ModelName=name,
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PrimaryContainer=primary_container,
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ExecutionRoleArn=os.environ.get("EXECUTION_ROLE_ARN"),
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)
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logger.info(f"Successfully created model resource: {response}")
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def get_production_variants(model_name, instance_type, initial_instance_count):
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return [
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{
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'VariantName': 'prod',
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'ModelName': model_name,
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'InitialInstanceCount': initial_instance_count,
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'InstanceType': instance_type,
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"ModelDataDownloadTimeoutInSeconds": 60 * 30, # Specify the model download timeout in seconds.
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"ContainerStartupHealthCheckTimeoutInSeconds": 60 * 10, # Specify the health checkup timeout in seconds
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}
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]
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@tracer.capture_method
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def _create_endpoint_config_provisioned(endpoint_config_name, model_name, initial_instance_count,
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instance_type):
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production_variants = get_production_variants(model_name, instance_type, initial_instance_count)
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logger.info(f"Creating endpoint configuration ProductionVariants: {production_variants}")
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response = sagemaker.create_endpoint_config(
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EndpointConfigName=endpoint_config_name,
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ProductionVariants=production_variants
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)
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logger.info(f"Successfully created endpoint configuration: {response}")
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@tracer.capture_method
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def _create_endpoint_config_async(endpoint_config_name, s3_output_path, model_name, initial_instance_count,
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instance_type, event: CreateEndpointEvent):
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if event.service_type != "sd":
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success_topic = os.environ.get('COMFY_SNS_INFERENCE_SUCCESS')
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error_topic = os.environ.get('COMFY_SNS_INFERENCE_ERROR')
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else:
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success_topic = async_success_topic
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error_topic = async_error_topic
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async_inference_config = {
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"OutputConfig": {
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"S3OutputPath": s3_output_path,
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"NotificationConfig": {
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"SuccessTopic": success_topic,
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"ErrorTopic": error_topic
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}
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},
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"ClientConfig": {
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# (Optional) Specify the max number of inflight invocations per instance
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# If no value is provided, Amazon SageMaker will choose an optimal value for you
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"MaxConcurrentInvocationsPerInstance": 1
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}
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}
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production_variants = get_production_variants(model_name, instance_type, initial_instance_count)
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logger.info(f"Creating endpoint configuration AsyncInferenceConfig: {async_inference_config}")
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logger.info(f"Creating endpoint configuration ProductionVariants: {production_variants}")
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response = sagemaker.create_endpoint_config(
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EndpointConfigName=endpoint_config_name,
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AsyncInferenceConfig=async_inference_config,
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ProductionVariants=production_variants
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)
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logger.info(f"Successfully created endpoint configuration: {response}")
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