641 lines
24 KiB
Python
641 lines
24 KiB
Python
import time
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import logging
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import logging.config
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import os
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import traceback
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi import FastAPI, Request, HTTPException
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from fastapi.responses import JSONResponse
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from fastapi.exception_handlers import http_exception_handler
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from mangum import Mangum
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from common.response_wrapper import resp_err
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from common.enum import MessageEnum
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from common.constant import const
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from common.exception_handler import biz_exception
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from parse.parameter_parser import json_convert_to_payload
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from fastapi_pagination import add_pagination
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from datetime import datetime
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from typing import List
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import boto3
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from botocore.client import Config
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from botocore.exceptions import BotoCoreError, ClientError
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import json
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import uuid
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from sagemaker.predictor import Predictor
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from sagemaker.predictor_async import AsyncPredictor
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from sagemaker.serializers import JSONSerializer
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from sagemaker.deserializers import JSONDeserializer
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from boto3.dynamodb.conditions import Attr, Key
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logging.config.fileConfig('logging.conf', disable_existing_loggers=False)
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logger = logging.getLogger(const.LOGGER_API)
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STEP_FUNCTION_ARN = os.environ.get('STEP_FUNCTION_ARN')
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DDB_INFERENCE_TABLE_NAME = os.environ.get('DDB_INFERENCE_TABLE_NAME')
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DDB_TRAINING_TABLE_NAME = os.environ.get('DDB_TRAINING_TABLE_NAME')
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DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME = os.environ.get('DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME')
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REGION_NAME = os.environ['AWS_REGION']
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S3_BUCKET_NAME = os.environ.get('S3_BUCKET')
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ddb_client = boto3.resource('dynamodb')
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s3 = boto3.client('s3', region_name=REGION_NAME)
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sagemaker = boto3.client('sagemaker')
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inference_table = ddb_client.Table(DDB_INFERENCE_TABLE_NAME)
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endpoint_deployment_table = ddb_client.Table(DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME)
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# name for utils sagemaker endpoint name
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utils_endpoint_name = os.environ.get("SAGEMAKER_ENDPOINT_NAME")
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async def custom_exception_handler(request: Request, exc: HTTPException):
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headers = {
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"Access-Control-Allow-Headers": "Content-Type",
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "OPTIONS,POST,GET"
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}
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return JSONResponse(
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status_code=exc.status_code,
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content={"detail": exc.detail},
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headers=headers
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)
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app = FastAPI(
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title="API List of SageMaker Inference",
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version="0.9",
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)
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app.exception_handler(HTTPException)(custom_exception_handler)
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def get_uuid():
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uuid_str = str(uuid.uuid4())
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return uuid_str
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def getInferenceJobList():
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response = inference_table.scan()
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logger.info(f"inference job list response is {str(response)}")
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return response['Items']
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def getInferenceJob(inference_job_id):
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if not inference_job_id:
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logger.error("Invalid inference job id")
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raise ValueError("Inference job id must not be None or empty")
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try:
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resp = inference_table.query(
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KeyConditionExpression=Key('InferenceJobId').eq(inference_job_id)
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)
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logger.info(resp)
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record_list = resp['Items']
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if len(record_list) == 0:
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logger.error(f"No inference job info item for id: {inference_job_id}")
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raise ValueError(f"There is no inference job info item for id: {inference_job_id}")
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return record_list[0]
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except Exception as e:
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logger.error(f"Exception occurred when trying to query inference job with id: {inference_job_id}, exception is {str(e)}")
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raise
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def getEndpointDeploymentJobList():
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try:
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sagemaker = boto3.client('sagemaker')
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ddb = boto3.resource('dynamodb')
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endpoint_deployment_table = ddb.Table(DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME)
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response = endpoint_deployment_table.scan()
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logger.info(f"endpoint deployment job list response is {str(response)}")
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# Get the list of SageMaker endpoints
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list_results = sagemaker.list_endpoints()
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sagemaker_endpoints = [ep_info['EndpointName'] for ep_info in list_results['Endpoints']]
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logger.info(str(sagemaker_endpoints))
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# Filter the endpoint job list
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filtered_endpoint_jobs = []
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for job in response['Items']:
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if 'endpoint_name' in job:
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endpoint_name = job['endpoint_name']
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deployment_job_id = job['EndpointDeploymentJobId']
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if endpoint_name in sagemaker_endpoints:
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filtered_endpoint_jobs.append(job)
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else:
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# Remove the job item from the DynamoDB table if the endpoint doesn't exist in SageMaker
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endpoint_deployment_table.delete_item(Key={'EndpointDeploymentJobId': deployment_job_id})
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else:
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filtered_endpoint_jobs.append(job)
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return filtered_endpoint_jobs
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except ClientError as e:
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print(f"An error occurred: {e}")
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return []
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except Exception as e:
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print(f"An unexpected error occurred: {e}")
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return []
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def getEndpointDeployJob(endpoint_deploy_job_id):
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try:
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resp = endpoint_deployment_table.query(
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KeyConditionExpression=Key('EndpointDeploymentJobId').eq(endpoint_deploy_job_id)
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)
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logger.info(resp)
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except Exception as e:
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logger.error(e)
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record_list = resp['Items']
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if len(record_list) == 0:
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logger.error("There is no endpoint deployment job info item for id:" + endpoint_deploy_job_id)
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return {}
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return record_list[0]
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def getEndpointDeployJob_with_endpoint_name(endpoint_name):
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try:
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resp = endpoint_deployment_table.scan(
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FilterExpression=Attr('endpoint_name').eq(endpoint_name)
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)
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logger.info(resp)
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except Exception as e:
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logger.error(e)
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record_list = resp['Items']
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if len(record_list) == 0:
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logger.error("There is no endpoint deployment job info item with endpoint name:" + endpoint_name)
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return {}
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return record_list[0]
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def get_s3_objects(bucket_name, folder_name):
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# Ensure the folder name ends with a slash
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if not folder_name.endswith('/'):
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folder_name += '/'
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# List objects in the specified bucket and folder
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response = s3.list_objects_v2(Bucket=bucket_name, Prefix=folder_name)
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# Extract object names from the response
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object_names = [obj['Key'][len(folder_name):] for obj in response.get('Contents', []) if obj['Key'] != folder_name]
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return object_names
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def load_json_from_s3(bucket_name, key):
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# Get the JSON file from the specified bucket and key
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response = s3.get_object(Bucket=bucket_name, Key=key)
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json_file = response['Body'].read().decode('utf-8')
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# Load the JSON file into a dictionary
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data = json.loads(json_file)
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return data
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# Global exception capture
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stepf_client = boto3.client('stepfunctions')
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@app.get("/inference")
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def root():
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return {"message": const.SOLUTION_NAME}
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def get_curent_time():
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# Get the current time
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now = datetime.now()
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formatted_time = now.strftime("%Y-%m-%d-%H-%M-%S")
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return formatted_time
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@app.post("/inference/run-sagemaker-inference")
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async def run_sagemaker_inference(request: Request):
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try:
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logger.info('entering the run_sage_maker_inference function!')
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inference_id = get_uuid()
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payload_checkpoint_info = await request.json()
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print(f"!!!!!!!!!!input in json format {payload_checkpoint_info}")
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task_type = payload_checkpoint_info.get('task_type')
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print(f"Task Type: {task_type}")
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params_dict = load_json_from_s3(S3_BUCKET_NAME, 'config/aigc.json')
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# logger.info(json.dumps(params_dict))
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payload = json_convert_to_payload(params_dict, payload_checkpoint_info, task_type)
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print(f"input in json format:")
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def show_slim_dict(payload):
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pay_type = type(payload)
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if pay_type is dict:
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for k, v in payload.items():
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print(f"{k}")
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show_slim_dict(v)
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elif pay_type is list:
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for v in payload:
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print(f"list")
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show_slim_dict(v)
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elif pay_type is str:
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if len(payload) > 100:
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print(f" : {len(payload)} contents")
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else:
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print(f" : {payload}")
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else:
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print(f" : {payload}")
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show_slim_dict(payload)
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endpoint_name = payload["endpoint_name"]
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predictor = Predictor(endpoint_name)
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# adjust time out time to 1 hour
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initial_args = {}
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initial_args["InvocationTimeoutSeconds"]=3600
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predictor = AsyncPredictor(predictor, name=endpoint_name)
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predictor.serializer = JSONSerializer()
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predictor.deserializer = JSONDeserializer()
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prediction = predictor.predict_async(data=payload, initial_args=initial_args, inference_id=inference_id)
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output_path = prediction.output_path
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#put the item to inference DDB for later check status
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current_time = str(datetime.now())
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response = inference_table.put_item(
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Item={
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'InferenceJobId': inference_id,
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'startTime': current_time,
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'status': 'inprogress',
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'taskType': task_type
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})
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print(f"output_path is {output_path}")
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headers = {
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"Access-Control-Allow-Headers": "Content-Type",
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "OPTIONS,POST,GET"
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}
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response = JSONResponse(content={"inference_id": inference_id, "status": "inprogress", "endpoint_name": endpoint_name, "output_path": output_path}, headers=headers)
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return response
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except Exception as e:
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traceback.print_exc()
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logger.error(f"Error occurred: {str(e)}")
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# raise HTTPException(status_code=500, detail=f"An error occurred during processing.{str(e)}")
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headers = {
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"Access-Control-Allow-Headers": "Content-Type",
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"Access-Control-Allow-Origin": "*",
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"Access-Control-Allow-Methods": "OPTIONS,POST,GET"
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}
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current_time = get_curent_time()
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response = inference_table.put_item(
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Item={
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'InferenceJobId': inference_id,
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'startTime': current_time,
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'completeTime': current_time,
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'status': 'failure',
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'taskType': task_type or "unknown",
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'error': f"error info {str(e)}"}
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)
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response = JSONResponse(content={"inference_id": inference_id, "status":"failure", "error": f"error info {str(e)}"}, headers=headers)
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return response
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@app.post("/inference/deploy-sagemaker-endpoint")
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async def deploy_sagemaker_endpoint(request: Request):
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logger.info("entering the deploy_sagemaker_endpoint function!")
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endpoint_deployment_id = get_uuid()
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try:
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payload = await request.json()
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logger.info(f"input in json format {payload}")
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payload['endpoint_deployment_id'] = endpoint_deployment_id
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resp = stepf_client.start_execution(
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stateMachineArn=STEP_FUNCTION_ARN,
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input=json.dumps(payload)
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)
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#put the item to inference DDB for later check status
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current_time = str(datetime.now())
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response = endpoint_deployment_table.put_item(
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Item={
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'EndpointDeploymentJobId': endpoint_deployment_id,
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'startTime': current_time,
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'status': 'inprogress'
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})
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logger.info("trigger step-function with following response")
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logger.info(f"finish trigger step function for deployment with output {resp}")
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return 0
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except Exception as e:
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logger.error(f"error calling run-sagemaker-inference with exception: {e}")
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#put the item to inference DDB for later check status
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current_time = str(datetime.now())
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response = endpoint_deployment_table.put_item(
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Item={
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'EndpointDeploymentJobId': endpoint_deployment_id,
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'startTime': current_time,
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'status': 'failed',
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'completeTime': current_time,
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'error': str(e)
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})
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return 0
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@app.post("/inference/delete-sagemaker-endpoint")
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async def delete_sagemaker_endpoint(request: Request):
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logger.info("entering the delete_sagemaker_endpoint function!")
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try:
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payload = await request.json()
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delete_endpoint_list = payload.get('delete_endpoint_list', [])
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logger.info(f"delete endpoint list: {delete_endpoint_list}")
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# delete sagemaker endpoints and update DynamoDB in the same loop
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for endpoint in delete_endpoint_list:
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try:
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# check if endpoint exists
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try:
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response = sagemaker.describe_endpoint(EndpointName=endpoint)
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print(response)
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logger.info(f"Deleting endpoint: {endpoint}")
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# If the endpoint exists and you want to delete it, you can do so here:
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sagemaker.delete_endpoint(EndpointName=endpoint)
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except (BotoCoreError, ClientError) as error:
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if error.response['Error']['Code'] == 'ResourceNotFound':
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print("Endpoint not found, no need to delete.")
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else:
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# Handle other potential errors
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print(error)
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# update DynamoDB status
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resp = getEndpointDeployJob(endpoint)
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if resp:
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endpoint_deployment_job_id = resp['EndpointDeploymentJobId']
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logger.info(f"Updating DynamoDB status for: {endpoint_deployment_job_id}")
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endpoint_deployment_table.update_item(
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Key={
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'EndpointDeploymentJobId': endpoint_deployment_job_id
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},
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UpdateExpression="SET #s = :s",
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ExpressionAttributeNames={
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'#s': 'status'
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},
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ExpressionAttributeValues={
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':s': 'deleted'
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}
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)
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else:
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resp = getEndpointDeployJob_with_endpoint_name(endpoint)
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if resp:
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endpoint_deployment_job_id = resp['EndpointDeploymentJobId']
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logger.info(f"Updating DynamoDB status for: {endpoint_deployment_job_id}")
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endpoint_deployment_table.update_item(
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Key={
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'EndpointDeploymentJobId': endpoint_deployment_job_id
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},
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UpdateExpression="SET #s = :s",
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ExpressionAttributeNames={
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'#s': 'status'
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},
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ExpressionAttributeValues={
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':s': 'deleted'
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}
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)
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else:
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logger.error(f"No matching DynamoDB record found for endpoint: {endpoint}")
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except ClientError as e:
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if e.response['Error']['Code'] in ['ValidationException', 'ResourceNotFoundException']:
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logger.error(f"Endpoint or DynamoDB item {endpoint} does not exist, skipping")
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else:
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raise
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logger.info("Successfully processed endpoint deletions and status updates")
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return 0
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except Exception as e:
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logger.error(f"error deleting sagemaker endpoint with exception: {e}")
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return 0
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@app.get("/inference/list-endpoint-deployment-jobs")
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async def list_endpoint_deployment_jobs():
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logger.info(f"entering list_endpoint_deployment_jobs")
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return getEndpointDeploymentJobList()
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@app.get("/inference/list-inference-jobs")
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async def list_inference_jobs():
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logger.info(f"entering list_endpoint_deployment_jobs")
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return getInferenceJobList()
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@app.get("/inference/get-endpoint-deployment-job")
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async def get_endpoint_deployment_job(jobID: str = None):
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logger.info(f"entering get_endpoint_deployment_job function ")
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# endpoint_deployment_jobId = request.query_params
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endpoint_deployment_jobId = jobID
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logger.info(f"endpoint_deployment_jobId is {str(endpoint_deployment_jobId)}")
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return getEndpointDeployJob(endpoint_deployment_jobId)
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@app.get("/inference/get-inference-job")
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async def get_inference_job(jobID: str = None):
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inference_jobId = jobID
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logger.info(f"entering get_inference_job function with jobId: {inference_jobId}")
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try:
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return getInferenceJob(inference_jobId)
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except Exception as e:
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logger.error(f"Error getting inference job: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/inference/get-inference-job-image-output")
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async def get_inference_job_image_output(jobID: str = None) -> List[str]:
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inference_jobId = jobID
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if inference_jobId is None or inference_jobId.strip() == "":
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logger.info(f"jobId is empty string or None, just return empty string list")
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return []
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logger.info(f"Entering get_inference_job_image_output function with jobId: {inference_jobId}")
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try:
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job_record = getInferenceJob(inference_jobId)
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except Exception as e:
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logger.error(f"Error getting inference job: {str(e)}")
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return []
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# Assuming the job_record contains a list of image names
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image_names = job_record.get("image_names", [])
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presigned_urls = []
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for image_name in image_names:
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try:
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image_key = f"out/{inference_jobId}/result/{image_name}"
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presigned_url = generate_presigned_url(S3_BUCKET_NAME, image_key)
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presigned_urls.append(presigned_url)
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except Exception as e:
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logger.error(f"Error generating presigned URL for image {image_name}: {str(e)}")
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# Continue with the next image if this one fails
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continue
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return presigned_urls
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@app.get("/inference/get-inference-job-param-output")
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async def get_inference_job_param_output(jobID: str = None) -> List[str]:
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inference_jobId = jobID
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if inference_jobId is None or inference_jobId.strip() == "":
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logger.info(f"jobId is empty string or None, just return empty string list")
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return []
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logger.info(f"Entering get_inference_job_param_output function with jobId: {inference_jobId}")
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try:
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job_record = getInferenceJob(inference_jobId)
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except Exception as e:
|
|
logger.error(f"Error getting inference job: {str(e)}")
|
|
return []
|
|
|
|
presigned_url = ""
|
|
|
|
try:
|
|
json_key = f"out/{inference_jobId}/result/{inference_jobId}_param.json"
|
|
presigned_url = generate_presigned_url(S3_BUCKET_NAME, json_key)
|
|
except Exception as e:
|
|
logger.error(f"Error generating presigned URL: {str(e)}")
|
|
return []
|
|
|
|
return [presigned_url]
|
|
|
|
def generate_presigned_url(bucket_name: str, key: str, expiration=3600) -> str:
|
|
try:
|
|
response = s3.generate_presigned_url(
|
|
'get_object',
|
|
Params={'Bucket': bucket_name, 'Key': key},
|
|
ExpiresIn=expiration
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Error generating presigned URL: {e}")
|
|
raise
|
|
|
|
return response
|
|
|
|
|
|
@app.get("/inference/generate-s3-presigned-url-for-uploading")
|
|
async def generate_s3_presigned_url_for_uploading(s3_bucket_name: str = None, key: str = None):
|
|
if not s3_bucket_name:
|
|
s3_bucket_name = S3_BUCKET_NAME
|
|
|
|
if not key:
|
|
raise HTTPException(status_code=400, detail="Key parameter is required")
|
|
|
|
try:
|
|
presigned_url = s3.generate_presigned_url(
|
|
'put_object',
|
|
Params={
|
|
'Bucket': s3_bucket_name,
|
|
'Key': key,
|
|
'ContentType': 'text/plain;charset=UTF-8'
|
|
},
|
|
ExpiresIn=3600,
|
|
HttpMethod='PUT'
|
|
)
|
|
except Exception as e:
|
|
headers = {
|
|
"Access-Control-Allow-Headers": "*",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "OPTIONS,POST,GET,PUT"
|
|
}
|
|
return JSONResponse(content=str(e), status_code=500, headers=headers)
|
|
|
|
headers = {
|
|
"Access-Control-Allow-Headers": "*",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "OPTIONS,POST,GET,PUT"
|
|
}
|
|
|
|
response = JSONResponse(content=presigned_url, headers=headers)
|
|
|
|
return response
|
|
|
|
@app.get("/inference/get-texual-inversion-list")
|
|
async def get_texual_inversion_list():
|
|
logger.info(f"entering get_texual_inversion_list()")
|
|
return get_s3_objects(S3_BUCKET_NAME,'texual_inversion')
|
|
|
|
@app.get("/inference/get-lora-list")
|
|
async def get_lora_list():
|
|
return get_s3_objects(S3_BUCKET_NAME,'lora')
|
|
|
|
@app.get("/inference/get-hypernetwork-list")
|
|
async def get_hypernetwork_list():
|
|
return get_s3_objects(S3_BUCKET_NAME,'hypernetwork')
|
|
|
|
@app.get("/inference/get-controlnet-model-list")
|
|
async def get_controlnet_model_list():
|
|
return get_s3_objects(S3_BUCKET_NAME,'controlnet')
|
|
|
|
@app.post("/inference/run-model-merge")
|
|
async def run_model_merge(request: Request):
|
|
try:
|
|
logger.info('entering the run_model_merge function!')
|
|
|
|
# TODO: add logic for inference id
|
|
merge_id = get_uuid()
|
|
|
|
payload_checkpoint_info = await request.json()
|
|
print(f"!!!!!!!!!!input in json format {payload_checkpoint_info}")
|
|
|
|
params_dict = load_json_from_s3(S3_BUCKET_NAME, 'config/aigc.json')
|
|
|
|
logger.info(json.dumps(params_dict))
|
|
payload = json_convert_to_payload(params_dict, payload_checkpoint_info)
|
|
print(f"input in json format {payload}")
|
|
|
|
endpoint_name = payload["endpoint_name"]
|
|
|
|
predictor = Predictor(endpoint_name)
|
|
|
|
predictor = AsyncPredictor(predictor, name=endpoint_name)
|
|
predictor.serializer = JSONSerializer()
|
|
predictor.deserializer = JSONDeserializer()
|
|
prediction = predictor.predict_async(data=payload, inference_id=inference_id)
|
|
output_path = prediction.output_path
|
|
|
|
#put the item to inference DDB for later check status
|
|
current_time = get_curent_time()
|
|
response = inference_table.put_item(
|
|
Item={
|
|
'InferenceJobId': inference_id,
|
|
'startTime': current_time,
|
|
'status': 'inprogress'
|
|
})
|
|
print(f"output_path is {output_path}")
|
|
|
|
headers = {
|
|
"Access-Control-Allow-Headers": "Content-Type",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "OPTIONS,POST,GET"
|
|
}
|
|
|
|
response = JSONResponse(content={"inference_id": inference_id, "status": "inprogress", "endpoint_name": endpoint_name, "output_path": output_path}, headers=headers)
|
|
#response = JSONResponse(content={"inference_id": '6fa743f0-cb7a-496f-8205-dbd67df08be2', "status": "succeed", "output_path": ""}, headers=headers)
|
|
return response
|
|
|
|
except Exception as e:
|
|
logger.error(f"Error occurred: {str(e)}")
|
|
|
|
# raise HTTPException(status_code=500, detail=f"An error occurred during processing.{str(e)}")
|
|
headers = {
|
|
"Access-Control-Allow-Headers": "Content-Type",
|
|
"Access-Control-Allow-Origin": "*",
|
|
"Access-Control-Allow-Methods": "OPTIONS,POST,GET"
|
|
}
|
|
|
|
response = JSONResponse(content={"inference_id": inference_id, "status":"failure", "error": f"error info {str(e)}"}, headers=headers)
|
|
return response
|
|
|
|
|
|
|
|
#app.include_router(search) TODO: adding sub router for future
|
|
|
|
handler = Mangum(app)
|
|
add_pagination(app)
|