import time import logging import logging.config import os import traceback from fastapi.middleware.cors import CORSMiddleware from fastapi import FastAPI, Request, HTTPException from fastapi.responses import JSONResponse from fastapi.exception_handlers import http_exception_handler from mangum import Mangum from common.response_wrapper import resp_err from common.enum import MessageEnum from common.constant import const from common.exception_handler import biz_exception from parse.parameter_parser import json_convert_to_payload from fastapi_pagination import add_pagination from datetime import datetime from typing import List import boto3 from botocore.client import Config from botocore.exceptions import BotoCoreError, ClientError import json import uuid from sagemaker.predictor import Predictor from sagemaker.predictor_async import AsyncPredictor from sagemaker.serializers import JSONSerializer from sagemaker.deserializers import JSONDeserializer from boto3.dynamodb.conditions import Attr, Key logging.config.fileConfig('logging.conf', disable_existing_loggers=False) logger = logging.getLogger(const.LOGGER_API) STEP_FUNCTION_ARN = os.environ.get('STEP_FUNCTION_ARN') DDB_INFERENCE_TABLE_NAME = os.environ.get('DDB_INFERENCE_TABLE_NAME') DDB_TRAINING_TABLE_NAME = os.environ.get('DDB_TRAINING_TABLE_NAME') DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME = os.environ.get('DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME') REGION_NAME = os.environ['AWS_REGION'] S3_BUCKET_NAME = os.environ.get('S3_BUCKET') ddb_client = boto3.resource('dynamodb') s3 = boto3.client('s3', region_name=REGION_NAME) sagemaker = boto3.client('sagemaker') inference_table = ddb_client.Table(DDB_INFERENCE_TABLE_NAME) endpoint_deployment_table = ddb_client.Table(DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME) async def custom_exception_handler(request: Request, exc: HTTPException): headers = { "Access-Control-Allow-Headers": "Content-Type", "Access-Control-Allow-Origin": "*", "Access-Control-Allow-Methods": "OPTIONS,POST,GET" } return JSONResponse( status_code=exc.status_code, content={"detail": exc.detail}, headers=headers ) app = FastAPI( title="API List of SageMaker Inference", version="0.9", ) app.exception_handler(HTTPException)(custom_exception_handler) def get_uuid(): uuid_str = str(uuid.uuid4()) return uuid_str def getInferenceJobList(): response = inference_table.scan() logger.info(f"inference job list response is {str(response)}") return response['Items'] def build_filter_expression(end_time, endpoint, start_time, status, task_type): filter_expression = None if status: filter_expression = Attr('status').eq(status) if task_type: if filter_expression: filter_expression &= Attr('taskType').eq(task_type) else: filter_expression = Attr('taskType').eq(task_type) if start_time: if filter_expression: filter_expression &= Attr('startTime').gte(start_time) else: filter_expression = Attr('startTime').gte(start_time) if end_time: if filter_expression: filter_expression &= Attr('startTime').lte(end_time) else: filter_expression = Attr('startTime').lte(end_time) if endpoint: if filter_expression: filter_expression &= Attr('params.sagemaker_inference_endpoint_name').eq(endpoint) else: filter_expression = Attr('params.sagemaker_inference_endpoint_name').eq(endpoint) return filter_expression def query_inference_job_list(status: str, task_type: str, start_time: str, end_time: str, endpoint: str, checkpoint: str, limit: int): print(f"query_inference_job_list params are:{status},{task_type},{start_time},{end_time},{checkpoint},{endpoint}") try: response = None filter_expression = build_filter_expression(end_time, endpoint, start_time, status, task_type) if limit != const.PAGE_LIMIT_ALL and limit <= 0: logger.info(f"query inference job list error because of limit <0 {limit}") return "" if filter_expression: response = inference_table.scan( FilterExpression=filter_expression ) else: response = inference_table.scan() logger.info(f"query inference job list response is {str(response)}") if response: return filter_checkpoint_items(limit, checkpoint, response['Items']) return response except Exception as e: logger.info(f"query inference job list error ") logger.info(e) return "" def sort_by_start_time(item): return item.get("startTime", "") def filter_checkpoint_items(limit, checkpoint, items): items = sorted(items, key=sort_by_start_time, reverse=True) if checkpoint: filtered_data = [] for item in items: if "params" in item and "used_models" in item["params"]: used_models = item["params"]["used_models"].get("Stable-diffusion", []) for model in used_models: if "model_name" in model and model["model_name"] == checkpoint: filtered_data.append(item) if limit == const.PAGE_LIMIT_ALL: return filtered_data else: if len(filtered_data) >= limit: return filtered_data[0: limit] else: return filtered_data if limit == const.PAGE_LIMIT_ALL: return items else: if len(items) >= limit: return items[0: limit] else: return items def getInferenceJob(inference_job_id): if not inference_job_id: logger.error("Invalid inference job id") raise ValueError("Inference job id must not be None or empty") try: resp = inference_table.query( KeyConditionExpression=Key('InferenceJobId').eq(inference_job_id) ) # logger.info(resp) record_list = resp['Items'] if len(record_list) == 0: logger.error(f"No inference job info item for id: {inference_job_id}") raise ValueError(f"There is no inference job info item for id: {inference_job_id}") return record_list[0] except Exception as e: logger.error(f"Exception occurred when trying to query inference job with id: {inference_job_id}, exception is {str(e)}") raise def getEndpointDeploymentJobList(): try: sagemaker = boto3.client('sagemaker') ddb = boto3.resource('dynamodb') endpoint_deployment_table = ddb.Table(DDB_ENDPOINT_DEPLOYMENT_TABLE_NAME) response = endpoint_deployment_table.scan() logger.info(f"endpoint deployment job list response is {str(response)}") # Get the list of SageMaker endpoints list_results = sagemaker.list_endpoints() sagemaker_endpoints = [ep_info['EndpointName'] for ep_info in list_results['Endpoints']] logger.info(str(sagemaker_endpoints)) # Filter the endpoint job list filtered_endpoint_jobs = [] for job in response['Items']: if 'endpoint_name' in job: endpoint_name = job['endpoint_name'] deployment_job_id = job['EndpointDeploymentJobId'] if endpoint_name in sagemaker_endpoints: filtered_endpoint_jobs.append(job) else: # Remove the job item from the DynamoDB table if the endpoint doesn't exist in SageMaker endpoint_deployment_table.delete_item(Key={'EndpointDeploymentJobId': deployment_job_id}) else: filtered_endpoint_jobs.append(job) return filtered_endpoint_jobs except ClientError as e: print(f"An error occurred: {e}") return [] except Exception as e: print(f"An unexpected error occurred: {e}") return [] def getEndpointDeployJob(endpoint_deploy_job_id): try: resp = endpoint_deployment_table.query( KeyConditionExpression=Key('EndpointDeploymentJobId').eq(endpoint_deploy_job_id) ) logger.info(resp) except Exception as e: logger.error(e) record_list = resp['Items'] if len(record_list) == 0: logger.error("There is no endpoint deployment job info item for id:" + endpoint_deploy_job_id) return {} return record_list[0] def getEndpointDeployJob_with_endpoint_name(endpoint_name): try: resp = endpoint_deployment_table.scan( FilterExpression=Attr('endpoint_name').eq(endpoint_name) ) logger.info(resp) except Exception as e: logger.error(e) record_list = resp['Items'] if len(record_list) == 0: logger.error("There is no endpoint deployment job info item with endpoint name:" + endpoint_name) return {} return record_list[0] def get_s3_objects(bucket_name, folder_name): # Ensure the folder name ends with a slash if not folder_name.endswith('/'): folder_name += '/' # List objects in the specified bucket and folder response = s3.list_objects_v2(Bucket=bucket_name, Prefix=folder_name) # Extract object names from the response object_names = [obj['Key'][len(folder_name):] for obj in response.get('Contents', []) if obj['Key'] != folder_name] return object_names def load_json_from_s3(bucket_name, key): # Get the JSON file from the specified bucket and key response = s3.get_object(Bucket=bucket_name, Key=key) json_file = response['Body'].read().decode('utf-8') # Load the JSON file into a dictionary data = json.loads(json_file) return data # Global exception capture stepf_client = boto3.client('stepfunctions') @app.get("/inference") def root(): return {"message": const.SOLUTION_NAME} # def get_curent_time(): # # Get the current time # now = datetime.now() # formatted_time = now.strftime("%Y-%m-%d-%H-%M-%S") # return formatted_time @app.post("/inference/run-sagemaker-inference") @app.post("/inference-api/inference") async def run_sagemaker_inference(request: Request): try: logger.info('entering the run_sage_maker_inference function!') inference_id = get_uuid() payload_checkpoint_info = await request.json() print(f"!!!!!!!!!!input in json format {payload_checkpoint_info}") task_type = payload_checkpoint_info.get('task_type') print(f"Task Type: {task_type}") path = request.url.path logger.info(f'Path: {path}') if path == '/inference-api/inference': # Invoke by API logger.info('invoked by api') params_dict = load_json_from_s3(S3_BUCKET_NAME, 'template/inferenceTemplate.json') else: # Invoke by UI 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, task_type) print(f"input in json format:") checkpoint_name = None if task_type == 'img2img': checkpoint_name = params_dict['img2img_sagemaker_stable_diffusion_checkpoint'] elif task_type == 'txt2img': checkpoint_name = params_dict['txt2img_sagemaker_stable_diffusion_checkpoint'] def show_slim_dict(payload): pay_type = type(payload) if pay_type is dict: for k, v in payload.items(): print(f"{k}") show_slim_dict(v) elif pay_type is list: for v in payload: print(f"list") show_slim_dict(v) elif pay_type is str: if len(payload) > 100: print(f" : {len(payload)} contents") else: print(f" : {payload}") else: print(f" : {payload}") show_slim_dict(payload) endpoint_name = payload["endpoint_name"] predictor = Predictor(endpoint_name) # adjust time out time to 1 hour initial_args = {"InvocationTimeoutSeconds": 3600} predictor = AsyncPredictor(predictor, name=endpoint_name) predictor.serializer = JSONSerializer() predictor.deserializer = JSONDeserializer() prediction = predictor.predict_async(data=payload, initial_args=initial_args, inference_id=inference_id) output_path = prediction.output_path #put the item to inference DDB for later check status current_time = str(datetime.now()) response = inference_table.put_item( Item={ 'InferenceJobId': inference_id, 'startTime': current_time, 'status': 'inprogress', 'endpoint': endpoint_name, 'checkpoint': checkpoint_name, 'taskType': task_type }) 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) return response except Exception as e: traceback.print_exc() 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" } current_time = str(datetime.now()) response = inference_table.put_item( Item={ 'InferenceJobId': inference_id, 'startTime': current_time, 'completeTime': current_time, 'status': 'failure', 'endpoint': endpoint_name, 'checkpoint': checkpoint_name, 'taskType': task_type or "unknown", 'error': f"error info {str(e)}"} ) response = JSONResponse(content={"inference_id": inference_id, "status":"failure", "error": f"error info {str(e)}"}, headers=headers) return response @app.post("/inference/deploy-sagemaker-endpoint") async def deploy_sagemaker_endpoint(request: Request): logger.info("entering the deploy_sagemaker_endpoint function!") endpoint_deployment_id = get_uuid() try: payload = await request.json() logger.info(f"input in json format {payload}") payload['endpoint_deployment_id'] = endpoint_deployment_id # put the item to inference DDB for later check status # must insert item first current_time = str(datetime.now()) response = endpoint_deployment_table.put_item( Item={ 'EndpointDeploymentJobId': endpoint_deployment_id, 'startTime': current_time, 'endpoint_status': 'Creating', 'max_instance_number': payload['initial_instance_count'], 'autoscaling': payload['autoscaling_enabled'], 'owner_group_or_role': payload['assign_to_roles'] }) resp = stepf_client.start_execution( stateMachineArn=STEP_FUNCTION_ARN, input=json.dumps(payload) ) logger.info("trigger step-function with following response") logger.info(f"finish trigger step function for deployment with output {resp}") return 0 except Exception as e: logger.error(f"error calling run-sagemaker-inference with exception: {e}") #put the item to inference DDB for later check status current_time = str(datetime.now()) response = endpoint_deployment_table.put_item( Item={ 'EndpointDeploymentJobId': endpoint_deployment_id, 'startTime': current_time, 'status': 'failed', 'completeTime': current_time, 'error': str(e) }) return 0 @app.get("/inference/list-endpoint-deployment-jobs") async def list_endpoint_deployment_jobs(): logger.info(f"entering list_endpoint_deployment_jobs") return getEndpointDeploymentJobList() @app.get("/inference/list-inference-jobs") async def list_inference_jobs(): logger.info(f"entering list_endpoint_deployment_jobs") return getInferenceJobList() @app.post("/inference/query-inference-jobs") async def query_inference_jobs(request: Request): logger.info(f"entering query-inference-jobs") query_params = await request.json() logger.info(query_params) status = query_params.get('status') task_type = query_params.get('task_type') start_time = query_params.get('start_time') end_time = query_params.get('end_time') endpoint = query_params.get('endpoint') checkpoint = query_params.get('checkpoint') limit = query_params.get("limit") if query_params.get("limit") else const.PAGE_LIMIT_ALL logger.info(f"entering query-inference-jobs {status},{task_type},{start_time},{end_time},{checkpoint},{endpoint},{limit}") return query_inference_job_list(status, task_type, start_time, end_time, endpoint, checkpoint, limit) @app.get("/inference/get-endpoint-deployment-job") async def get_endpoint_deployment_job(jobID: str = None): logger.info(f"entering get_endpoint_deployment_job function ") # endpoint_deployment_jobId = request.query_params endpoint_deployment_jobId = jobID logger.info(f"endpoint_deployment_jobId is {str(endpoint_deployment_jobId)}") return getEndpointDeployJob(endpoint_deployment_jobId) @app.get("/inference/get-inference-job") async def get_inference_job(jobID: str = None): inference_jobId = jobID # logger.info(f"entering get_inference_job function with jobId: {inference_jobId}") try: return getInferenceJob(inference_jobId) except Exception as e: # logger.error(f"Error getting inference job: {str(e)}") raise HTTPException(status_code=500, detail=str(e)) @app.get("/inference/get-inference-job-image-output") async def get_inference_job_image_output(jobID: str = None) -> List[str]: inference_jobId = jobID if inference_jobId is None or inference_jobId.strip() == "": logger.info(f"jobId is empty string or None, just return empty string list") return [] logger.info(f"Entering get_inference_job_image_output function with jobId: {inference_jobId}") try: job_record = getInferenceJob(inference_jobId) except Exception as e: logger.error(f"Error getting inference job: {str(e)}") return [] # Assuming the job_record contains a list of image names image_names = job_record.get("image_names", []) presigned_urls = [] for image_name in image_names: try: image_key = f"out/{inference_jobId}/result/{image_name}" presigned_url = generate_presigned_url(S3_BUCKET_NAME, image_key) presigned_urls.append(presigned_url) except Exception as e: logger.error(f"Error generating presigned URL for image {image_name}: {str(e)}") # Continue with the next image if this one fails continue return presigned_urls @app.get("/inference/get-inference-job-param-output") async def get_inference_job_param_output(jobID: str = None) -> List[str]: inference_jobId = jobID if inference_jobId is None or inference_jobId.strip() == "": logger.info(f"jobId is empty string or None, just return empty string list") return [] logger.info(f"Entering get_inference_job_param_output function with jobId: {inference_jobId}") try: job_record = getInferenceJob(inference_jobId) 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}") task_type = payload_checkpoint_info.get('task_type') endpoint_name = payload["endpoint_name"] checkpoint_name = None if task_type == 'img2img': checkpoint_name = params_dict['img2img_sagemaker_stable_diffusion_checkpoint'] elif task_type == 'txt2img': checkpoint_name = params_dict['txt2img_sagemaker_stable_diffusion_checkpoint'] 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 = str(datetime.now()) response = inference_table.put_item( Item={ 'InferenceJobId': inference_id, 'startTime': current_time, 'status': 'inprogress', 'endpoint': endpoint_name, 'checkpoint': checkpoint_name, }) 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)