自定义托管服务的推理代码

FastAPI 是一个用于使用 Python APIs 进行构建的网络框架。

from fastapi import FastAPI, status, Request, Response
. . .
app = FastAPI()
. . .
@app.post('/invocations')
async def invocations(request: Request):
    # model() is a hypothetical function that gets the inference output:
    model_resp = await model(Request)

    response = Response(
        content=model_resp,
        status_code=status.HTTP_200_OK,
        media_type="text/plain",
    )
    return response
. . .

在此示例中，该invocations函数处理 SageMaker AI 向/invocations终端节点发送的推理请求。

Flask 是一个框架，用于通过 Python 开发 Web 应用程序。

import flask
. . .
app = flask.Flask(__name__)
. . .
@app.route('/invocations', methods=["POST"])
def invoke(request):
    # model() is a hypothetical function that gets the inference output:
    resp_body = model(request)
    return flask.Response(resp_body, mimetype='text/plain')
    

在此示例中，该invoke函数处理 SageMaker AI 向/invocations终端节点发送的推理请求。

FastAPI 是一个用于使用 Python APIs 进行构建的网络框架。

from starlette.responses import StreamingResponse
from fastapi import FastAPI, status, Request
. . .
app = FastAPI()
. . .
@app.post('/invocations')
async def invocations(request: Request):
    # Streams inference response using HTTP chunked encoding
    async def generate():
        # model() is a hypothetical function that gets the inference output:
        yield await model(Request)
        yield "\n"

    response = StreamingResponse(
        content=generate(),
        status_code=status.HTTP_200_OK,
        media_type="text/plain",
    )
    return response
. . .

在此示例中，该invocations函数处理 SageMaker AI 向/invocations终端节点发送的推理请求。为了流式处理响应，示例使用了 Starlette 框架中的 StreamingResponse 类。

Flask 是一个框架，用于通过 Python 开发 Web 应用程序。

import flask
. . .
app = flask.Flask(__name__)
. . .
@app.route('/invocations', methods=["POST"])
def invocations(request):
    # Streams inference response using HTTP chunked encoding

    def generate():
        # model() is a hypothetical function that gets the inference output:
        yield model(request)
        yield "\n"
    return flask.Response(
        flask.stream_with_context(generate()), mimetype='text/plain')
. . .

在此示例中，该invocations函数处理 SageMaker AI 向/invocations终端节点发送的推理请求。为了流式处理响应，示例使用了 Flask 框架中的 flask.stream_with_context 函数。

FastAPI 是一个用于使用 Python APIs 进行构建的网络框架。

import sys
import asyncio
import json
from fastapi import FastAPI, WebSocket, WebSocketDisconnect
from fastapi.responses import JSONResponse
import uvicorn

app = FastAPI()
...
@app.websocket("/invocations-bidirectional-stream")
async def websocket_invoke(websocket: WebSocket):
    """
    WebSocket endpoint with RFC 6455 ping/pong and fragmentation support
    
    Handles:
    - Text messages (JSON) - including fragmented frames
    - Binary messages - including fragmented frames
    - Ping frames (automatically responds with pong)
    - Pong frames (logs receipt)
    - Fragmented frames per RFC 6455 Section 5.4
    """
    await manager.connect(websocket)
    
    # Fragment reassembly buffers per RFC 6455 Section 5.4
    text_fragments = []
    binary_fragments = []
    
    while True:
        # Use receive() to handle all WebSocket frame types
        message = await websocket.receive()
        print(f"Received message: {message}")
        if message["type"] == "websocket.receive":
            if "text" in message:
                # Handle text frames (including fragments)
                text_data = message["text"]
                more_body = message.get("more_body", False)
                
                if more_body:
                    # This is a fragment, accumulate it
                    text_fragments.append(text_data)
                    print(f"Received text fragment: {len(text_data)} chars (more coming)")
                else:
                    # This is the final frame or a complete message
                    if text_fragments:
                        # Reassemble fragmented message
                        text_fragments.append(text_data)
                        complete_text = "".join(text_fragments)
                        text_fragments.clear()
                        print(f"Reassembled fragmented text message: {len(complete_text)} chars total")
                        await handle_text_message(websocket, complete_text)
                    else:
                        # Complete message in single frame
                        await handle_text_message(websocket, text_data)
                
            elif "bytes" in message:
                # Handle binary frames (including fragments)
                binary_data = message["bytes"]
                more_body = message.get("more_body", False)
                
                if more_body:
                    # This is a fragment, accumulate it
                    binary_fragments.append(binary_data)
                    print(f"Received binary fragment: {len(binary_data)} bytes (more coming)")
                else:
                    # This is the final frame or a complete message
                    if binary_fragments:
                        # Reassemble fragmented message
                        binary_fragments.append(binary_data)
                        complete_binary = b"".join(binary_fragments)
                        binary_fragments.clear()
                        print(f"Reassembled fragmented binary message: {len(complete_binary)} bytes total")
                        await handle_binary_message(websocket, complete_binary)
                    else:
                        # Complete message in single frame
                        await handle_binary_message(websocket, binary_data)
                
        elif message["type"] == "websocket.ping":
            # Handle ping frames - RFC 6455 Section 5.5.2
            ping_data = message.get("bytes", b"")
            print(f"Received PING frame with payload: {ping_data}")
            # FastAPI automatically sends pong response
            
        elif message["type"] == "websocket.pong":
            # Handle pong frames
            pong_data = message.get("bytes", b"")
            print(f"Received PONG frame with payload: {pong_data}")
            
        elif message["type"] == "websocket.close":
            # Handle close frames - RFC 6455 Section 5.5.1
            close_code = message.get("code", 1000)
            close_reason = message.get("reason", "")
            print(f"Received CLOSE frame - Code: {close_code}, Reason: '{close_reason}'")
            
            # Send close frame response if not already closing
            try:
                await websocket.close(code=close_code, reason=close_reason)
                print(f"Sent CLOSE frame response - Code: {close_code}")
            except Exception as e:
                print(f"Error sending close frame: {e}")
            break
            
        elif message["type"] == "websocket.disconnect":
            print("Client initiated disconnect")
            break

        else:
            print(f"Received unknown message type: {message['type']}")
            break

                        
async def handle_binary_message(websocket: WebSocket, binary_data: bytes):
    """Handle incoming binary messages (complete or reassembled from fragments)"""
    print(f"Processing complete binary message: {len(binary_data)} bytes")
    
    try:
        # Echo back the binary data
        await websocket.send_bytes(binary_data)
    except Exception as e:
        print(f"Error handling binary message: {e}")

async def handle_text_message(websocket: WebSocket, data: str):
    """Handle incoming text messages"""
    try:
        # Send response back to the same client
        await manager.send_personal_message(data, websocket)
    except Exception as e:
        print(f"Error handling text message: {e}")

def main():
    if len(sys.argv) > 1 and sys.argv[1] == "serve":
        print("Starting server on port 8080...")
        uvicorn.run(app, host="0.0.0.0", port=8080)
    else:
        print("Usage: python app.py serve")
        sys.exit(1)

if __name__ == "__main__":
    main()

在此示例中，该websocket_invoke函数处理 SageMaker AI 向/invocations-bidirectional-stream终端节点发送的推理请求。它显示了如何处理流请求和将响应流回客户端。