mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
29325a7214
Around the time we (I) implemented pydantic events, I noticed a short pause between progress images every 4 or 5 steps when generating with SDXL. It didn't happen with SD1.5, but I did notice that with SD1.5, we'd get 4 or 5 progress events simultaneously. I'd expect one event every ~25ms, matching my it/s with SD1.5. Mysterious! Digging in, I found an issue is related to our use of a synchronous queue for events. When the event queue is empty, we must call `asyncio.sleep` before checking again. We were sleeping for 100ms. Said another way, every time we clear the event queue, we have to wait 100ms before another event can be dispatched, even if it is put on the queue immediately after we start waiting. In practice, this means our events get buffered into batches, dispatched once every 100ms. This explains why I was getting batches of 4 or 5 SD1.5 progress events at once, but not the intermittent SDXL delay. But this 100ms wait has another effect when the events are put on the queue in intervals that don't perfectly line up with the 100ms wait. This is most noticeable when the time between events is >100ms, and can add up to 100ms delay before the event is dispatched. For example, say the queue is empty and we start a 100ms wait. Then, immediately after - like 0.01ms later - we push an event on to the queue. We still need to wait another 99.9ms before that event will be dispatched. That's the SDXL delay. The easy fix is to reduce the sleep to something like 0.01 seconds, but this feels kinda dirty. Can't we just wait on the queue and dispatch every event immediately? Not with the normal synchronous queue - but we can with `asyncio.Queue`. I switched the events queue to use `asyncio.Queue` (as seen in this commit), which lets us asynchronous wait on the queue in a loop. Unfortunately, I ran into another issue - events now felt like their timing was inconsistent, but in a different way than with the 100ms sleep. The time between pushing events on the queue and dispatching them was not consistently ~0ms as I'd expect - it was highly variable from ~0ms up to ~100ms. This is resolved by passing the asyncio loop directly into the events service and using its methods to create the task and interact with the queue. I don't fully understand why this resolved the issue, because either way we are interacting with the same event loop (as shown by `asyncio.get_running_loop()`). I suppose there's some scheduling magic happening.
212 lines
7.1 KiB
Python
212 lines
7.1 KiB
Python
import asyncio
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import logging
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import mimetypes
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import socket
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from contextlib import asynccontextmanager
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from pathlib import Path
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import torch
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import uvicorn
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from fastapi import FastAPI
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.middleware.gzip import GZipMiddleware
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from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
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from fastapi.responses import HTMLResponse
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from fastapi_events.handlers.local import local_handler
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from fastapi_events.middleware import EventHandlerASGIMiddleware
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from torch.backends.mps import is_available as is_mps_available
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# for PyCharm:
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# noinspection PyUnresolvedReferences
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import invokeai.backend.util.hotfixes # noqa: F401 (monkeypatching on import)
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import invokeai.frontend.web as web_dir
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from invokeai.app.api.dependencies import ApiDependencies
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from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
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from invokeai.app.api.routers import (
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app_info,
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board_images,
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boards,
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download_queue,
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images,
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model_manager,
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session_queue,
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utilities,
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workflows,
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)
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from invokeai.app.api.sockets import SocketIO
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from invokeai.app.services.config.config_default import get_config
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from invokeai.app.util.custom_openapi import get_openapi_func
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from invokeai.backend.util.devices import TorchDevice
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from invokeai.backend.util.logging import InvokeAILogger
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app_config = get_config()
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if is_mps_available():
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import invokeai.backend.util.mps_fixes # noqa: F401 (monkeypatching on import)
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logger = InvokeAILogger.get_logger(config=app_config)
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# fix for windows mimetypes registry entries being borked
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# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
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mimetypes.add_type("application/javascript", ".js")
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mimetypes.add_type("text/css", ".css")
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torch_device_name = TorchDevice.get_torch_device_name()
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logger.info(f"Using torch device: {torch_device_name}")
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loop = asyncio.new_event_loop()
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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# Add startup event to load dependencies
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ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, loop=loop, logger=logger)
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yield
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# Shut down threads
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ApiDependencies.shutdown()
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# Create the app
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# TODO: create this all in a method so configuration/etc. can be passed in?
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app = FastAPI(
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title="Invoke - Community Edition",
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docs_url=None,
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redoc_url=None,
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separate_input_output_schemas=False,
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lifespan=lifespan,
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)
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# Add event handler
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event_handler_id: int = id(app)
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app.add_middleware(
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EventHandlerASGIMiddleware,
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handlers=[local_handler], # TODO: consider doing this in services to support different configurations
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middleware_id=event_handler_id,
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)
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socket_io = SocketIO(app)
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app.add_middleware(
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CORSMiddleware,
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allow_origins=app_config.allow_origins,
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allow_credentials=app_config.allow_credentials,
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allow_methods=app_config.allow_methods,
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allow_headers=app_config.allow_headers,
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)
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app.add_middleware(GZipMiddleware, minimum_size=1000)
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# Include all routers
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app.include_router(utilities.utilities_router, prefix="/api")
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app.include_router(model_manager.model_manager_router, prefix="/api")
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app.include_router(download_queue.download_queue_router, prefix="/api")
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app.include_router(images.images_router, prefix="/api")
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app.include_router(boards.boards_router, prefix="/api")
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app.include_router(board_images.board_images_router, prefix="/api")
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app.include_router(app_info.app_router, prefix="/api")
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app.include_router(session_queue.session_queue_router, prefix="/api")
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app.include_router(workflows.workflows_router, prefix="/api")
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app.openapi = get_openapi_func(app)
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@app.get("/docs", include_in_schema=False)
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def overridden_swagger() -> HTMLResponse:
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return get_swagger_ui_html(
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openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
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title=f"{app.title} - Swagger UI",
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swagger_favicon_url="static/docs/invoke-favicon-docs.svg",
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)
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@app.get("/redoc", include_in_schema=False)
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def overridden_redoc() -> HTMLResponse:
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return get_redoc_html(
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openapi_url=app.openapi_url, # type: ignore [arg-type] # this is always a string
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title=f"{app.title} - Redoc",
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redoc_favicon_url="static/docs/invoke-favicon-docs.svg",
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)
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web_root_path = Path(list(web_dir.__path__)[0])
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try:
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app.mount("/", NoCacheStaticFiles(directory=Path(web_root_path, "dist"), html=True), name="ui")
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except RuntimeError:
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logger.warn(f"No UI found at {web_root_path}/dist, skipping UI mount")
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app.mount(
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"/static", NoCacheStaticFiles(directory=Path(web_root_path, "static/")), name="static"
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) # docs favicon is in here
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def check_cudnn(logger: logging.Logger) -> None:
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"""Check for cuDNN issues that could be causing degraded performance."""
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if torch.backends.cudnn.is_available():
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try:
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# Note: At the time of writing (torch 2.2.1), torch.backends.cudnn.version() only raises an error the first
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# time it is called. Subsequent calls will return the version number without complaining about a mismatch.
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cudnn_version = torch.backends.cudnn.version()
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logger.info(f"cuDNN version: {cudnn_version}")
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except RuntimeError as e:
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logger.warning(
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"Encountered a cuDNN version issue. This may result in degraded performance. This issue is usually "
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"caused by an incompatible cuDNN version installed in your python environment, or on the host "
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f"system. Full error message:\n{e}"
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)
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def invoke_api() -> None:
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def find_port(port: int) -> int:
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"""Find a port not in use starting at given port"""
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# Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
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# https://github.com/WaylonWalker
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with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
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s.settimeout(1)
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if s.connect_ex(("localhost", port)) == 0:
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return find_port(port=port + 1)
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else:
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return port
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if app_config.dev_reload:
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try:
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import jurigged
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except ImportError as e:
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logger.error(
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'Can\'t start `--dev_reload` because jurigged is not found; `pip install -e ".[dev]"` to include development dependencies.',
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exc_info=e,
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)
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else:
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jurigged.watch(logger=InvokeAILogger.get_logger(name="jurigged").info)
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port = find_port(app_config.port)
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if port != app_config.port:
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logger.warn(f"Port {app_config.port} in use, using port {port}")
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check_cudnn(logger)
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config = uvicorn.Config(
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app=app,
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host=app_config.host,
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port=port,
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loop="asyncio",
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log_level=app_config.log_level,
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ssl_certfile=app_config.ssl_certfile,
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ssl_keyfile=app_config.ssl_keyfile,
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)
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server = uvicorn.Server(config)
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# replace uvicorn's loggers with InvokeAI's for consistent appearance
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for logname in ["uvicorn.access", "uvicorn"]:
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log = InvokeAILogger.get_logger(logname)
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log.handlers.clear()
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for ch in logger.handlers:
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log.addHandler(ch)
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loop.run_until_complete(server.serve())
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if __name__ == "__main__":
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invoke_api()
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