def async_show_image_mat(image_mat, text=None, title=None, cell_size: tuple = None, image_name=None):
"""
:return: an async task object
"""
from async_ import AsyncLoop, AsyncManager
ui_loop = AsyncManager.get_loop(AsyncLoop.UIThread)
coro = coro_show_image_mat(image_mat, text=text, title=title,
cell_size=cell_size, block=False, image_name=image_name)
task = AsyncManager.create_task(coro, loop=ui_loop)
return AsyncManager.run_task(task, loop=ui_loop) # possibly only one task in this batch
def async_preload_gpu_devices():
"""
Preload in another loop/thread, hopefully call this during waiting for user inputs or other waiting period.
"""
# IMPROVE: needn't to run in an aysncio loop (host in a new thread), to run in a new thread is enough.
from async_ import AsyncLoop, AsyncManager
async def coro_simple_run(): preload_gpu_devices()
loop = AsyncManager.get_loop(AsyncLoop.DataProcess)
DEBUG(f"[tensorflow] preload gpu devices in another thread...")
task = AsyncManager.run_task(coro_simple_run(), loop=loop)
return task
def async_run(self, **params):
"""
Generally we need to launch web app in another loop/thread, to not block ML operations.
"""
webapp = self
# IMPROVE: web app need not to run in an aysncio loop (host in a new thread), to run in a new thread is enough.
from async_ import AsyncLoop, AsyncManager
async def coro_webapp_run():
webapp.run(**params)
webapp_loop = AsyncManager.get_loop(AsyncLoop.WebApp)
task = AsyncManager.run_task(coro_webapp_run(), loop=webapp_loop)
DEBUG(
f"[webapp_loop] listening to port {params.get('port', '<unknown>')} ..."
)
return task