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feat(ml): composable ml (#9973)

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* modularize model classes

* various fixes

* expose port

* change response

* round coordinates

* simplify preload

* update server

* simplify interface

simplify

* update tests

* composable endpoint

* cleanup

fixes

remove unnecessary interface

support text input, cleanup

* ew camelcase

* update server

server fixes

fix typing

* ml fixes

update locustfile

fixes

* cleaner response

* better repo response

* update tests

formatting and typing

rename

* undo compose change

* linting

fix type

actually fix typing

* stricter typing

fix detection-only response

no need for defaultdict

* update spec file

update api

linting

* update e2e

* unnecessary dimension

* remove commented code

* remove duplicate code

* remove unused imports

* add batch dim
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Mert 2024-06-06 23:09:47 -04:00 • committed by GitHub
parent 7a46f80ddc
commit 2b1b43a7e4
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25
machine-learning/app/models/cache.py
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@ -5,9 +5,9 @@ from aiocache.lock import OptimisticLock
from aiocache.plugins import TimingPlugin
from app.models import from_model_type
from app.models.base import InferenceModel
from ..schemas import ModelType, has_profiling
from .base import InferenceModel
from ..schemas import ModelTask, ModelType, has_profiling
class ModelCache:
@ -31,28 +31,21 @@ class ModelCache:
if profiling:
plugins.append(TimingPlugin())
self.revalidate_enable = revalidate
self.should_revalidate = revalidate
self.cache = SimpleMemoryCache(timeout=timeout, plugins=plugins, namespace=None)
async def get(self, model_name: str, model_type: ModelType, **model_kwargs: Any) -> InferenceModel:
"""
Args:
model_name: Name of model in the model hub used for the task.
model_type: Model type or task, which determines which model zoo is used.
Returns:
model: The requested model.
"""
key = f"{model_name}{model_type.value}{model_kwargs.get('mode', '')}"
async def get(
self, model_name: str, model_type: ModelType, model_task: ModelTask, **model_kwargs: Any
) -> InferenceModel:
key = f"{model_name}{model_type}{model_task}"
async with OptimisticLock(self.cache, key) as lock:
model: InferenceModel | None = await self.cache.get(key)
if model is None:
model = from_model_type(model_type, model_name, **model_kwargs)
model = from_model_type(model_name, model_type, model_task, **model_kwargs)
await lock.cas(model, ttl=model_kwargs.get("ttl", None))
elif self.revalidate_enable:
elif self.should_revalidate:
await self.revalidate(key, model_kwargs.get("ttl", None))
return model