feat(ml): composable ml ()

* 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
This commit is contained in:
Mert 2024-06-06 23:09:47 -04:00 committed by GitHub
parent 7a46f80ddc
commit 2b1b43a7e4
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39 changed files with 982 additions and 999 deletions
machine-learning

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@ -37,7 +37,6 @@ def on_test_start(environment: Environment, **kwargs: Any) -> None:
global byte_image
assert environment.parsed_options is not None
image = Image.new("RGB", (environment.parsed_options.image_size, environment.parsed_options.image_size))
byte_image = BytesIO()
image.save(byte_image, format="jpeg")
@ -45,34 +44,25 @@ class InferenceLoadTest(HttpUser):
abstract: bool = True
host = "http://127.0.0.1:3003"
data: bytes
headers: dict[str, str] = {"Content-Type": "image/jpg"}
# re-use the image across all instances in a process
def on_start(self) -> None:
global byte_image
self.data = byte_image.getvalue()
class CLIPTextFormDataLoadTest(InferenceLoadTest):
@task
def encode_text(self) -> None:
data = [
("modelName", self.environment.parsed_options.clip_model),
("modelType", "clip"),
("options", json.dumps({"mode": "text"})),
("text", "test search query"),
]
request = {"clip": {"textual": {"modelName": self.environment.parsed_options.clip_model}}}
data = [("entries", json.dumps(request)), ("text", "test search query")]
self.client.post("/predict", data=data)
class CLIPVisionFormDataLoadTest(InferenceLoadTest):
@task
def encode_image(self) -> None:
data = [
("modelName", self.environment.parsed_options.clip_model),
("modelType", "clip"),
("options", json.dumps({"mode": "vision"})),
]
request = {"clip": {"visual": {"modelName": self.environment.parsed_options.clip_model, "options": {}}}}
data = [("entries", json.dumps(request))]
files = {"image": self.data}
self.client.post("/predict", data=data, files=files)
@ -80,11 +70,18 @@ class CLIPVisionFormDataLoadTest(InferenceLoadTest):
class RecognitionFormDataLoadTest(InferenceLoadTest):
@task
def recognize(self) -> None:
data = [
("modelName", self.environment.parsed_options.face_model),
("modelType", "facial-recognition"),
("options", json.dumps({"minScore": self.environment.parsed_options.face_min_score})),
]
request = {
"facial-recognition": {
"recognition": {
"modelName": self.environment.parsed_options.face_model,
"options": {"minScore": self.environment.parsed_options.face_min_score},
},
"detection": {
"modelName": self.environment.parsed_options.face_model,
},
}
}
data = [("entries", json.dumps(request))]
files = {"image": self.data}
self.client.post("/predict", data=data, files=files)