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Meshcheryakov Ilya 2024-05-30 00:30:30 +03:00
parent 64daef23ba
commit b8f5d81636
3 changed files with 28 additions and 143 deletions
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80
clearml_serving/serving/main.py
View File

@ -99,84 +99,14 @@ router = APIRouter(
)
# cover all routing options for model version `/{model_id}`, `/{model_id}/123`, `/{model_id}?version=123`
# @router.post("/{model_id}/{version}")
# @router.post("/{model_id}/")
# @router.post("/{model_id}")
# async def serve_model(model_id: str, version: Optional[str] = None, request: Union[bytes, Dict[Any, Any]] = None):
# try:
# return_value = await processor.process_request(
# base_url=model_id,
# version=version,
# request_body=request
# )
# except EndpointNotFoundException as ex:
# raise HTTPException(status_code=404, detail="Error processing request, endpoint was not found: {}".format(ex))
# except (EndpointModelLoadException, EndpointBackendEngineException) as ex:
# session_logger.report_text("[{}] Exception [{}] {} while processing request: {}\n{}".format(
# instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
# raise HTTPException(status_code=422, detail="Error [{}] processing request: {}".format(type(ex), ex))
# except ServingInitializationException as ex:
# session_logger.report_text("[{}] Exception [{}] {} while loading serving inference: {}\n{}".format(
# instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
# raise HTTPException(status_code=500, detail="Error [{}] processing request: {}".format(type(ex), ex))
# except ValueError as ex:
# session_logger.report_text("[{}] Exception [{}] {} while processing request: {}\n{}".format(
# instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
# if "CUDA out of memory. " in str(ex) or "NVML_SUCCESS == r INTERNAL ASSERT FAILED" in str(ex):
# raise CUDAException(exception=ex)
# else:
# raise HTTPException(status_code=422, detail="Error [{}] processing request: {}".format(type(ex), ex))
# except Exception as ex:
# session_logger.report_text("[{}] Exception [{}] {} while processing request: {}\n{}".format(
# instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
# raise HTTPException(status_code=500, detail="Error [{}] processing request: {}".format(type(ex), ex))
# return return_value
@router.post("/{model_id}/v1/chat/completions")
@router.post("/{model_id}/v1/chat/completions/")
async def serve_model(model_id: str, request: Union[bytes, Dict[Any, Any]] = None):
@router.post("/v1/{endpoint_type}")
@router.post("/v1/{endpoint_type}/")
async def llm_serve_model(endpoint_type: str, request: Union[bytes, Dict[Any, Any]] = None):
try:
return_value = await processor.process_request(
base_url=model_id,
version=None,
base_url=request["model"],
request_body=request,
url_type="chat_completion"
)
except EndpointNotFoundException as ex:
raise HTTPException(status_code=404, detail="Error processing request, endpoint was not found: {}".format(ex))
except (EndpointModelLoadException, EndpointBackendEngineException) as ex:
session_logger.report_text("[{}] Exception [{}] {} while processing request: {}\n{}".format(
instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
raise HTTPException(status_code=422, detail="Error [{}] processing request: {}".format(type(ex), ex))
except ServingInitializationException as ex:
session_logger.report_text("[{}] Exception [{}] {} while loading serving inference: {}\n{}".format(
instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
raise HTTPException(status_code=500, detail="Error [{}] processing request: {}".format(type(ex), ex))
except ValueError as ex:
session_logger.report_text("[{}] Exception [{}] {} while processing request: {}\n{}".format(
instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
if "CUDA out of memory. " in str(ex) or "NVML_SUCCESS == r INTERNAL ASSERT FAILED" in str(ex):
raise CUDAException(exception=ex)
else:
raise HTTPException(status_code=422, detail="Error [{}] processing request: {}".format(type(ex), ex))
except Exception as ex:
session_logger.report_text("[{}] Exception [{}] {} while processing request: {}\n{}".format(
instance_id, type(ex), ex, request, "".join(traceback.format_exc())))
raise HTTPException(status_code=500, detail="Error [{}] processing request: {}".format(type(ex), ex))
return return_value
@router.post("/{model_id}/v1/completions")
@router.post("/{model_id}/v1/completions/")
async def serve_model(model_id: str, request: Union[bytes, Dict[Any, Any]] = None):
try:
return_value = await processor.process_request(
base_url=model_id,
version=None,
request_body=request,
url_type="completion"
url_type=endpoint_type
)
except EndpointNotFoundException as ex:
raise HTTPException(status_code=404, detail="Error processing request, endpoint was not found: {}".format(ex))

17
clearml_serving/serving/model_request_processor.py
View File

@ -155,7 +155,7 @@ class ModelRequestProcessor(object):
self._serving_base_url = None
self._metric_log_freq = None
async def process_request(self, base_url: str, version: str, request_body: dict, url_type: str) -> dict:
async def process_request(self, base_url: str, request_body: dict, url_type: str) -> dict:
"""
Process request coming in,
Raise Value error if url does not match existing endpoints
@ -167,11 +167,11 @@ class ModelRequestProcessor(object):
while self._update_lock_flag:
await asyncio.sleep(0.5+random())
# retry to process
return await self.process_request(base_url=base_url, version=version, request_body=request_body)
return await self.process_request(base_url=base_url, request_body=request_body, url_type=url_type)
try:
# normalize url and version
url = self._normalize_endpoint_url(base_url, version)
url = self._normalize_endpoint_url(base_url)
# check canary
canary_url = self._process_canary(base_url=url)
@ -1211,16 +1211,16 @@ class ModelRequestProcessor(object):
if processor.is_preprocess_async \
else processor.preprocess(body, state, stats_collect_fn)
# noinspection PyUnresolvedReferences
if url_type == "completion":
if url_type == "completions":
processed = await processor.completion(preprocessed, state, stats_collect_fn) \
if processor.is_process_async \
else processor.completion(preprocessed, state, stats_collect_fn)
elif url_type == "chat_completion":
elif url_type == "chat/completions":
processed = await processor.chat_completion(preprocessed, state, stats_collect_fn) \
if processor.is_process_async \
else processor.chat_completion(preprocessed, state, stats_collect_fn)
else:
raise ValueError(f"wrong url_type: expected 'completion' and 'chat_completion', got {url_type}")
raise ValueError(f"wrong url_type: expected 'completions' and 'chat/completions', got {url_type}")
# noinspection PyUnresolvedReferences
return_value = await processor.postprocess(processed, state, stats_collect_fn) \
if processor.is_postprocess_async \
@ -1341,8 +1341,9 @@ class ModelRequestProcessor(object):
return task
@classmethod
def _normalize_endpoint_url(cls, endpoint: str, version: Optional[str] = None) -> str:
return "{}/{}".format(endpoint.rstrip("/"), version or "").rstrip("/")
def _normalize_endpoint_url(cls, endpoint: str) -> str:
# return "{}/{}".format(endpoint.rstrip("/"), version or "").rstrip("/")
return endpoint
@classmethod
def _validate_model(cls, endpoint: Union[ModelEndpoint, ModelMonitoring]) -> bool:

74
clearml_serving/serving/preprocess_service.py
View File

@ -19,7 +19,7 @@ class BasePreprocessRequest(object):
__preprocessing_lookup = {}
__preprocessing_modules = set()
_grpc_env_conf_prefix = "CLEARML_GRPC_"
_default_serving_base_url = "http://127.0.0.1:8080/serve/"
_default_serving_base_url = "http://127.0.0.1:8080/clearml/"
_server_config = {} # externally configured by the serving inference service
_timeout = None # timeout in seconds for the entire request, set in __init__
is_preprocess_async = False
@ -292,7 +292,7 @@ class TritonPreprocessRequest(BasePreprocessRequest):
self._grpc_stub = {}
async def process(
async def chat_completion(
self,
data: Any,
state: dict,
@ -428,74 +428,28 @@ class TritonPreprocessRequest(BasePreprocessRequest):
return output_results[0] if index == 1 else output_results
@BasePreprocessRequest.register_engine("sklearn", modules=["joblib", "sklearn"])
class SKLearnPreprocessRequest(BasePreprocessRequest):
def __init__(self, model_endpoint: ModelEndpoint, task: Task = None):
super(SKLearnPreprocessRequest, self).__init__(
model_endpoint=model_endpoint, task=task)
if self._model is None:
# get model
import joblib # noqa
self._model = joblib.load(filename=self._get_local_model_file())
def process(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
"""
The actual processing function.
We run the model in this context
"""
return self._model.predict(data)
@BasePreprocessRequest.register_engine("xgboost", modules=["xgboost"])
class XGBoostPreprocessRequest(BasePreprocessRequest):
def __init__(self, model_endpoint: ModelEndpoint, task: Task = None):
super(XGBoostPreprocessRequest, self).__init__(
model_endpoint=model_endpoint, task=task)
if self._model is None:
# get model
import xgboost # noqa
self._model = xgboost.Booster()
self._model.load_model(self._get_local_model_file())
def process(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
"""
The actual processing function.
We run the model in this context
"""
return self._model.predict(data)
@BasePreprocessRequest.register_engine("lightgbm", modules=["lightgbm"])
class LightGBMPreprocessRequest(BasePreprocessRequest):
def __init__(self, model_endpoint: ModelEndpoint, task: Task = None):
super(LightGBMPreprocessRequest, self).__init__(
model_endpoint=model_endpoint, task=task)
if self._model is None:
# get model
import lightgbm # noqa
self._model = lightgbm.Booster(model_file=self._get_local_model_file())
def process(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
"""
The actual processing function.
We run the model in this context
"""
return self._model.predict(data)
@BasePreprocessRequest.register_engine("custom")
class CustomPreprocessRequest(BasePreprocessRequest):
def __init__(self, model_endpoint: ModelEndpoint, task: Task = None):
super(CustomPreprocessRequest, self).__init__(
model_endpoint=model_endpoint, task=task)
def process(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
def completion(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
"""
The actual processing function.
We run the process in this context
"""
if self._preprocess is not None and hasattr(self._preprocess, 'process'):
return self._preprocess.process(data, state, collect_custom_statistics_fn)
if self._preprocess is not None and hasattr(self._preprocess, 'completion'):
return self._preprocess.completion(data, state, collect_custom_statistics_fn)
return None
def chat_completion(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
"""
The actual processing function.
We run the process in this context
"""
if self._preprocess is not None and hasattr(self._preprocess, 'chat_completion'):
return self._preprocess.chat_completion(data, state, collect_custom_statistics_fn)
return None