ClearML/clearml-serving
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Meshcheryakov Ilya 2024-05-29 21:18:39 +03:00
parent 6859920848
commit 64daef23ba
6 changed files with 104 additions and 20 deletions
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2
clearml_serving/serving/Dockerfile
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@ -4,7 +4,7 @@ FROM python:3.11-bullseye
ENV LC_ALL=C.UTF-8
# install base package
RUN pip3 install --no-cache-dir clearml-serving
# RUN pip3 install --no-cache-dir clearml-serving
# get latest execution code from the git repository
# RUN cd $HOME && git clone https://github.com/allegroai/clearml-serving.git

2
clearml_serving/serving/entrypoint.sh
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@ -18,7 +18,7 @@ UVICORN_SERVE_LOOP="${UVICORN_SERVE_LOOP:-uvloop}"
UVICORN_LOG_LEVEL="${UVICORN_LOG_LEVEL:-warning}"
# set default internal serve endpoint (for request pipelining)
CLEARML_DEFAULT_BASE_SERVE_URL="${CLEARML_DEFAULT_BASE_SERVE_URL:-http://127.0.0.1:$SERVING_PORT/serve}"
CLEARML_DEFAULT_BASE_SERVE_URL="${CLEARML_DEFAULT_BASE_SERVE_URL:-http://127.0.0.1:$SERVING_PORT/clearml}"
CLEARML_DEFAULT_TRITON_GRPC_ADDR="${CLEARML_DEFAULT_TRITON_GRPC_ADDR:-127.0.0.1:8001}"
# print configuration

84
clearml_serving/serving/main.py
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@ -92,7 +92,7 @@ async def cuda_exception_handler(request, exc):
router = APIRouter(
prefix="/serve",
prefix="/clearml",
tags=["models"],
responses={404: {"description": "Model Serving Endpoint Not found"}},
route_class=GzipRoute, # mark-out to remove support for GZip content encoding
@ -100,15 +100,49 @@ 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):
# @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):
try:
return_value = await processor.process_request(
base_url=model_id,
version=version,
request_body=request
version=None,
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))
@ -123,7 +157,41 @@ async def serve_model(model_id: str, version: Optional[str] = None, request: Uni
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):
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"
)
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))

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) -> dict:
async def process_request(self, base_url: str, version: str, request_body: dict, url_type: str) -> dict:
"""
Process request coming in,
Raise Value error if url does not match existing endpoints
@ -188,7 +188,7 @@ class ModelRequestProcessor(object):
processor = processor_cls(model_endpoint=ep, task=self._task)
self._engine_processor_lookup[url] = processor
return_value = await self._process_request(processor=processor, url=url, body=request_body)
return_value = await self._process_request(processor=processor, url=url, body=request_body, url_type=url_type)
finally:
self._request_processing_state.dec()
@ -1188,7 +1188,7 @@ class ModelRequestProcessor(object):
# update preprocessing classes
BasePreprocessRequest.set_server_config(self._configuration)
async def _process_request(self, processor: BasePreprocessRequest, url: str, body: dict) -> dict:
async def _process_request(self, processor: BasePreprocessRequest, url: str, body: dict, url_type: str) -> dict:
# collect statistics for this request
stats_collect_fn = None
collect_stats = False
@ -1211,9 +1211,16 @@ class ModelRequestProcessor(object):
if processor.is_preprocess_async \
else processor.preprocess(body, state, stats_collect_fn)
# noinspection PyUnresolvedReferences
processed = await processor.process(preprocessed, state, stats_collect_fn) \
if url_type == "completion":
processed = await processor.completion(preprocessed, state, stats_collect_fn) \
if processor.is_process_async \
else processor.process(preprocessed, state, stats_collect_fn)
else processor.completion(preprocessed, state, stats_collect_fn)
elif url_type == "chat_completion":
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}")
# noinspection PyUnresolvedReferences
return_value = await processor.postprocess(processed, state, stats_collect_fn) \
if processor.is_postprocess_async \

15
clearml_serving/serving/preprocess_service.py
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@ -576,13 +576,22 @@ class CustomAsyncPreprocessRequest(BasePreprocessRequest):
return await self._preprocess.postprocess(data, state, collect_custom_statistics_fn)
return data
async def process(self, data: Any, state: dict, collect_custom_statistics_fn: Callable[[dict], None] = None) -> Any:
async 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 await self._preprocess.process(data, state, collect_custom_statistics_fn)
if self._preprocess is not None and hasattr(self._preprocess, 'completion'):
return await self._preprocess.completion(data, state, collect_custom_statistics_fn)
return None
async 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 await self._preprocess.chat_completion(data, state, collect_custom_statistics_fn)
return None
@staticmethod

2
clearml_serving/statistics/entrypoint.sh
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@ -10,7 +10,7 @@ echo CLEARML_DEFAULT_KAFKA_SERVE_URL="$CLEARML_DEFAULT_KAFKA_SERVE_URL"
SERVING_PORT="${CLEARML_SERVING_PORT:-9999}"
# set default internal serve endpoint (for request pipelining)
CLEARML_DEFAULT_BASE_SERVE_URL="${CLEARML_DEFAULT_BASE_SERVE_URL:-http://127.0.0.1:$SERVING_PORT/serve}"
CLEARML_DEFAULT_BASE_SERVE_URL="${CLEARML_DEFAULT_BASE_SERVE_URL:-http://127.0.0.1:$SERVING_PORT/clearml}"
CLEARML_DEFAULT_TRITON_GRPC_ADDR="${CLEARML_DEFAULT_TRITON_GRPC_ADDR:-127.0.0.1:8001}"
# print configuration