Merge 61be8733c8 into 724c99c605

clearml_serving/preprocess/preprocess_template.py

@@ -41,6 +41,17 @@ class Preprocess(object):
         """
         pass
 
+    def unload(self) -> None:
+        """
+        OPTIONAL: provide unloading method for the model
+        For example:
+        ```py
+        import torch
+        torch.cuda.empty_cache()
+        ```
+        """
+        pass
+
     def preprocess(
             self,
             body: Union[bytes, dict],

clearml_serving/serving/entrypoint.sh

@@ -9,6 +9,7 @@ echo CLEARML_EXTRA_PYTHON_PACKAGES="$CLEARML_EXTRA_PYTHON_PACKAGES"
 echo CLEARML_SERVING_NUM_PROCESS="$CLEARML_SERVING_NUM_PROCESS"
 echo CLEARML_SERVING_POLL_FREQ="$CLEARML_SERVING_POLL_FREQ"
 echo CLEARML_DEFAULT_KAFKA_SERVE_URL="$CLEARML_DEFAULT_KAFKA_SERVE_URL"
+echo CLEARML_SERVING_RESTART_ON_FAILURE="$CLEARML_SERVING_RESTART_ON_FAILURE"
 
 SERVING_PORT="${CLEARML_SERVING_PORT:-8080}"
 GUNICORN_NUM_PROCESS="${CLEARML_SERVING_NUM_PROCESS:-4}"
@@ -40,29 +41,36 @@ then
       python3 -m pip install $CLEARML_EXTRA_PYTHON_PACKAGES
 fi
 
-if [ -z "$CLEARML_USE_GUNICORN" ]
+while : ; do
-then
+  if [ -z "$CLEARML_USE_GUNICORN" ]
-  if [ -z "$CLEARML_SERVING_NUM_PROCESS" ]
   then
-    echo "Starting Uvicorn server - single worker"
+    if [ -z "$CLEARML_SERVING_NUM_PROCESS" ]
-    PYTHONPATH=$(pwd) python3 -m uvicorn \
+    then
-        clearml_serving.serving.main:app --log-level $UVICORN_LOG_LEVEL --host 0.0.0.0 --port $SERVING_PORT --loop $UVICORN_SERVE_LOOP \
+      echo "Starting Uvicorn server - single worker"
-        $UVICORN_EXTRA_ARGS
+      PYTHONPATH=$(pwd) python3 -m uvicorn \
+          clearml_serving.serving.main:app --log-level $UVICORN_LOG_LEVEL --host 0.0.0.0 --port $SERVING_PORT --loop $UVICORN_SERVE_LOOP \
+          $UVICORN_EXTRA_ARGS
+    else
+      echo "Starting Uvicorn server - multi worker"
+      PYTHONPATH=$(pwd) python3 clearml_serving/serving/uvicorn_mp_entrypoint.py \
+          clearml_serving.serving.main:app --log-level $UVICORN_LOG_LEVEL --host 0.0.0.0 --port $SERVING_PORT --loop $UVICORN_SERVE_LOOP \
+          --workers $CLEARML_SERVING_NUM_PROCESS $UVICORN_EXTRA_ARGS
+    fi
   else
-    echo "Starting Uvicorn server - multi worker"
+    echo "Starting Gunicorn server"
-    PYTHONPATH=$(pwd) python3 clearml_serving/serving/uvicorn_mp_entrypoint.py \
+    # start service
-        clearml_serving.serving.main:app --log-level $UVICORN_LOG_LEVEL --host 0.0.0.0 --port $SERVING_PORT --loop $UVICORN_SERVE_LOOP \
+    PYTHONPATH=$(pwd) python3 -m gunicorn \
-        --workers $CLEARML_SERVING_NUM_PROCESS $UVICORN_EXTRA_ARGS
+        --preload clearml_serving.serving.main:app \
+        --workers $GUNICORN_NUM_PROCESS \
+        --worker-class uvicorn.workers.UvicornWorker \
+        --max-requests $GUNICORN_MAX_REQUESTS \
+        --timeout $GUNICORN_SERVING_TIMEOUT \
+        --bind 0.0.0.0:$SERVING_PORT \
+        $GUNICORN_EXTRA_ARGS
   fi
-else
+
-  echo "Starting Gunicorn server"
+  if [ -z "$CLEARML_SERVING_RESTART_ON_FAILURE" ]
-  # start service
+  then
-  PYTHONPATH=$(pwd) python3 -m gunicorn \
+    break
-      --preload clearml_serving.serving.main:app \
+  fi
-      --workers $GUNICORN_NUM_PROCESS \
+done
-      --worker-class uvicorn.workers.UvicornWorker \
-      --max-requests $GUNICORN_MAX_REQUESTS \
-      --timeout $GUNICORN_SERVING_TIMEOUT \
-      --bind 0.0.0.0:$SERVING_PORT \
-      $GUNICORN_EXTRA_ARGS
-fi

clearml_serving/serving/main.py

@@ -1,13 +1,9 @@
 import os
 import traceback
 import gzip
-import asyncio
 
 from fastapi import FastAPI, Request, Response, APIRouter, HTTPException
 from fastapi.routing import APIRoute
-from fastapi.responses import PlainTextResponse
-
-from starlette.background import BackgroundTask
 
 from typing import Optional, Dict, Any, Callable, Union
 
@@ -52,9 +48,6 @@ try:
 except (ValueError, TypeError):
     pass
 
-class CUDAException(Exception):
-    def __init__(self, exception: str):
-        self.exception = exception
 
 # start FastAPI app
 app = FastAPI(title="ClearML Serving Service", version=__version__, description="ClearML Service Service router")
@@ -77,20 +70,6 @@ async def startup_event():
         processor.launch(poll_frequency_sec=model_sync_frequency_secs*60)
 
 
-@app.on_event('shutdown')
-def shutdown_event():
-    print('RESTARTING INFERENCE SERVICE!')
-    
-async def exit_app():
-    loop = asyncio.get_running_loop()
-    loop.stop()
-    
-@app.exception_handler(CUDAException)
-async def cuda_exception_handler(request, exc):
-    task = BackgroundTask(exit_app)
-    return PlainTextResponse("CUDA out of memory. Restarting service", status_code=500, background=task)
-
-
 router = APIRouter(
     prefix="/serve",
     tags=["models"],
@@ -124,9 +103,9 @@ async def serve_model(model_id: str, version: Optional[str] = None, request: Uni
         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)
+            # can't always recover from this - prefer to exit the program such that it can be restarted
-        else:
+            os._exit(1)
-            raise HTTPException(status_code=422, detail="Error [{}] processing request: {}".format(type(ex), ex))
+        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())))

clearml_serving/serving/model_request_processor.py

@@ -1,7 +1,6 @@
 import json
 import os
 import gc
-import torch
 from collections import deque
 from pathlib import Path
 from random import random
@@ -169,6 +168,8 @@ class ModelRequestProcessor(object):
             # retry to process
             return await self.process_request(base_url=base_url, version=version, request_body=request_body)
 
+        processor = None
+        url = None
         try:
             # normalize url and version
             url = self._normalize_endpoint_url(base_url, version)
@@ -190,6 +191,8 @@ class ModelRequestProcessor(object):
 
             return_value = await self._process_request(processor=processor, url=url, body=request_body)
         finally:
+            if url and processor is not None and processor is not self._engine_processor_lookup.get(url):
+                gc.collect()
             self._request_processing_state.dec()
 
         return return_value
@@ -907,22 +910,22 @@ class ModelRequestProcessor(object):
                 if cleanup or model_monitor_update:
                     self._update_serving_plot()
                 if cleanup:
+                    gc.collect()
                     self._engine_processor_lookup = dict()
             except Exception as ex:
                 print("Exception occurred in monitoring thread: {}".format(ex))
             sleep(poll_frequency_sec)
             try:
                 # we assume that by now all old deleted endpoints requests already returned
+                call_gc_collect = False
                 if model_monitor_update and not cleanup:
                     for k in list(self._engine_processor_lookup.keys()):
                         if k not in self._endpoints:
                             # atomic
-                            self._engine_processor_lookup[k]._model = None
-                            self._engine_processor_lookup[k]._preprocess = None
-                            del self._engine_processor_lookup[k]
                             self._engine_processor_lookup.pop(k, None)
-                            gc.collect()
+                            call_gc_collect = True
-                            torch.cuda.empty_cache()
+                if call_gc_collect:
+                    gc.collect()
                 cleanup = False
                 model_monitor_update = False
             except Exception as ex:

clearml_serving/serving/preprocess_service.py

@@ -90,7 +90,18 @@ class BasePreprocessRequest(object):
             sys.modules[spec.name] = _preprocess
             spec.loader.exec_module(_preprocess)
 
-        Preprocess = _preprocess.Preprocess  # noqa
+        class PreprocessDelWrapper(_preprocess.Preprocess):
+            def __del__(self):
+                super_ = super(PreprocessDelWrapper, self)
+                if callable(getattr(super_, "unload", None)):
+                    try:
+                        super_.unload()
+                    except Exception as ex:
+                        print("Failed unloading model: {}".format(ex))
+                if callable(getattr(super_, "__del__", None)):
+                    super_.__del__()
+
+        Preprocess = PreprocessDelWrapper # noqa
         # override `send_request` method
         Preprocess.send_request = BasePreprocessRequest._preprocess_send_request
         # create preprocess class

docker/docker-compose-triton-gpu.yml

@@ -96,6 +96,7 @@ services:
       CLEARML_DEFAULT_KAFKA_SERVE_URL: ${CLEARML_DEFAULT_KAFKA_SERVE_URL:-clearml-serving-kafka:9092}
       CLEARML_DEFAULT_TRITON_GRPC_ADDR: ${CLEARML_DEFAULT_TRITON_GRPC_ADDR:-clearml-serving-triton:8001}
       CLEARML_USE_GUNICORN: ${CLEARML_USE_GUNICORN:-}
+      CLEARML_SERVING_RESTART_ON_FAILURE: ${CLEARML_SERVING_RESTART_ON_FAILURE:-}
       CLEARML_SERVING_NUM_PROCESS: ${CLEARML_SERVING_NUM_PROCESS:-}
       CLEARML_EXTRA_PYTHON_PACKAGES: ${CLEARML_EXTRA_PYTHON_PACKAGES:-}
       AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID:-}

docker/docker-compose-triton.yml

@@ -96,6 +96,7 @@ services:
       CLEARML_DEFAULT_KAFKA_SERVE_URL: ${CLEARML_DEFAULT_KAFKA_SERVE_URL:-clearml-serving-kafka:9092}
       CLEARML_DEFAULT_TRITON_GRPC_ADDR: ${CLEARML_DEFAULT_TRITON_GRPC_ADDR:-clearml-serving-triton:8001}
       CLEARML_USE_GUNICORN: ${CLEARML_USE_GUNICORN:-}
+      CLEARML_SERVING_RESTART_ON_FAILURE: ${CLEARML_SERVING_RESTART_ON_FAILURE:-}
       CLEARML_SERVING_NUM_PROCESS: ${CLEARML_SERVING_NUM_PROCESS:-}
       CLEARML_EXTRA_PYTHON_PACKAGES: ${CLEARML_EXTRA_PYTHON_PACKAGES:-}
       AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID:-}

docker/docker-compose.yml

@@ -96,6 +96,7 @@ services:
       CLEARML_DEFAULT_KAFKA_SERVE_URL: ${CLEARML_DEFAULT_KAFKA_SERVE_URL:-clearml-serving-kafka:9092}
       CLEARML_DEFAULT_TRITON_GRPC_ADDR: ${CLEARML_DEFAULT_TRITON_GRPC_ADDR:-}
       CLEARML_USE_GUNICORN: ${CLEARML_USE_GUNICORN:-}
+      CLEARML_SERVING_RESTART_ON_FAILURE: ${CLEARML_SERVING_RESTART_ON_FAILURE:-}
       CLEARML_SERVING_NUM_PROCESS: ${CLEARML_SERVING_NUM_PROCESS:-}
       CLEARML_EXTRA_PYTHON_PACKAGES: ${CLEARML_EXTRA_PYTHON_PACKAGES:-}
       AWS_ACCESS_KEY_ID: ${AWS_ACCESS_KEY_ID:-}

examples/huggingface/readme.md

@@ -73,6 +73,8 @@ CLEARML_EXTRA_PYTHON_PACKAGES=transformers
 # Change this depending on your machine and performance needs
 CLEARML_USE_GUNICORN=1
 CLEARML_SERVING_NUM_PROCESS=8
+# Restarts if the serving process crashes
+CLEARML_SERVING_RESTART_ON_FAILURE=1
 ```
 
 Huggingface models require Triton engine support, please use `docker-compose-triton.yml` / `docker-compose-triton-gpu.yml` or if running on Kubernetes, the matching helm chart to set things up. Check the repository main readme documentation if you need help.