Add clearml_serving_inference restart on CUDA OOM (#75)

* initial commit

* add OOM handler for MIG profiles

---------

Co-authored-by: Meshcheryakov Ilya <i.meshcheryakov@mts.ai>

clearml_serving/serving/entrypoint.sh

@@ -2,6 +2,7 @@
 
 # print configuration
 echo CLEARML_SERVING_TASK_ID="$CLEARML_SERVING_TASK_ID"
+echo CLEARML_INFERENCE_TASK_ID="$CLEARML_INFERENCE_TASK_ID"
 echo CLEARML_SERVING_PORT="$CLEARML_SERVING_PORT"
 echo CLEARML_USE_GUNICORN="$CLEARML_USE_GUNICORN"
 echo CLEARML_EXTRA_PYTHON_PACKAGES="$CLEARML_EXTRA_PYTHON_PACKAGES"

clearml_serving/serving/init.py

@@ -6,6 +6,7 @@ from clearml_serving.serving.preprocess_service import BasePreprocessRequest
 
 def setup_task(force_threaded_logging=None):
     serving_service_task_id = os.environ.get("CLEARML_SERVING_TASK_ID", None)
+    inference_service_task_id = os.environ.get("CLEARML_INFERENCE_TASK_ID", False) # according Task.init() docs
 
     # always use background thread, it requires less memory
     if force_threaded_logging or os.environ.get("CLEARML_BKG_THREAD_REPORT") in ("1", "Y", "y", "true"):
@@ -24,6 +25,7 @@ def setup_task(force_threaded_logging=None):
         project_name=serving_task.get_project_name(),
         task_name="{} - serve instance".format(serving_task.name),
         task_type="inference",  # noqa
+        continue_last_task=inference_service_task_id,
     )
     instance_task.set_system_tags(["service"])
     # make sure we start logging thread/process

clearml_serving/serving/main.py

@@ -1,9 +1,13 @@
 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
 
@@ -48,6 +52,9 @@ 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")
@@ -70,6 +77,20 @@ 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"],
@@ -102,6 +123,9 @@ 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) 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(

clearml_serving/serving/model_request_processor.py

@@ -1,5 +1,7 @@
 import json
 import os
+import gc
+import torch
 from collections import deque
 from pathlib import Path
 from random import random
@@ -915,7 +917,12 @@ class ModelRequestProcessor(object):
                     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()
+                            torch.cuda.empty_cache()
                 cleanup = False
                 model_monitor_update = False
             except Exception as ex: