| 6 | |
| 7 | |
| 8 | class DistributedInferenceService: |
| 9 | def __init__(self): |
| 10 | self.worker = None |
| 11 | self.is_running = False |
| 12 | self.args = None |
| 13 | |
| 14 | def start_distributed_inference(self, args) -> bool: |
| 15 | self.args = args |
| 16 | if self.is_running: |
| 17 | logger.warning("Distributed inference service is already running") |
| 18 | return True |
| 19 | |
| 20 | try: |
| 21 | self.worker = TorchrunInferenceWorker() |
| 22 | |
| 23 | if not self.worker.init(args): |
| 24 | raise RuntimeError("Worker initialization failed") |
| 25 | |
| 26 | self.is_running = True |
| 27 | logger.info(f"Rank {self.worker.rank} inference service started successfully") |
| 28 | return True |
| 29 | |
| 30 | except Exception as e: |
| 31 | logger.error(f"Error starting inference service: {str(e)}") |
| 32 | self.stop_distributed_inference() |
| 33 | return False |
| 34 | |
| 35 | def stop_distributed_inference(self): |
| 36 | if not self.is_running: |
| 37 | return |
| 38 | |
| 39 | try: |
| 40 | if self.worker: |
| 41 | self.worker.cleanup() |
| 42 | logger.info("Inference service stopped") |
| 43 | except Exception as e: |
| 44 | logger.error(f"Error stopping inference service: {str(e)}") |
| 45 | finally: |
| 46 | self.worker = None |
| 47 | self.is_running = False |
| 48 | |
| 49 | async def submit_task_async(self, task_data: dict) -> Optional[dict]: |
| 50 | if not self.is_running or not self.worker: |
| 51 | logger.error("Inference service is not started") |
| 52 | return None |
| 53 | |
| 54 | if self.worker.rank != 0: |
| 55 | return None |
| 56 | |
| 57 | try: |
| 58 | if self.worker.processing: |
| 59 | logger.info(f"Waiting for previous task to complete before processing task {task_data.get('task_id')}") |
| 60 | |
| 61 | self.worker.processing = True |
| 62 | result = await self.worker.process_request(task_data) |
| 63 | self.worker.processing = False |
| 64 | return result |
| 65 | except Exception as e: |