distributed_executor_backend=distributed_executor_backend, ) # Add the requests to the engine. @@ -229,8 +231,9 @@ def main(args: argparse.Namespace): args.max_model_len, args.enforce_eager, args.kv_cache_dtype,
Fix `ValueError: Unrecognized distributed executor backend tp. Supported values are 'ray', 'mp' 'uni', 'external_launcher' or custom ExecutorBase subclass.` ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? Test on my local node Signed-off-by: ...
为了方便代码查看,我们将_DistributedRendezvousOpExecutor.run()的代码做简化,主要是删除掉与心跳无关的action处理分支,一并附上_keep_alive()函数的实现: class DynamicRendezvousHandler(RendezvousHandler): def _keep_alive(self) -> None: op = _RendezvousKeepAliveOp() deadline = self._get_deadline(se...
INFO:TorchDistributor:Started distributed training with 16 executor proceses /databricks/python/lib/python3.10/site-packages/pkg_resources/__init__.py:123: PkgResourcesDeprecationWarning: 1.1build1 is an invalid version and will not be supported in a future release warnings.warn( /data...
OnOutOfMemoryError='kill %p' org.apache.spark.executor.CoarseGrainedExecutorBackend --driver-url spark://CoarseGrainedScheduler@10.0.0.13:43942 --executor-id 4 --hostname 10.0.0.14 --cores 3 --app-id application_1485916338528_0008 --user-class-path file:/mnt/resource/hadoop/yarn/...
下面我们再看下Rendezvous的OP是如何执行的。上文提到OP是通过_DistributedRendezvousOpExecutor.run()接口统一来完成的。 主流程包裹在while循环中,直到OP的action为finish方可退出循环; 首先,会调用_BackendRendezvousStateHolder.sync()接口在所有node间进行_RendezvousState的同步; ...
the backend.Log Parameters The default format of each log line is as follows: [Date + Time + Time zone] + [Session ID/Database name/Thread ID/Process name/Transaction ID/Command ID] + [Log level] + [Method + Line] + Log description. ...
pg_stat_get_backend_client_addr(integer) Description: Specifies the IP address of the client connected to the given server process. If the connection is over a Unix domain socket, or if the current user is neither a system administrator nor the same user as that of the session being queried...
optimizer model building, implementation of new executor operators, and distributed GB-tree building. The details are as follows: ● Parsing layer: For IUD and SELECT, hasGSI is set in the semantic parsing phase to mark whether the current query block contains GSIs. That is, the hasGSI member...
1$python workload.py232023-05-02 15:10:01,105 INFO streaming_executor.py:91 -- Executing DAG4InputDataBuffer[Input] -> TaskPoolMapOperator[MapBatches(decode_frames)] ->5ActorPoolMapOperator[MapBatches(FrameAnnotator)] ->6ActorPoolMapOperator[MapBatches(FrameClassifier)] ->7TaskPoolMapOperator...