# Copyright The PyTorch Lightning team.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at## http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.importloggingfromtypingimportDictfrompytorch_lightning.profilers.profilerimportProfilerfrompytorch_lightning.utilitiesimport_TPU_AVAILABLEfrompytorch_lightning.utilities.exceptionsimportMisconfigurationExceptionif_TPU_AVAILABLE:importtorch_xla.debug.profilerasxplog=logging.getLogger(__name__)
[docs]classXLAProfiler(Profiler):STEP_FUNCTIONS={"validation_step","test_step","predict_step"}RECORD_FUNCTIONS={"training_step","backward","validation_step","test_step","predict_step",}def__init__(self,port:int=9012)->None:"""XLA Profiler will help you debug and optimize training workload performance for your models using Cloud TPU performance tools. Args: port: the port to start the profiler server on. An exception is raised if the provided port is invalid or busy. """ifnot_TPU_AVAILABLE:raiseMisconfigurationException("`XLAProfiler` is only supported on TPUs")super().__init__(dirpath=None,filename=None)self.port=portself._recording_map:Dict={}self._step_recoding_map:Dict={}self._start_trace:bool=False
To analyze traffic and optimize your experience, we serve cookies on this site. By clicking or navigating, you agree to allow our usage of cookies. Read PyTorch Lightning's Privacy Policy.