""" GPU Usage Logger ==================== Log GPU memory and GPU usage during training """ from pytorch_lightning.callbacks.base import Callback import subprocess import os import time class GpuUsageLogger(Callback): r""" Automatically logs GPU memory and GPU usage during training stage. Args: memory_utilisation: Set to ``True`` to log used, free and percentage of memory utilisation at starts and ends of each step. Default: ``True``. From nvidia-smi --help-query-gpu memory.used = ```Total memory allocated by active contexts.``` memory.free = ```Total free memory.``` gpu_utilisation: Set to ``True`` to log percentage of GPU utilisation. at starts and ends of each step. Default: ``True``. intra_step_time: Set to ``True`` to log the time of each step. Default: ``False`` inter_step_time: Set to ``True`` to log the time between the end of one step and the start of the next. Default: ``False`` fan_speed: Set to ``True`` to log percentage of fan speed. Default: ``False``. temperature: Set to ``True`` to log the memory and gpu temperature in degrees C. Default: ``False`` Example:: >>> from pytorch_lightning import Trainer >>> from pytorch_lightning.callbacks import GpuUsageLogger >>> gpu_usage = GpuUsageLogger() >>> trainer = Trainer(callbacks=[gpu_usage]) Gpu usage is mainly based on nvidia-smi --query-gpu command. The description of the queries used here as appears in in ``nvidia-smi --help-query-gpu``: "fan.speed" ```The fan speed value is the percent of maximum speed that the device's fan is currently intended to run at. It ranges from 0 to 100 %. Note: The reported speed is the intended fan speed. If the fan is physically blocked and unable to spin, this output will not match the actual fan speed. Many parts do not report fan speeds because they rely on cooling via fans in the surrounding enclosure.``` "memory.used" ```Total memory allocated by active contexts.``` "memory.free" ```Total free memory.``` "utilization.gpu" ```Percent of time over the past sample period during which one or more kernels was executing on the GPU. The sample period may be between 1 second and 1/6 second depending on the product.``` "utilization.memory" ```Percent of time over the past sample period during which global (device) memory was being read or written. The sample period may be between 1 second and 1/6 second depending on the product.``` "temperature.gpu" ```Core GPU temperature. in degrees C.``` "temperature.memory" ```HBM memory temperature. in degrees C.``` """ def __init__(self, memory_utilisation: bool = True, gpu_utilisation: bool = True, intra_step_time: bool = False, inter_step_time: bool = False, fan_speed: bool = False, temperature: bool = False): super(GpuUsageLogger).__init__() self.memory_utilisation = memory_utilisation self.gpu_utilisation = gpu_utilisation self.intra_step_time = intra_step_time self.inter_step_time = inter_step_time self.fan_speed = fan_speed self.temperature = temperature self.snap_intra_step_time = None self.snap_inter_step_time = None def on_batch_start(self, trainer, pl_module): if self.gpu_utilisation: self._log_gpu(trainer) if self.memory_utilisation: self._log_memory(trainer) if self.inter_step_time: # First log at beginning of second step if self.snap_inter_step_time: trainer.logger.log_metrics({'Batch_Time/inter_step (ms)': (time.time() - self.snap_inter_step_time) * 1000}, step=trainer.global_step) if self.intra_step_time: self.snap_intra_step_time = time.time() def on_batch_end(self, trainer, pl_module): if self.gpu_utilisation: self._log_gpu(trainer) if self.memory_utilisation: self._log_memory(trainer) if self.fan_speed: trainer.logger.log_metrics(self._get_gpu_stat("fan.speed", "%"), step=trainer.global_step) if self.temperature: trainer.logger.log_metrics(self._get_gpu_stat("temperature.gpu", "degrees C"), step=trainer.global_step) trainer.logger.log_metrics(self._get_gpu_stat("temperature.memory", "degrees C"), step=trainer.global_step) if self.inter_step_time: self.snap_inter_step_time = time.time() if self.intra_step_time: if self.snap_intra_step_time: trainer.logger.log_metrics({'Batch_Time/intra_step (ms)': (time.time() - self.snap_intra_step_time) * 1000}, step=trainer.global_step) def on_train_epoch_start(self, trainer, pl_module): self.snap_intra_step_time = None self.snap_inter_step_time = None @staticmethod def _get_gpu_stat(pitem: str, unit: str): result = subprocess.run(["/bin/nvidia-smi", f"--query-gpu={pitem}", "--format=csv,nounits,noheader"], encoding="utf-8", stdout=subprocess.PIPE, stderr=subprocess.PIPE, # for backward compatibility with python version 3.6 check=True) try: gpu_usage = [float(x) for x in result.stdout.strip().split(os.linesep)] except ValueError: gpu_usage = [0] return {f"GPU_{pitem}/gpu_id_{index} ({unit})": usage for index, usage in enumerate(gpu_usage)} def _log_gpu(self, trainer): trainer.logger.log_metrics(self._get_gpu_stat("utilization.gpu", "%"), step=trainer.global_step) def _log_memory(self, trainer): trainer.logger.log_metrics(self._get_gpu_stat("memory.used", "MB"), step=trainer.global_step) trainer.logger.log_metrics(self._get_gpu_stat("memory.free", "MB"), step=trainer.global_step) trainer.logger.log_metrics(self._get_gpu_stat("utilization.memory", "%"), step=trainer.global_step)