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Source code for ignite.contrib.metrics.gpu_info

# -*- coding: utf-8 -*-
import warnings

import torch

from ignite.engine import Events
from ignite.metrics import Metric


[docs]class GpuInfo(Metric): """Provides GPU information: a) used memory percentage, b) gpu utilization percentage values as Metric on each iterations. .. Note :: In case if gpu utilization reports "N/A" on a given GPU, corresponding metric value is not set. Examples: .. code-block:: python # Default GPU measurements GpuInfo().attach(trainer, name='gpu') # metric names are 'gpu:X mem(%)', 'gpu:X util(%)' # Logging with TQDM ProgressBar(persist=True).attach(trainer, metric_names=['gpu:0 mem(%)', 'gpu:0 util(%)']) # Progress bar will looks like # Epoch [2/10]: [12/24] 50%|█████ , gpu:0 mem(%)=79, gpu:0 util(%)=59 [00:17<1:23] # Logging with Tensorboard tb_logger.attach(trainer, log_handler=OutputHandler(tag="training", metric_names='all'), event_name=Events.ITERATION_COMPLETED) """ def __init__(self): try: import pynvml except ImportError: raise RuntimeError( "This contrib module requires pynvml to be installed. " "Please install it with command: \n pip install pynvml" ) # Let's check available devices if not torch.cuda.is_available(): raise RuntimeError("This contrib module requires available GPU") from pynvml.smi import nvidia_smi # Let it fail if no libnvidia drivers or NMVL library found self.nvsmi = nvidia_smi.getInstance() super(GpuInfo, self).__init__()
[docs] def reset(self): pass
[docs] def update(self, output): pass
[docs] def compute(self): data = self.nvsmi.DeviceQuery("memory.used, memory.total, utilization.gpu") if len(data) == 0 or ("gpu" not in data): warnings.warn("No GPU information available") return [] return data["gpu"]
[docs] def completed(self, engine, name): data = self.compute() if len(data) < 1: warnings.warn("No GPU information available") return for i, data_by_rank in enumerate(data): mem_name = "{}:{} mem(%)".format(name, i) if "fb_memory_usage" not in data_by_rank: warnings.warn("No GPU memory usage information available in {}".format(data_by_rank)) continue mem_report = data_by_rank["fb_memory_usage"] if not ("used" in mem_report and "total" in mem_report): warnings.warn( "GPU memory usage information does not provide used/total " "memory consumption information in {}".format(mem_report) ) continue engine.state.metrics[mem_name] = int(mem_report["used"] * 100.0 / mem_report["total"]) for i, data_by_rank in enumerate(data): util_name = "{}:{} util(%)".format(name, i) if "utilization" not in data_by_rank: warnings.warn("No GPU utilization information available in {}".format(data_by_rank)) continue util_report = data_by_rank["utilization"] if not ("gpu_util" in util_report): warnings.warn( "GPU utilization information does not provide 'gpu_util' information in {}".format(util_report) ) continue try: engine.state.metrics[util_name] = int(util_report["gpu_util"]) except ValueError: # Do not set GPU utilization information pass
[docs] def attach(self, engine, name="gpu", event_name=Events.ITERATION_COMPLETED): engine.add_event_handler(event_name, self.completed, name)

© Copyright 2022, PyTorch-Ignite Contributors. Last updated on 11/27/2022, 9:04:08 PM.

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