class_docstr: Hook executed at the start of training and after every training iteration is done.
  Logs Gpu nvidia-smi stats to logger streams: at the start of training and
  after 50 training iterations.
class_docstr_tok: ["Hook","executed","at","the","start","of","training","and","after","every","training","iteration","is","done",".","Logs","Gpu","nvidia-smi","stats","to","logger","streams",":","at","the","start","of","training","and","after","50","training","iterations","."]
docstr: Logs Gpu nvidia-smi stats to logger streams.
docstr_tok: ["Logs","Gpu","nvidia-smi","stats","to","logger","streams","."]
code: def on_start(self, task: "tasks.ClassyTask") -> None:
      """
      Logs Gpu nvidia-smi stats to logger streams.
      """
      if is_primary() and (task.device.type == "cuda"):
          # print the nvidia-smi stats
          log_gpu_stats()
  
imports: import atexit
  import datetime
  import json
  import logging
  import time
  from typing import List, Optional
  import torch
  from classy_vision import meters, tasks
  from classy_vision.generic.distributed_util import get_rank, is_primary
  from classy_vision.hooks.classy_hook import ClassyHook
  from fairscale.nn.data_parallel import FullyShardedDataParallel
  from iopath.common.file_io import g_pathmgr
  from vissl.models.model_helpers import model_output_has_nan
  from vissl.utils.checkpoint import CheckpointWriter, is_checkpoint_phase
  from vissl.utils.env import get_machine_local_and_dist_rank
  from vissl.utils.io import save_file
  from vissl.utils.logger import log_gpu_stats
  from vissl.utils.perf_stats import PerfStats
label_desc: Use image node_id 1 for calling the LogGpuStatsHook obj's underlying member method code with example usage: obj.on_start(task) without return types