code: def main( gpu:Param("GPU to run on", str)=None ):
      
      gpu = setup_distrib(gpu)
      n_gpus = num_distrib()
      path = url2path(URLs.CIFAR)
      ds_tfms = ([*rand_pad(4, 32), flip_lr(p=0.5)], [])
      workers = min(16, num_cpus()//n_gpus)
      data = ImageDataBunch.from_folder(path, valid='test', ds_tfms=ds_tfms, bs=512//n_gpus,
                                        num_workers=workers).normalize(cifar_stats)
      learn = Learner(data, wrn_22(), metrics=accuracy)
      if gpu is None: learn.model = nn.DataParallel(learn.model)
      else: learn.to_distributed(gpu)
      learn.to_fp16()
      learn.fit_one_cycle(35, 3e-3, wd=0.4)
docs: Distrubuted training of CIFAR-10.
      Fastest speed is if you run as follows:
          python -m fastai.launch train_cifar.py