              default=False,
              backend="nvidia",
              group="training",
              help="Use the Tensorflow Mirrored Distrubution Strategy to train on multiple GPUs."))
          argument_list.append(dict(
              opts=("-mp", "--mixed-precision"),
              action="store_true",
              dest="mixed_precision",
              default=False,
              backend="nvidia",
              group="training",
              help="R|NVIDIA GPUs can run operations in float16 faster than in float32. Mixed "
                   "precision allows you to use a mix of float16 with float32, to get the "
                   "performance benefits from float16 and the numeric stability benefits from "
                   "float32.\nWhile mixed precision will run on most Nvidia models, it will only "
                   "speed up training on more recent GPUs. Those with compute capability 7.0 or "
                   "higher will see the greatest performance benefit from mixed precision because "
                   "they have Tensor Cores. Older GPUs offer no math performance benefit for using "
                   "mixed precision, however memory and bandwidth savings can enable some speedups. "
                   "Generally RTX GPUs and later will offer the most benefit."))
          argument_list.append(dict(
              opts=("-s", "--save-interval"),
              action=Slider,
              min_max=(10, 1000),
              rounding=10,
 
</s> remove     def __init__(self, arguments, allow_growth, is_predict):
        logger.debug("Initializing %s: (arguments: %s, allow_growth: %s, is_predict: %s)",
                     self.__class__.__name__, arguments, allow_growth, is_predict)
 </s> add     def __init__(self, arguments, mixed_precision, allow_growth, is_predict):
        logger.debug("Initializing %s: (arguments: %s, mixed_precision: %s, allow_growth: %s, "
                     "is_predict: %s)", self.__class__.__name__, arguments, mixed_precision,
                     allow_growth, is_predict) </s> remove         if get_backend() != "nvidia" or not use_mixed_precision:
 </s> add         if not use_mixed_precision: </s> remove         use_mixed_precision = not is_predict and arguments.mixed_precision
 </s> add         use_mixed_precision = not is_predict and mixed_precision and get_backend() == "nvidia"
              default=False,
              backend="nvidia",
              group="training",
              help="Use the Tensorflow Mirrored Distrubution Strategy to train on multiple GPUs."))
















          argument_list.append(dict(
              opts=("-s", "--save-interval"),
              action=Slider,
              min_max=(10, 1000),
              rounding=10,
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Training - Move Mixed Precision to model config 
lib/cli/args.py