                   "This option prevents Tensorflow from allocating all of the GPU VRAM at launch "
                   "but can lead to higher VRAM fragmentation and slower performance. Should only "
                   "be enabled if you are receiving errors regarding 'cuDNN fails to initialize' "
                   "when commencing training.")
  
      def load_module(self, filename, module_path, plugin_type):
          """ Load the defaults module and add defaults """
          logger.debug("Adding defaults: (filename: %s, module_path: %s, plugin_type: %s",
                       filename, module_path, plugin_type)
 
</s> remove         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."))
 </s> add  </s> remove         if get_backend() != "nvidia" or not use_mixed_precision:
 </s> add         if not use_mixed_precision: </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)
        self.add_item(
            section=section,
            title="mixed_precision",
            datatype=bool,
            default=False,
            group="network",
            info="R|[Nvidia Only], 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.")
                   "This option prevents Tensorflow from allocating all of the GPU VRAM at launch "
                   "but can lead to higher VRAM fragmentation and slower performance. Should only "
                   "be enabled if you are receiving errors regarding 'cuDNN fails to initialize' "
                   "when commencing training.")
        self.add_item(
            section=section,
            title="mixed_precision",
            datatype=bool,
            default=False,
            group="network",
            info="R|[Nvidia Only], 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.")
  
      def load_module(self, filename, module_path, plugin_type):
          """ Load the defaults module and add defaults """
          logger.debug("Adding defaults: (filename: %s, module_path: %s, plugin_type: %s",
                       filename, module_path, plugin_type)
keep keep keep add keep keep keep keep keep
 Training - Move Mixed Precision to model config 
plugins/train/_config.py