          """ Return the vram in use """
          self.initialize()
          if self.plaid:
              # NB There is no useful way to get allocated VRAM on PlaidML.
              # OpenCL loads and unloads VRAM as required, so this returns the global memory size
              # less the maximum allowed allocation size. It's not particularly useful
              vram = [self.plaid.vram[idx] - self.plaid.max_alloc[idx]
                      for idx in range(self.device_count)]
  
          elif IS_MACOS:
              vram = [pynvx.cudaGetMemUsed(handle, ignore=True) / (1024 * 1024)
                      for handle in self.handles]
          else:
 
</s> remove             # OpenCL loads and unloads VRAM as required, so this returns the maximum allowed
            # allocation size. It's not particularly useful
            vram = self.plaid.max_alloc
 </s> add             # OpenCL loads and unloads VRAM as required, so this returns the total memory
            # It's not particularly useful
            vram = self.plaid.vram </s> remove         return plaidml.settings.device_ids
 </s> add         return [idx for idx, d_id in enumerate(self.ids) if d_id in plaidml.settings.device_ids] </s> remove         if self.device_count == 0:
 </s> add         if self.device_count == 0 or (self.is_plaidml and not supports_plaidml): </s> remove     @staticmethod
    def get_vram_free():
 </s> add     def get_vram_free(self):
            # OpenCL loads and unloads VRAM as required, so this returns 0
            # It's not particularly useful
            vram = [0 for idx in range(self.device_count)]
          """ Return the vram in use """
          self.initialize()
          if self.plaid:
              # NB There is no useful way to get allocated VRAM on PlaidML.
            # OpenCL loads and unloads VRAM as required, so this returns 0
            # It's not particularly useful
            vram = [0 for idx in range(self.device_count)]
            # OpenCL loads and unloads VRAM as required, so this returns 0
            # It's not particularly useful
            vram = [0 for idx in range(self.device_count)]
            # OpenCL loads and unloads VRAM as required, so this returns 0
            # It's not particularly useful
            vram = [0 for idx in range(self.device_count)]
            # OpenCL loads and unloads VRAM as required, so this returns 0
            # It's not particularly useful
            vram = [0 for idx in range(self.device_count)]
  
          elif IS_MACOS:
              vram = [pynvx.cudaGetMemUsed(handle, ignore=True) / (1024 * 1024)
                      for handle in self.handles]
          else:
keep keep keep keep replace replace replace replace keep keep keep keep keep
 Fix Active Devices for plaidML. Add Supports PlaidML to Extractors 
lib/gpu_stats.py