what does `step` mean in `max_steps` ?
I notice that step in PyTorch Lighting can mean batch or optimizer.step().  So what does step mean in max_steps ? @rohitgr7
The following code maybe helpful:

  
    
      pytorch-lightning/pytorch_lightning/loops/epoch/training_epoch_loop.py
    
    
        Lines 258 to 260
      in
      5da065e
    
  
  
    

        
          
           if not self._should_accumulate(): 
        

        
          
               # progress global step according to grads progress 
        

        
          
               self.global_step += 1 
        
    
  



  
    
      pytorch-lightning/pytorch_lightning/loops/epoch/training_epoch_loop.py
    
    
        Lines 323 to 331
      in
      5da065e
    
  
  
    

        
          
               def _should_accumulate(self) -> bool: 
        

        
          
                   """Checks if the optimizer step should be performed or gradients should be accumulated for the current 
        

        
          
                   step.""" 
        

        
          
                   accumulation_done = self._accumulated_batches_reached() 
        

        
          
                   # Lightning steps on the final batch 
        

        
          
                   is_final_batch = self._num_ready_batches_reached() 
        

        
          
                   # but the strategy might not 
        

        
          
                   strategy_accumulates_on_final_batch = self.trainer.strategy.handles_gradient_accumulation or not is_final_batch 
        

        
          
                   return not accumulation_done and strategy_accumulates_on_final_batch 
        
    
  



  
    
      pytorch-lightning/pytorch_lightning/loops/epoch/training_epoch_loop.py
    
    
        Lines 90 to 93
      in
      5da065e
    
  
  
    

        
          
           @property 
        

        
          
           def _is_training_done(self) -> bool: 
        

        
          
               max_steps_reached = _is_max_limit_reached(self.global_step, self.max_steps) 
        

        
          
               return max_steps_reached or self._num_ready_batches_reached()
it means global_step. So if you have accumulation factors = 2 and max_steps = 10, then the total training batches that will be covered here will be 20 instead of 10. global_step indicate total optimization steps.