Hello all,
I was curious – for hyperparameter search which sometimes leads to issues like CUDA out of memory due to batch_size and/or image size, related parameters is this code structure an okay practice? If there is a better way of handling this I would love to hear comments!
for run_count, hyperparams in enumerate(HYPERPARAMS, start=1):

    # define model, dataloaders, etc
    try:
        #Call Training Utils     

    except RuntimeError as e:
       # Handle Possible Errors
        with open("run_parameters.txt", "a+") as text_file:
            text_file.write("*** Runtime Error: {} \n\n".format(e))            
        
    finally:
        #Rest For Next Run
        del model
        del optimizer_ft
        del exp_lr_scheduler
        del dataloaders
        gc.collect()
        torch.cuda.empty_cache()