Pytorch is taking more memory then move on to the next loop, so eventually fails out of cuda memory:
I have some kind of high level code, so model training and etc. are wrapped by pipeline_network class. My main goal is to train new model every new fold.
for train_idx, valid_idx in cv.split(meta_train[DEPTH_COLUMN].values.reshape(-1)):

        meta_train_split, meta_valid_split = meta_train.iloc[train_idx], meta_train.iloc[valid_idx]
        
        pipeline_network = unet(config=CONFIG, suffix = 'fold' + str(fold), train_mode=True)

But then I move on to 2nd fold everything fails out of gpu memory:
RuntimeError: cuda runtime error (2) : out of memory at /pytorch/torch/lib/THC/generic/THCStorage.cu:58

At the end of epoch I tried to manually delete that pipeline with no luck:
 def clean_object_from_memory(obj): #definition
    del obj
    gc.collect()
    torch.cuda.empty_cache()

clean_object_from_memory( clean_object_from_memory) # calling

Calling this didn’t help as well:
def dump_tensors(gpu_only=True):
        torch.cuda.empty_cache()
        total_size = 0
        for obj in gc.get_objects():
            try:
                if torch.is_tensor(obj):
                    if not gpu_only or obj.is_cuda:
                        del obj
                        gc.collect()
                elif hasattr(obj, "data") and torch.is_tensor(obj.data):
                    if not gpu_only or obj.is_cuda:
                        del obj
                        gc.collect()
            except Exception as e:
                pass

How can reset pytorch then I move on to the next fold?