How to free up all memory pytorch is taken from gpu memory

Question

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?

HzCheng · Accepted Answer · 2019-05-06 04:32:22Z

15

Try delete the object with del and then apply torch.cuda.empty_cache(). The reusable memory will be freed after this operation.

answered May 6, 2019 at 4:32

HzCheng

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1 Comment

Rocketq Over a year ago

I suggested that step as a well. But you right, this is the main step

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