(Suppose there are 2G memory on one GPU, and stream A caches 1G after deal with request1. However, I found that, when using libtorch, the memory allocated by each stream will be cached by that stream, which cannot be reused by other stream.

I have tried libtorch, since it support multiple stream. Instead, I want to use multiple cuda streams to deal with several requests on one GPU concurrently. Since the model is dynamically activated, so batching requests and do inference is not a good idea. I am trying to deploy a model with dynamic connection of our company to production environment. Qihui zhouui Asks: GPU Memory allocation with multiple cuda stream Would anyone be able to help me in this regards please. It gives an error due to the different number in coordinates of the polygones (3 and 4 respectively). Sequence item with index 0 has a wrong type

Points = np.array(,, ],, ,]])ġ1 # Use fillPoly() function and give input as If you meant to do this, you must specify 'dtype=object' when creating the ndarray.

var/folders/hh/phtw2ldx7gj88krjgwdbz9680000gn/T/ipykernel_1755/1373141779.py:9: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated.

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