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Error: OOM when allocating tensor with shape

i am facing issue with my inception model during the performance testing with Apache JMeter.

Error: OOM when allocating tensor with shape[800,1280,3] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc [[Node: Cast = CastDstT=DT_FLOAT, SrcT=DT_UINT8, _device="/job:localhost/replica:0/task:0/device:GPU:0"]] Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

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Mohd Daoud Avatar asked Jun 08 '18 12:06

Mohd Daoud


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What is oom in Tensorflow?

OOM (Out Of Memory) errors can occur when building and training a neural network model on the GPU. The size of the model is limited by the available memory on the GPU. The following may occur when a model has exhausted the memory : Resource Exhausted Error : an error message that indicates Out Of Memory (OOM)


1 Answers

OOM stands for Out Of Memory. That means that your GPU has run out of space, presumably because you've allocated other tensors which are too large. You can fix this by making your model smaller or reducing your batch size. By the looks of it, you're feeding in a large image (800x1280) you may want to consider downsampling.

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Cory Nezin Avatar answered Oct 17 '22 16:10

Cory Nezin