Comments (4)
I run the training for scotus.bz2
data by typing in python train.py
. The program is run on AWS p2.8xlarge instance (12 GB GPU memory per GPU).
And it throws the following exception
Limit: 11330676327
InUse: 11035230976
MaxInUse: 11038110976
NumAllocs: 10139
MaxAllocSize: 71999744
W tensorflow/core/common_runtime/bfc_allocator.cc:274] ****************************************************************************************************
W tensorflow/core/common_runtime/bfc_allocator.cc:275] Ran out of memory trying to allocate 34.33MiB. See logs for memory state.
W tensorflow/core/framework/op_kernel.cc:975] Resource exhausted: OOM when allocating tensor with shape[3000,3000]
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@julien-c Thanks for the information! I used to think using p2.8xlarge can solve the issue.
When I set batch_size to 10, and rnn_size to 50, the Out Of Memory issue disappears.
It would be great if the default parameter could be set to fit a ordinary GPU's memory..
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Default parameters are set to obtain the best global results that I could, pushing my GPU to the limit. It really makes a difference with a chatbot... char-rnn
is probably less sensitive to performance because it's not interactive, while the chatbot gets a lot more responsive and topical as performance improves.
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Related Issues (20)
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