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hanfeisun avatar hanfeisun commented on September 24, 2024

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 avatar julien-c commented on September 24, 2024

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hanfeisun avatar hanfeisun commented on September 24, 2024

@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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pender avatar pender commented on September 24, 2024

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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