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I have a large network that is somewhat similar to Wavenet. Although (it seems) that my GPU has enough memory, I get an out of memory error on fitting (see logs below).

Any idea?

How can I troubleshoot these kind of CUDA driver issues?


2017-12-22 23:32:05.288986: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\core\common_runtime\gpu\gpu_device.cc:955] Found device 0 with properties: name: GeForce GTX 1080 Ti major: 6 minor: 1 memoryClockRate (GHz) 1.6575 pciBusID 0000:01:00.0 Total memory: 11.00GiB Free memory: 10.71GiB

2017-12-22 23:32:05.288986: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\core\common_runtime\gpu\gpu_device.cc:976] DMA: 0 2017-12-22 23:32:05.288986: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\core\common_runtime\gpu\gpu_device.cc:986] 0: Y 2017-12-22 23:32:05.429386: I C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\core\common_runtime\gpu\gpu_device.cc:1045] Creating TensorFlow device (/gpu:0) -> (device : 0, name: GeForce GTX 1080 Ti, pci bus id: 0000:01:00.0)

2017-12-22 23:32:06.131386: E C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\stream_executor\cuda\cuda_driver.cc:924] failed to allocate 10.17G (10922166272 bytes) fro m device: CUDA_ERROR_OUT_OF_MEMORY 2017-12-22 23:32:06.599386: E C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\stream_executor\cuda\cuda_driver.cc:924] failed to allocate 9.15G (9829949440 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY 2017-12-22 23:32:07.332586: E C:\tf_jenkins\home\workspace\rel-win\M\windows-gpu\PY\36\tensorf low\stream_executor\cuda\cuda_driver.cc:924] failed to allocate 8.24G (8846954496 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY built graph

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Did you try to start your training with a smaller data set and that worked?

How about using generators to gradually input your data? For me that worked quite well (in Keras : model.fit_generator(...))

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  • $\begingroup$ Actually when I restarted training the error went away. My question is more general on how to approach such errors (debugging). I guess we need to use the NVidia CUDA profiler. $\endgroup$
    – Eran
    Dec 26 '17 at 21:03
  • $\begingroup$ Did you have an other Model running in parallel and did not set the allow growth parameter (config = tf.ConfigProto() config.gpu_options.allow_growth=True sess = tf.Session(config=config))? Then it could be that the model before have allocated all the space. $\endgroup$ Dec 26 '17 at 21:38
  • $\begingroup$ I cannot find such API in Keras: fit_generator. Did you mean: medium.com/@fromtheast/… ? $\endgroup$
    – Eran
    Jan 24 '18 at 11:07
  • $\begingroup$ You can find it at: keras.io/models/sequential just search for fit_generator. But yes, it is the same, he also refers to that page. $\endgroup$ Feb 1 '18 at 14:29

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