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Failed to allocate from device cuda error out of memory

0 GeForce GTX 105. 0 Off | N/ A | | 49% 64C P0 63W / 75W | 3863MiB / 4038MiB | 87% Default. 514867: E tensorflow/ stream_ executor/ cuda/ cuda_ driver. cc: 893] failed to allocate 134. 44Mbytes) from device: CUDA_ ERROR_ OUT_ OF_ MEMORY: 46: 15. System information OS - High Sierra 10. 13 Tensorflow - 1. 9 CUDA - 9 cuDNN - 7 Describe the problem CUDA_ ERROR_ OUT_ OF_ MEMORY running tensorflow on GPU. failed to allocate 9. 24Gbytes) from device: CUDA_ ERROR_ OUT_ OF_ MEMORY [ [ 22.

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    The out of memory error also occurs in pytorch when you allocate memory more than 8gb or so. With this you can limit the amount of GPU memory allocated by the program, in this case to 90 percent of the available GPU memory. Maybe this is sufficient to solve your problem of the network trying to allocate more memory. There are some options: 1- reduce your batch size. 2- use memory growing: config = tf. ConfigProto( ) config. allow_ growth = True session = tf. Session( config= config,. ) 3- don' t allocate whole of your GPU. Most of the models are working for me, but SyntaxNet fails with a CUDA out of memory error even though the card has 8GB. failed to allocate 3. 25Gbytes) from device: CUDA_ ERROR_ OUT_ OF_ MEMORY. I tensorflow/ core/ common_ runtime/ gpu/ gpu_ device. cc: 975] Creating TensorFlow device ( / gpu: 0) - > ( device: 0, name: TITAN X ( Pascal), pci bus id: 0000: 01: 00. 0) E tensorflow/ stream_ executor/ cuda/ cuda_ driver.

    cc: 1002] failed to allocate 11. 90Gbytes) from device:. [ 1] com/ questions/ / cuda- error- out- of- memory- in- tensorflow. I just tried running the same code in an environment with the cuda libraries and tensorflow with GPU support installed. Session( ) I tensorflow/ core/ common_ runtime/ gpu/ gpu_ init. cc: 102] Found device 0 with properties: name: Tesla K80 major: 3 minor: 7. The original MemoryError is probably caused by trying to allocate more memory than the machine has available. Add stack trace or printf near here and see what is causing the GPU construction to fail. I always get the following error using different network' s size ( even nmt_ small) : I tensorflow/ core/ common_ runtime/ gpu/ gpu_ device. cc: 885] Found device 0 with properties: name: Graphics Device major: 6. name: Graphics Device, pci bus id: 0000: 03: 00. cc: 1002] failed to allocate. I tensorflow/ stream_ executor/ dso_ loader.

    cc: 111] successfully opened CUDA library libcublas. By default, tensorflow try to allocate a fraction per_ process_ gpu_ memory_ fraction of the GPU memory to his. This can fail and raise the CUDA_ OUT_ OF_ MEMORY warnings. I do not know what is the fallback in this case ( either using CPU ops or a allow_ growth= True ). Keras Tensorflow error CUDA_ ERROR_ INVALID_ DEVICE two session same GPU.