Hi,
I have a dual boot PC. The Windows 10 OS is on an SSD. The Ubuntu is on an HDD. I installed CUDA 9.0 and followed the instructions to install cuDNN and did not get an error message I copied the 3 cuDNN files into their indicated directories. I see CUDA in the Control Panel but I don’t see cuDNN. How do I know if it’s installed properly? The docs indicate that cuDNN increases the speed by 100%.
When I train a model in Ubuntu, it runs at twice the speed as it does in Windows. Windows has the SSD so I thought that would be faster.
I’m running Python 3.6, Keras 2.2, tensorflow 1.9, tensorflow-gpu 1.9. Below is the screen output when the script runs.
Thanks,
Doug
2018-08-15 11:39:05.011390: I T:\src\github\tensorflow\tensorflow\core\platform\cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-08-15 11:39:05.468282: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1392] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.7335
pciBusID: 0000:01:00.0
totalMemory: 8.00GiB freeMemory: 6.59GiB
2018-08-15 11:39:05.468924: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1471] Adding visible gpu devices: 0
2018-08-15 11:39:06.571279: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:952] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-08-15 11:39:06.571602: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:958] 0
2018-08-15 11:39:06.571814: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:971] 0: N
2018-08-15 11:39:06.572272: I T:\src\github\tensorflow\tensorflow\core\common_runtime\gpu\gpu_device.cc:1084] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6364 MB memory) → physical GPU (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
C:/Users/ML/Desktop/KovalCNN/koval_cnn_8_07.py:132: UserWarning: Update your Model
call to the Keras 2 API: Model(inputs=Tensor("in..., outputs=Tensor("de...)