tensorflow-gpu

问题 Tensorflow-gpu version - 1.4.0 CUDA version - 8.0 cuDNN - v6.0 output from nvidia-smi: +-----------------------------------------------------------------------------+ | NVIDIA-SMI 388.59 Driver Version: 388.59 | |-------------------------------+----------------------+----------------------+ | GPU Name TCC/WDDM | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+====================

问题 I just installed tensorflow for gpu and am using keras for my CNN. During training my GPU is only used about 5%, but 5 out of 6gb of the vram is being used during the training. Sometimes it glitches, prints 0.000000e+00 in the console and the gpu goes to 100% but then after a few seconds the training slows back down to 5%. My GPU is the Zotac gtx 1060 mini and I am using a Ryzen 5 1600x. Epoch 1/25 121/3860 [..............................] - ETA: 31:42 - loss: 3.0575 - acc: 0.0877 - val_loss:

问题 I just installed tensorflow for gpu and am using keras for my CNN. During training my GPU is only used about 5%, but 5 out of 6gb of the vram is being used during the training. Sometimes it glitches, prints 0.000000e+00 in the console and the gpu goes to 100% but then after a few seconds the training slows back down to 5%. My GPU is the Zotac gtx 1060 mini and I am using a Ryzen 5 1600x. Epoch 1/25 121/3860 [..............................] - ETA: 31:42 - loss: 3.0575 - acc: 0.0877 - val_loss:

问题 I just installed tensorflow for gpu and am using keras for my CNN. During training my GPU is only used about 5%, but 5 out of 6gb of the vram is being used during the training. Sometimes it glitches, prints 0.000000e+00 in the console and the gpu goes to 100% but then after a few seconds the training slows back down to 5%. My GPU is the Zotac gtx 1060 mini and I am using a Ryzen 5 1600x. Epoch 1/25 121/3860 [..............................] - ETA: 31:42 - loss: 3.0575 - acc: 0.0877 - val_loss:

问题 I used tensorflow to train CNN with Nvidia Geforce 1060 (6G memory), but I got a OOM exception. The training process was fine on first two epochs, but got the OOM exception on the third epoch. ============================ 2017-10-27 11:47:30.219130: W tensorflow/core/common_runtime/bfc_allocator.cc:277] **********************************************************************************************xxxxxx 2017-10-27 11:47:30.265389: W tensorflow/core/framework/op_kernel.cc:1192] Resource

问题 I used tensorflow to train CNN with Nvidia Geforce 1060 (6G memory), but I got a OOM exception. The training process was fine on first two epochs, but got the OOM exception on the third epoch. ============================ 2017-10-27 11:47:30.219130: W tensorflow/core/common_runtime/bfc_allocator.cc:277] **********************************************************************************************xxxxxx 2017-10-27 11:47:30.265389: W tensorflow/core/framework/op_kernel.cc:1192] Resource

问题 Is there way to check I installed GPU version of Tensorflow ? !nvidia-smi Mon Dec 18 23:58:01 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 384.90 Driver Version: 384.90 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+====

问题 Is there way to check I installed GPU version of Tensorflow ? !nvidia-smi Mon Dec 18 23:58:01 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 384.90 Driver Version: 384.90 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+====

问题 Is there way to check I installed GPU version of Tensorflow ? !nvidia-smi Mon Dec 18 23:58:01 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 384.90 Driver Version: 384.90 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+====

问题 I am trying to index into a tensor to get a slice or single element from 1d tensors. I find that there is significant performance difference when using the numpy way of indexing [:] and slice vs tf.gather (almost 30-40% ). Also I observe that tf.gather has significant overhead when used on scalars (looping over unstacked tensor) as opposed to tensor . Is this a known issue ? example code (inefficient) : for node_idxs in graph.nodes(): node_indice_list = tf.unstack(node_idxs) result = [] for