This question has nothing to do with SSE AVX warnings, etc. I have included output for completeness. The problem is that in some versions of cuda libs, I think, in the end, the machine has an NVIDA 1070 card and has the Cuda libraries that are used earlier in this process, but something is missing at the end? I pip installed the TensorFlow 1.0 release. I also downloaded the repo separately to get the latest lessons. This tutorial is specifically for getting copies of all the features of Tensorboard. Trying to run Minst_with_summaries.py from tenorFlow tutorials in a repo (I copied the file from the repo to the working directory), and I use Anaconda and Python 3.6, I get the following:
(py36) tom@tomServal :~/Documents/LearningRepos/Working$ python Minst_with_summaries.py I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally Extracting /tmp/tensorflow/mnist/input_data/train-images-idx3-ubyte.gz Extracting /tmp/tensorflow/mnist/input_data/train-labels-idx1-ubyte.gz Extracting /tmp/tensorflow/mnist/input_data/t10k-images-idx3-ubyte.gz Extracting /tmp/tensorflow/mnist/input_data/t10k-labels-idx1-ubyte.gz W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations. W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations. I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: name: GeForce GTX 1070 major: 6 minor: 1 memoryClockRate (GHz) 1.645 pciBusID 0000:01:00.0 Total memory: 7.92GiB Free memory: 7.48GiB I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0: YI tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1070, pci bus id: 0000:01:00.0) Accuracy at step 0: 0.1213 Accuracy at step 10: 0.6962 Accuracy at step 20: 0.8054 Accuracy at step 30: 0.8447 Accuracy at step 40: 0.8718 Accuracy at step 50: 0.8779 Accuracy at step 60: 0.8846 Accuracy at step 70: 0.8783 Accuracy at step 80: 0.8853 Accuracy at step 90: 0.8989 I tensorflow/stream_executor/dso_loader.cc:126] Couldn't open CUDA library libcupti.so.8.0. LD_LIBRARY_PATH: :/usr/local/cuda/lib64 F tensorflow/core/platform/default/gpu/cupti_wrapper.cc:59] Check failed: ::tensorflow::Status::OK() == (::tensorflow::Env::Default()->GetSymbolFromLibrary( GetDsoHandle(), kName, &f)) (OK vs. Not found: /home/tom/anaconda3/envs/py36/lib/python3.6/site-packages/tensorflow/python/_pywrap_tensorflow.so: undefined symbol: cuptiActivityRegisterCallbacks)could not find cuptiActivityRegisterCallbacksin libcupti DSO Aborted
It seems to me that the TensorFlow installation may be missing. See the last few lines above? How to fix? Also address this issue on GitHub: https://github.com/tensorflow/tensorflow/issues/7975
The answer was posted on GitHub, and there seems to be an installation error that can be fixed:
adding /usr/local/cuda/extras/CUPTI/lib64 to your LD_LIBRARY_PATH
It would be helpful if @mrry opens again so that others can see the correct resolution.
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