概述

这个问题与SSE AVX等警告无关.我已经将输出包括在内为完整性.问题是一些cuda libs的失败,我认为,最后,机器有一个NVIDA 1070卡,并且有一个Cuda libs,在这个过程的早期使用但是最后还缺少什么？
我pip安装了TensorFlow 1.0版
我还单独下载了repo以获得最新的教程.
本教程专门用于获取所有Tensorboard功能的实例.
尝试从repo中的tensorFlow教程运行Minst_with_summaries.py(我将文件从repo复制到工作目录中),我正在使用Anaconda和Python 3.6我得到以下内容：

(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:   Y 
I 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

在我看来,TensorFlow的安装可能会遗漏一些东西看到上面的几行？
怎么修？
另请参阅GitHub上的此问题：https://github.com/tensorflow/tensorflow/issues/7975

答案发布在GitHub上,似乎有一个安装错误可以修复：

adding /usr/local/cuda/extras/CUPTI/lib64 to your LD_LIBRARY_PATH

如果@mrry重新打开以便其他人可以看到正确的解决方案,将会有所帮助.

您可以尝试git-hub问题建议的apt-get安装,但这不适合我：这样做：

答案发布在GitHub上,似乎有一个安装错误可以修复：

adding /usr/local/cuda/extras/CUPTI/lib64 to your LD_LIBRARY_PATH

你可以通过编辑.bash配置文件来做到这一点

总结

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