这个问题与SSE AVX等警告无关。我已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。 试图从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？ 另请在GitHub上引用此问题：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，那将会很有帮助。
答案 0 :(得分：2)
还在GitHub上引用此问题：https://github.com/tensorflow/tensorflow/issues/7975
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：
zed_board->Write1Led(pBase,1,1);
您可以通过编辑.bash个人资料
来实现答案 1 :(得分：1)
我在窗户上遇到这个。我通过将C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\extras\CUPTI\libx64添加到环境变量来解决。