在Ubuntu上成功为GTX 1080 ti安装了Cuda和cudnn，在jupyter笔记本中运行一个简单的TF程序，在运行tensorflow-gpu==1.0与tensorflow==1.0的conda环境中速度没有增加。
当我运行 nvidia-smi 时：
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.66 Driver Version: 375.66 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 108... Off | 0000:01:00.0 On | N/A |
| 24% 45C P0 62W / 250W | 537MiB / 11171MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1101 G /usr/lib/xorg/Xorg 310MiB |
| 0 1877 G compiz 219MiB |
| 0 3184 G /usr/lib/firefox/firefox 5MiB |
+-----------------------------------------------------------------------------+
我尝试将“with tf.device("/gpu:0"):”放在矩阵乘法前面，但它只是给了我一个错误：
“InvalidArgumentError（请参阅上面的回溯）：无法将设备分配给节点“MatMul”：无法满足显式设备规范“/device:GPU:0”，因为在此过程中没有注册与该规范匹配的设备；可用设备：/job ：本地主机/副本：0/任务：0/cpu：0
[[节点：MatMul = MatMul[T=DT_FLOAT，transpose_a=false，transpose_b=false，_device="/device:GPU:0"]（重塑，softmax/变量/读取）]]"
我知道 cudnn 已正确安装，因为我在终端中运行它时收到此消息。
import tensorflow as tf
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
我想这一定是Jupiter笔记本的问题，有兼容性问题吗？当我运行 TF 会话时，我得到以下输出：
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.
Device mapping: no known devices.
"""
